The social network analysis (SNA), branch of complex systems can be used in the construction of multiagent
systems. This paper proposes a study of how social network analysis can assist in modeling multiagent
systems, while addressing similarities and differences between the two theories. We built a prototype
of multi-agent systems for resolution of tasks through the formation of teams of agents that are formed on
the basis of the social network established between agents. Agents make use of performance indicators to
assess when should change their social network to maximize the participation in teams.
The Mathematics of Social Network Analysis: Metrics for Academic Social NetworksEditor IJCATR
Social network analysis plays an important role in analyzing social relations and patterns of interaction among actors in a
social network. Such networks can be casual, like those on social media sites, or formal, like academic social networks. Each of these
networks is characterised by underlying data which defines various features of the network. Keeping in view the size and diversity of
these networks it may not be possible to dissect entire network with conventional means. Social network visualization can be used to
graphically represent these networks in a concise and easy to understand manner. Social network visualization tools rely heavily on
quantitative features to numerically define various attributes of the network. These features also referred to as social network metrics
used everyday mathematics as their foundations. In this paper we provide an overview of various social network analysis metrics that
are commonly used to analyse social networks. Explanation of these metrics and their relevance for academic social networks is also
outlined
a modified weight balanced algorithm for influential users community detectio...INFOGAIN PUBLICATION
In the modern era online users are increasing day by day. Different users are using various social networks in different forms. The behavior and attitude of the users of social networking sites varies U2U (User to User). In online social networking users join many groups and communities as per interests and according to the groups’/Communities’ influential user. This paper consist of 7 sections , first section emphasis on introduction to the community evelotion and community. Second section signify movement between communities ,third section involve related work about the research.. Fourth section includes Problem Definition and fifth section involve Methodology (Proposed Algorithm Process ,Get Community Matrix, Community detetcion).Sixth section involve Implementation. Furthermore implementation include Datasets ,Quantitative performance, Graphical Results, Enhancement in the existing work..Last section include Conclusion and then references. In this paper,we are implementing and proposing the community detection in social media .In the proposed we have deployed a Longest Chain Subsequence metric for finding the number of connections to the kernel community.
Preso on social network analysis for rtp analytics unconferenceBruce Conner
Selected highlights of Coursera Social Networking course, taught by Prof. Lada Adamic of the Univ. of Michigan. Presented at the annual Annual RTP Analytics Unconference, May 4, 2013
The science of networks is becoming an increasingly important and intriguing area of study that reveals many a patterns and relationships often hidden. This presentation is about the use of SNA to study the network of the Digital Library Community
The Mathematics of Social Network Analysis: Metrics for Academic Social NetworksEditor IJCATR
Social network analysis plays an important role in analyzing social relations and patterns of interaction among actors in a
social network. Such networks can be casual, like those on social media sites, or formal, like academic social networks. Each of these
networks is characterised by underlying data which defines various features of the network. Keeping in view the size and diversity of
these networks it may not be possible to dissect entire network with conventional means. Social network visualization can be used to
graphically represent these networks in a concise and easy to understand manner. Social network visualization tools rely heavily on
quantitative features to numerically define various attributes of the network. These features also referred to as social network metrics
used everyday mathematics as their foundations. In this paper we provide an overview of various social network analysis metrics that
are commonly used to analyse social networks. Explanation of these metrics and their relevance for academic social networks is also
outlined
a modified weight balanced algorithm for influential users community detectio...INFOGAIN PUBLICATION
In the modern era online users are increasing day by day. Different users are using various social networks in different forms. The behavior and attitude of the users of social networking sites varies U2U (User to User). In online social networking users join many groups and communities as per interests and according to the groups’/Communities’ influential user. This paper consist of 7 sections , first section emphasis on introduction to the community evelotion and community. Second section signify movement between communities ,third section involve related work about the research.. Fourth section includes Problem Definition and fifth section involve Methodology (Proposed Algorithm Process ,Get Community Matrix, Community detetcion).Sixth section involve Implementation. Furthermore implementation include Datasets ,Quantitative performance, Graphical Results, Enhancement in the existing work..Last section include Conclusion and then references. In this paper,we are implementing and proposing the community detection in social media .In the proposed we have deployed a Longest Chain Subsequence metric for finding the number of connections to the kernel community.
Preso on social network analysis for rtp analytics unconferenceBruce Conner
Selected highlights of Coursera Social Networking course, taught by Prof. Lada Adamic of the Univ. of Michigan. Presented at the annual Annual RTP Analytics Unconference, May 4, 2013
The science of networks is becoming an increasingly important and intriguing area of study that reveals many a patterns and relationships often hidden. This presentation is about the use of SNA to study the network of the Digital Library Community
The evolution of the Web and its applications has undergone in the last few years a mutation towards
technologies that include the social dimension as a first class entity in which the users, their interactions
and the emerging social networks are the center of this evolution. The web is growing and evolving the
intelligibility of its resources and data, the connectivity of its parts and its autonomy as a whole system. The
social dimension of the current and future web is being at the roots of its dynamics and evolution. It is thus,
fundamental to propose new underlying infrastructure to the web and applications on the web, to make
more explicit this social dimension and facilitate its exploitation. The work presented is this paper
contributes to this initiative by proposing a multi-agent modeling based on the system coupling to its
environment through its social dimension. Applied to a collaborative tagging system, the exploitation of the
social dimension of tagging allows an intelligent and better sharing of resources and enhancing social
learning between users.
Multi agent paradigm for cognitive parameter based feature similarity for soc...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Mining and Analyzing Academic Social NetworksEditor IJCATR
Academics establish relationships by way of various interactions like jointly authoring a research paper or report, jointly
supervising a thesis, working jointly on a project, etc. Some of these relationships are ubiquitous whereas other are hard to keep track
of. Of all types of possible academic and research collaborations, co-authorship is best documented. In this paper we analyze the coauthorship
based academic social networks of computer science engineering departments of Indian Institutes of Technology (IITs) as
evidenced from their research publications produced during 2011 and 2015. We use social network analysis metrics to study the
collaboration networks in four leading IITs. From experimental results it can be concluded that IIT Delhi and IIT Kharagpur have a
close knit collaboration network whereas the collaboration network of IIT Kanpur and IIT Madras is fragmented. However, the
collaboration networks of all the four IITs exhibit similar network properties as expected from any other collaboration network
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
TOPIC networking portfolio
ACADEMIC LEVEL Undergrad. (yrs 3-4)
DISCIPLINE Business Studies
DOCUMENT TYPE Term paper
SPACING DOUBLE
CITATION STYLE Harvard
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Hybrid sentiment and network analysis of social opinion polarization icoictAndry Alamsyah
The rapid growth of social media and user generated contents (UGC) has provided a rich source of potentially relevant data. The problems arise on how to summarize those data to understand and transforming it into information. Twitter as one of the most popular social networking and micro-blogging service can be analyzed in terms of content produced with sentiment analysis. On the other hand, some types of networks can also be constructed to analyze the social network structure and network properties. This research intended to combine those content and structural approaches into hybrid approach for identifies social opinion polarization, this is in the form of conversation network. Sentiment analysis used to determine public sentiment, and social network analysis used to analyze the structure of the network, detecting communities and influential actors in the network. Using this hybrid approach, we have comprehensive understanding about social opinion polarization. As case study, we present real social opinion polarization about reclamation issue in Indonesia.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
The evolution of the Web and its applications has undergone in the last few years a mutation towards
technologies that include the social dimension as a first class entity in which the users, their interactions
and the emerging social networks are the center of this evolution. The web is growing and evolving the
intelligibility of its resources and data, the connectivity of its parts and its autonomy as a whole system. The
social dimension of the current and future web is being at the roots of its dynamics and evolution. It is thus,
fundamental to propose new underlying infrastructure to the web and applications on the web, to make
more explicit this social dimension and facilitate its exploitation. The work presented is this paper
contributes to this initiative by proposing a multi-agent modeling based on the system coupling to its
environment through its social dimension. Applied to a collaborative tagging system, the exploitation of the
social dimension of tagging allows an intelligent and better sharing of resources and enhancing social
learning between users.
Multi agent paradigm for cognitive parameter based feature similarity for soc...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Current trends of opinion mining and sentiment analysis in social networkseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Mining and Analyzing Academic Social NetworksEditor IJCATR
Academics establish relationships by way of various interactions like jointly authoring a research paper or report, jointly
supervising a thesis, working jointly on a project, etc. Some of these relationships are ubiquitous whereas other are hard to keep track
of. Of all types of possible academic and research collaborations, co-authorship is best documented. In this paper we analyze the coauthorship
based academic social networks of computer science engineering departments of Indian Institutes of Technology (IITs) as
evidenced from their research publications produced during 2011 and 2015. We use social network analysis metrics to study the
collaboration networks in four leading IITs. From experimental results it can be concluded that IIT Delhi and IIT Kharagpur have a
close knit collaboration network whereas the collaboration network of IIT Kanpur and IIT Madras is fragmented. However, the
collaboration networks of all the four IITs exhibit similar network properties as expected from any other collaboration network
Social Network Analysis power point presentation Ratnesh Shah
Basics of social network analysis,Application and also explain interesting study done by facebook , twitter, youtube and many more social media network ,slide contains some of interesting study to get knowledge about online social network analysis.
TOPIC networking portfolio
ACADEMIC LEVEL Undergrad. (yrs 3-4)
DISCIPLINE Business Studies
DOCUMENT TYPE Term paper
SPACING DOUBLE
CITATION STYLE Harvard
Subscriber Churn Prediction Model using Social Network Analysis In Telecommun...BAINIDA
Subscriber Churn Prediction Model using Social Network Analysis In Telecommunication Industry โดย เชษฐพงศ์ ปัญญาชนกุล อาจารย์ ดร. อานนท์ ศักดิ์วรวิชญ์
ในงาน THE FIRST NIDA BUSINESS ANALYTICS AND DATA SCIENCES CONTEST/CONFERENCE จัดโดย คณะสถิติประยุกต์และ DATA SCIENCES THAILAND
Hybrid sentiment and network analysis of social opinion polarization icoictAndry Alamsyah
The rapid growth of social media and user generated contents (UGC) has provided a rich source of potentially relevant data. The problems arise on how to summarize those data to understand and transforming it into information. Twitter as one of the most popular social networking and micro-blogging service can be analyzed in terms of content produced with sentiment analysis. On the other hand, some types of networks can also be constructed to analyze the social network structure and network properties. This research intended to combine those content and structural approaches into hybrid approach for identifies social opinion polarization, this is in the form of conversation network. Sentiment analysis used to determine public sentiment, and social network analysis used to analyze the structure of the network, detecting communities and influential actors in the network. Using this hybrid approach, we have comprehensive understanding about social opinion polarization. As case study, we present real social opinion polarization about reclamation issue in Indonesia.
Social network analysis [SNA] is the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities. SNA provides both a visual and a mathematical analysis of human relationships.
Ciphering algorithms play a main role in information security systems. Therefore in this paper we are
considering the important performance of these algorithms like CPU time consumption, memory usage and
battery usage. This research tries to demonstrate a fair comparison between the most common algorithms
and with a novel method called Secured Watermark System (SWS) in data encryption field according to
CPU time, packet size and power consumption. It provides a comparison the most known algorithms used
in encryption: AES (Rijndael), DES, Blowfish, and Secured Watermark System (SWS).
For comparing these algorithms with each other variations of data block sizes, and a variation of
encryption-decryption speeds where used in this research.
In addition a comparison with different platforms such as Windows 8, Windows XP and Linux has been
conducted. Finally the results of the experimentation demonstrate the performance and efficiency of the
compared encryption algorithms with different parameters.
With the increase in Internet users the number of malicious users are also growing day-by-day posing a
serious problem in distinguishing between normal and abnormal behavior of users in the network. This
has led to the research area of intrusion detection which essentially analyzes the network traffic and tries
to determine normal and abnormal patterns of behavior.In this paper, we have analyzed the standard
NSL-KDD intrusion dataset using some neural network based techniques for predicting possible
intrusions. Four most effective classification methods, namely, Radial Basis Function Network, Self-
Organizing Map, Sequential Minimal Optimization, and Projective Adaptive Resonance Theory have been
applied. In order to enhance the performance of the classifiers, three entropy based feature selection
methods have been applied as preprocessing of data. Performances of different combinations of classifiers
and attribute reduction methods have also been compared.
The process of language learning involves the mastery of countless tasks: making the constituent sounds of
the language being learned, learning the grammatical patterns, and acquiring the requisite vocabulary for
reception and production.
While a plethora of computational tools exist to facilitate the first and second of these tasks, a number of
challenges arise with respect to enabling the third. This paper describes a tool that has been designed to
support language learners with the challenge of understanding the use of ‘closed-class’ lexical items.
The process of learning the Arabic for ‘office’ is (mktb) is relatively simple and should be possible by
means of simple repetition of the word. However, it is much more difficult to learn and correctly use the
Arabic equivalent of the word ‘on’. The current paper describes a mechanism for the delivery of diagnostic
information regarding specific lexical examples, with the aim of clearly demonstrating why a particular
translation of a given closed-class item may be appropriate in certain situations but not others, thereby
helping learners to understand and use the term correctly.
In the absence of a dedicated designer on a project, teams are left with no option but to take a leap of faith where it comes to the quality of usability of a product or feature. As a result although you may deliver great quality code and a feature set net value to the end user and thereby your customer is in question.
The focus of this workshop is to equip non design roles with tools designers use for testing quality of designs.
Study in Risk Management of Steel Plant ProjectsDilpreet Singh
The scope of study includes qualitative and quantitative analysis which helps in consolidating decision making under uncertainty. The emphasis has been given in finding the most critical risks which were quantified with the help of @RISK software and as per the tornedo chart the results are interpreted. This helps in identifying major project as a management input and which in turn identifies mitigation solutions to resolve riskoriented issues in steel plant projects.
Multi agent paradigm for cognitive parameter based feature similarity for soc...eSAT Journals
Abstract The ABM methodology is a favorable approach to model and analyze complex social phenomena that may involve non-linear feedback loops. It has been applied successfully to model a number of social phenomena involving different social processes and organizational structures. Availability of cheap computing power and rich software resources has made ABM a widely used and hence more popular methodology. A modeler using ABM however have be careful about choosing the right amount of detail (less and more both can be problematic) and validating (internal and external) the model. Interpreting and analyzing results is also an involved task. In this paper, we have demonstrated how ABM can be applied to model and analyze the voting preference formation and resultant voting decisions of individuals in a population. The model assumes a two party system. We designed three versions of the simulation and observed the results for a large number of runs with different parameter variations. The results obtained present interesting picture and resultant inferences.
jashkadshkjsa ksa jdhs k sdajhdsa ks kdsjah dsk sadk asjdh aks asdjk sadkads k kadsjkjsad sadk sdakj dsa jALJALADSJLSAK ASKLASSADJ LAS DSAKSAD LSDK L SADL DASLK ASDJSDAKLJSADLK L SADL LSDAKDASJ L SAD
Fuzzy AndANN Based Mining Approach Testing For Social Network AnalysisIJERA Editor
Fast and Appropriate Social Network Analysis (SNA) tools ,techniques, are required to collect and classify
opinion scores on social networksites , as a grouping on wrong opinion may create problems for a society or
country . Social Network Analysis (SNA) is popular means for researcher as the number of users and groups
increasing day by day on that social sites , and a large group may influence other.In this paper, we
recommendhybrid model of opinion recommendation systems, for single user and for collective community
respectively, formed on social liking and influence network theory. By collecting thedata of user social networks
and preferenceslike, we designed aimproved hybrid prototype to imitate the social influence by like and sharing
the information among groups.The significance of this paper to analyze the suitability of ANN and Fuzzy sets
method in a hybrid manner for social web sites classifications, First, we intend to use Artificial Neural
Network(ANN)techniques in social media data classification by using some contemporary methods different
than the conventional methods of statistics and data analysis, in next we want to propagate the fuzzy approach
as a way to overcome the uncertainity that is always present in social media analysis . We give a brief overview
of the main ideas and recent results of social networks analysis , and we point to relationships between the two
social network analysis and classification approaches .This researchsuggests a hybrid classification model build
on fuzzy and artificial neural network (HFANN). Information Gain and three popular social sites are used to
collect information depicting features that are then used to train and test the proposed methods . This neoteric
approach combines the advantages of ANN and Fuzzy sets in classification accuracy with utilizing social data
and knowledge base available in the hate lexicons.
Running head DEPRESSION PREDICTION DRAFT1DEPRESSION PREDICTI.docxhealdkathaleen
Running head: DEPRESSION PREDICTION DRAFT 1
DEPRESSION PREDICTION DRAFT 3
Data Science and Big Data Analytics
Option 1- Depression Prediction in Digital World
Introduction
This paper explores human behavior to detect their depression levels in the digital world by analyzing their behavioral patterns. The digital world has reduced the interaction time between human encounters and made it an easily available interaction via social media. More than 80% of the human emotions are shared in social media since the physical human interaction has fallen drastically that everyone prefers social media rather than healthy human interactions. Social media is serving as a double edge sword when human emotions are considered since it has the ability to destruct happiness and also the ability to lift a person from depression and related issues. This leads to focus and emphasize on ethical usage of social media since 90% of the user lot are taking it for granted maintaining too many profiles on different pseudo names. As the platform has grown bigger many types of research have been taking place for understanding the current psychological situations of the world population.
Literature Review
The emotional analysis has gone into a research-level topic many researches have incorporated linguistic processing and content interpretation to understand the behaviors of an individual. This paper studies the human behaviors and their emotions by considering the sources like customer reviews on internet articles, social media postings, stock fluctuations, product reviews, newspaper reactions, etc. This paper uses K-mean clustering and Neural networks to efficiently understand the human behaviors which give the true values for false rejections and true rejections. Back Propagation Neural Networks helps in gathering the related patterns that define a particular emotion and thereby the subject emotions get narrowed down and if the subject is a close study material then it would be easy to diagnose the subject with the required solution
Deep Learning Neural Network in depression analysis
The recent advances in Data Science and analysis techniques lead to many groundbreaking researches and depression prediction is one such research where the technology is giving enough insights in the medical field. Digital world is the home ground for many depression related issues since the major population is spending too much of time on the social media and digital gadgets that they prefer sharing their dark sides and emotions in the form of social media postings and writings in wide range of platforms like blogs and community groups.
Link mining
Link mining is one of the prominent technologies in the data mining where the data instances are linked through wide range of data models that can categorize the content into various groups based on the prime focus and subject of the sentences. Unstructured data is not considered in this aspect since the Link min ...
Social network analysis is the application of network theory to the modeling and analysis of social systems. Free MP3 Podcast reveals how to use social media to sell more stuff. Find out more at www.sociamigo.com/mp3
The possibility of modeling and abstracting interaction has been the key driver in social network-based
research as it facilitates, among other things, the generation of recommendations which is vital for most
businesses. Being ubiquitous, learning activities also facilitate the formation of these networks. Thus, to
gain insights into the evolution of activities and interactions that occur during learning events, it is
important to understand these networks and their respective models. In this article, we present a survey of
the representative methods employed in modeling various interactions observed in learner-centered
networks. Finally, we comparatively analyze the respective models and identify which models perform
better in respective cases.
Energy Awareness and the Role of “Critical Mass” In Smart Citiesirjes
A Smart City could be depicted as a place, logical and physical, in which a crowd of heterogeneous
entities is related in time and space through different types of interactions. Any type of entity, whether it is a
device or a person, clustered in communities, becomes a source of context-based data.
Energy awareness is able to drive the process of bringing our society to limit energy waste and to optimize
usage of available resources, causing a strong environmental and social impact. Then, following social network
analysis methodologies related to the dynamics of complex systems, it is possible to find out, emergent and
sometimes hidden new habits of electricity usage. Through an initial Critical Mass, involving a multitude of
consumers, each related to more contexts, we evaluate the triggering and spreading of a collective attitude. To
this aim, in this paper, we propose a novel analytical model defining a new concept of critical mass, which
includes centrality measures both in a single layer and in a multilayer social network.
MULTI-AGENT PARADIGM FOR LEADERSHIP SELECTION: A REVIEWEditor IJMTER
A Multi-agent System (MAS) is comprised of multiple interacting intelligent agents. Agents
in the MAS could all be of same type (homogeneous) or different (heterogeneous). MAS are used to
solve problems which are either difficult for an individual agent to solve or when the problem is
inherently comprised of multiple actors interacting together. However, the nature of MAS design
coordination among agents in MAS is always a core issue. Coordination and cooperation allows the
agents to manage their inter dependencies and the type and nature of interactions. Coordination and
cooperation differ in degree of inter-agent knowledge and beliefs. Agent coordination is usually
achieved in the backdrop of a communication system between agents. This paper is a based on the
review of various work on selection of multi-agent for various task domain.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
1. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
DOI : 10.5121/ijaia.2015.6305 51
SNA-BASED REASONING FOR MULTI-
AGENT TEAM COMPOSITION
André Filipe de Moraes Batista1
and Maria das Graças Bruno Marietto2
1
Metropolitan-United Faculty (FMU), São Paulo, Brazil.
2
Federal University of ABC (UFABC), São Paulo, Brazil.
ABSTRACT
The social network analysis (SNA), branch of complex systems can be used in the construction of multi-
agent systems. This paper proposes a study of how social network analysis can assist in modeling multi-
agent systems, while addressing similarities and differences between the two theories. We built a prototype
of multi-agent systems for resolution of tasks through the formation of teams of agents that are formed on
the basis of the social network established between agents. Agents make use of performance indicators to
assess when should change their social network to maximize the participation in teams.
KEYWORDS
Multi-Agent Systems, Social Network Analysis, Complex Systems, Reasoning, Task Resolvers
1. Introduction
One of the research topics in the Multi-Agent Systems (MAS) area is the definition of models that
represents social structures, such as organizations and alliances, in order to analyze more
objectively the emergent behavior of open systems. Individuals who are related to each other by
different types of relationships, such as dependencies on goals, conflicts over resources, similar
beliefs and so on compose organizations and alliances.
Since the 80s several studies were carried out using the theory of MAS to represent models of
social phenomena. Axelrod in [1] argues that the object of these studies is the use of the theory of
MAS to break up simplistic definitions on a particular subject, due to the need of the model be
mathematically tractable. Thus, social phenomena models normally used concepts such as
homogeneity, ignoring interactions.
With the use of agent-based models, this research area has been benefited by being able to
represent most of the behaviors of autonomous agents and the interactions between them. Agent-
based models are in the most cases suitable for decentralized processing and decision making,
particularly when individual interactions lead to the emergence of collective patterns, as well as
complex systems. Therefore, there is a close relationship between MAS and Complex Systems
Theory, and this relationship can be represented in the Social Network context.
An important question is how to represent these relationships, characterized by a high degree of
dynamism. Dealing with social issues, MAS area was inspired mainly by the economic
organizational theory and legal theory. Although, there is a lack of attention in the area of
research that describes the relationships between individuals within human organizations and
their dynamics. To this area, we give the name of Social Network Analysis (SNA). The social
network analysis has emerged as a key technique in modern sociology, anthropology, social
2. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
52
psychology, communication studies, information science, organizational studies, and economics,
among others.
This work aims to integrate the techniques of multi-agent systems and social network analysis,
allowing an agent society make use of SNA metrics in the decision-making process considering
attributes such as the position in the network and the value and the importance of an agent in the
network. Since both techniques use the social element, the agent, the synergy between them will
allow easy use of models based on social network analysis, so that the multi-agent systems
become more reliable in the representation of social phenomena. For this, an application was
developed representing process of selecting teams for solving tasks in a multi-agent society, in
which agents have specific skills and their position in the social network will influence their
performance in the multi-agent society.
This work is structured as follows: in Section presents a review of the issues of multi-agent
systems and social network analysis area, while Section presents prospects of integration between
both techniques. Section presents and discuss about the prototype of multi-agent system for a
task-solving problem by agents teams making use of SNA technique. Section presents the final
considerations of this work.
2. Background
In this section, we present a review of the literature on multi-agent systems (MAS) and social
network analysis (SNA) research areas.
2.1. Multi-Agents Systems
Multi-agent systems consists in multiple agents that interact to each other in order to perform a
specific set of tasks. The metaphor of intelligence used by these systems is the intelligent
community in which social behavior is the basis for the system intelligence. From this, we can
distribute the agents in areas of specialization and with the interaction among them a complex
problem can be solved in a faster and more dynamically way[2].
The metaphor often used to structure the agents society is the human social groups, where expert
teams can solve problems cooperatively, and the complexity exceeds the individual capacities of
each of their members [3].
The autonomy of an agent allows it to take its own decisions to achieve its goals [4]. Thus, agents
can get in and get out of the society, change their rules, roles, inter-dependent relationships with
other agents, etc. This feature leads to a new generation of systems and distributed applications
intrinsically dynamic, open and complex.
The notion of agents and multi-agent systems have been adopted in the modeling of various
complex systems involving urban planning, biology, logistics and production, and many others [5,
6, 7, 8, 9, 10, 11, 12, 13]. Lynne and Nigel [14] propose a model based on agents for social
networks. At the same step that the proposed model is simplistic, it can represent a wide variety
of social networks. Teresa and Nina [15] implemented a Java tool that presents the dynamic
model of social behavior associated with the recruitment of terrorists based on prescriptive
models. They used concepts of MAS and SNA to represent this dynamic. Ronald et al. [16]
proposed a model based on agents to analysis the social influence in the travel activities decision
process. The agents have a travel schedule, and interact with each other in order to schedule social
activities, in particular trade based on the nature of the activity, who will participate, time and
3. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
53
location. The structure formed between the agents described as a complex social network is the
core of the decision process.
2.2. Social Network Analysis
Social Network Analysis (SNA) is a scientific research area derived from areas such as
Sociology, Social Psychology and Anthropology. This area studies the relational links
(a.k.arelational tie) between social actors. An actor in the SNA can be both individuals and
companies, analyzed as individual units, or collective social units such as, for example,
departments within an organization, public service agencies in a city, nation-states of the
continent, among others. The SNA fundamentally differs from other studies in that the emphasis
is not on the attributes (characteristics) of the actors, but the links between them.
Relations between actors pairs are made by relational ties or linkages. The most common types of
links are: individual assessment (eg, friendship or respect); the transaction and the transfer of
material resources (a purchase and sale transaction between two companies); the transfer of
nonmaterial resources (the exchange of electronic messages) or not; the association or affiliation
that occurs when actors participate in joint events (parties); interaction (sitting next to another
person); the movement and the physical and social connection; links between formal roles (boss-
subordinate authority loop in a company); biological relationships (father and son) and so on.
SNA is a method for enhancing the sharing of knowledge by analyzing the position and structure
between actors, i.e their relationships. According to [17], the network analysis process considers
basically two analytical perspectives that complement each other:
1. Egocentric - this type of analysis has attention in facing the particular node/actor (ego) and
other nodes/actors in the network with others ego nodes that it maintains relations.
Therefore, the number, the magnitude and the diversity of the direct or indirectly
connections established with the ego define the other network nodes;
2. Socio centric - in this type of analysis, information about the pattern of links between all
nodes/actors in the network is used broadly to identify reticulated subgroups with high
degree of internal cohesion and the nodes that have similar positions on the network.
The interpretation of the results obtained with the SNA metric can be made from three points of
view, namely:
1. Individual positions - from the viewpoint of the actors;
2. Whole network - from the viewpoint of the assembly of links forming the network;
3. clusters and components - from the viewpoint of the groups formed due to some kind of
relationship.
The social network analysis metrics provides mathematical mechanisms to analyze a given
society or group. Among the most significant metrics for understanding the role of an actor in the
network, we can cite:
• Centrality Degree: it indicates the prestige and power that the author has in that particular
community. In the SNA context, the power dimension is derived from the relational ties of
an actor. The more relationship an actor makes, the more power it has.
• Betweenness Degree: it shows how much an actor is between others actors on the network.
It helps to locate actors in positions of influence, those with the network information, etc.
An actor with a high degree of betweenness may be functionally operating as a broker in
the network;
4. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
54
• Closeness Centrality: shows the closeness of an actor to others on the network and thus its
access to resources circulating in the network, based on the evaluation of the shortest path
within the network.
Many of these metrics can be used in a multi-agent scenario, since it is composed of independent
individuals who interact with each other. However, there is still a gap on the theory and practice
on this issue, that is, how to implement and model a MAS with many different relationships
between agents in order to capture and analyze them.
3. Social Network Analysis and Multi-Agent Systems
One of the biggest questions about the social network analysis is“it is known that social networks
can be used to analyze human societies. But these can also be used for societies of agents?”. An
inconvenient and often questionable point, on social network analysis is that normally a
transcription of the human social structures in sociological meanings is made from a clear and
artificial manner, due to the complexities of individuals and their relationships. On the opposite
side the simplicity in social terms of multi-agents systems suggests that the analysis of social
networks can be applied to these, including may get even better results.
In the interconnection of two areas of study, one of the points of attention is the level of approach
to be carried out. A multi-agent system is built from the specification of the smallest entity: the
agent. The behavior and the structures are emerging consequences of their interactions. The same
is true for Social Networks; the interaction of the actors will result in the emergence of the
structure of the social network.
Many MAS consist of a complex network of autonomous and interdependent agents. In most of
these systems, agents must select a set of other agents with whom will interact, based on factors
such as limited resources, exchange of interests of knowledge, etc.
[18] Showed that the structure of existing artificial social network in multi-agent society, when
used to govern the actions of the agents, is extremely connected with the performance of the
multi-agent society.
Modeling scenarios in which agents are geared to social networks presents a set of challenges.
First, agents must make adaptations in the network by decisions made based on local system
information. This local information can give the agent a figure partially or completely wrong
about the current environment. In this case, it is said that an agent has a limited horizon of the
system. Since agents are organized as networks, and how various agents perform their local
decisions (adapting your social network), these simultaneous changes may neglect the benefits of
adaptation made by an agent.
Focusing on the social network adaptation strategy of an agent in a cooperative MAS (the agents
cooperate towards a single goal), the following questions should be considered when modeling
such agents, which are
• Local perception of overall performance: how an agent can estimate the collective
performance of the organization? These estimates may be unreliable, since they are each
based on partial views of the organization. A possible solution to this difficulty is to explore
the communication skills of the agents. With use of some communication protocols, an
agent can estimate whether its perception of overall performance is correct, or if it can use
the perception of neighboring agents to improve it;
5. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
55
• Triggers of adaptation: when an agent must decide to adapt its structure of local
connectivity? There are many possibilities of these triggers be fired. An agent, for example,
may decide to adapt its structure based on estimates of performances in relation to the
attendance of its goal;
• Network adaptation: how an agent decides which has to be removed connection and how it
selects a new agent to establish a new social connection. A good strategy advocated by [18]
is the adaptation based on reference where an agent asks for a reference about another agent
for one of your neighbors.
4. Social Network Analysis’ based Reasoning for Multi-Agent Team
Composition
Based on [18], the proposed prototype on this work is a simple but intuitive multi-agent
cooperative system. It is a multi-agent society for dynamic team formation of task resolvers.
This prototype presents a model of dynamic formation of teams, where teams of agents are
formed spontaneously and in the decentralized form, as soon as the decision of an agent in
adhering to a team is done by base in its local social network.
In the proposed model, a set of tasks are generated periodically and are globally advertised to the
multi-agent society. The role of agents in this society is forming teams to solve these tasks. The
participating agents of this society are involved for a social network. For an agent to be on a team,
it must possess a social connection (that is, an edge in the network) with at least one member of
the team.
Once this prototype focuses on the team formation process, when these teams are complete the
task is considered done. Thus, in this model the multi-agent society consists of N agents, A =
{a1,a2,...,aN}, where each agent can be considered as a single node on the social network. The
network is modeled as an adjacency matrix E, where each adjacency matrix elements eij= 1if there
is an edge between the agents ai and aj, or eij= 0 otherwise. The degree (number of connections) of
an agent ai is defined by
In multi-agent society, each agent has a single ability, σi∈ [1,σ], where σ is the number of different
types of abilities present in the society. During the process of forming teams, each agent can be in
one of three states: uncommitted, committed, active. An agent is in the state uncommitted if it is
available to join teams and therefore is not in any team. When an agent selects a task in which it
is able to perform, this is in the committed state. When this agent effectively is accepted for the
team, the same is in the active state.
Agents that are in the active state can no longer get out of the team until it is complete or a
timeout is reached. In this case, the team was not formed in the required time to resolve the task.
The tasks are advertised in multi-agent society in a fixed interval of time µ. Each task is a vector
Tk of size |k| with required skills. Each task is announced by a finite number of time γ, ensuring
that resources (i.e., agents) linked to a task become available if the team is not formed.
The requirement of a team be an induced connected subgraph of the social network means that for
any agent on the team, ai∈ Mk - where Mk is the k team of this society, there
6. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
56
Figure 1: Process of forming teams of agents to task resolution, making use of social network metrics.
should be at least some other agent aj∈ Mk,i≠j such that eij= 1. This implies that an agent in the
uncommitted state is only eligible to enter in the committed state in two situations: 1) team
startup, when the team is empty, and 2) joining a team, when at least one of the neighbors of the
agent is in committed state related to this task.
Unlike the model presented by cite [18], in this model all agents in the uncommitted state can try
to belong to a team when they receive a task in which its ability is being requested. At this point,
a Scheduler agent should coordinate the multi-agent society. It is up to this agent accepts or not
the agents making use of SNA heuristics. Figure 1 presents an indicative flow chart of the multi-
agent process, highlighting where the techniques and metrics from social network analysis are
used.
In the flow shown in Figure 1 interactions, the role of the manager agent is generating tasks (skill
set) and send them to the scheduler agent. The scheduler agent has the following set of functions:
receive tasks sent by the manager agent and transmit them to all task agents; receive the proposals
from task agents; select the agents to composition of the teams and check the conclusion
(satisfactory or not) of the tasks.
Task agents receive tasks and if they have the necessary skills, send an application to the
scheduler agent. Right now, they are awaiting a confirmation or a rejection message. If it is
rejected then returns to receive tasks. If accepted, goes to the team and so it is waiting for the
complete formation of the team.
Social strategies are implemented as follows:
1. Scheduler Agent: it knows the entire network of agents. When an agent task sends a
message stating the interest in participating in the team, the scheduler agent checks whether
the team is empty (in this case the agent is starting the team), or if the agent knows other
agents that make up the team. This strategy that how the structure of social networks can be
used for decision-making. Such approach can be used to solve problems of allocation in a
general way. These problems have a high degree of complexity, and this occurs for two
main reasons. First, find the optimal scheduling is a NP-hard problem. In addition, each
scheduling problem has particular details, involving changes in the search algorithm of the
7. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
57
solutions. Thus, it becomes necessary to use some type of heuristic to reduce the search
space. SNA metrics can be a valid heuristic in this situation;
2. Task Agent: each task agent has a local measure of performance. This measure will be the
gear adaptation of the agent’s social network strategy. This strategy is based on
performance and reference [18]. The trigger of this adaptation is the measure of local
performance Y (ai), which each agent ai has. This measure is the ratio between the number
of times the agent participated effectively in a team (it was accepted for a team) by the
number of attempts to participate. The measure of performance is considered valid when
the agent tried to enter a team at least v times. With each iteration, the agent ai chooses
adapt their social network if this has a measure of performance valid, and if the measure of
performance is below the average of the sum of the performance of all its neighbors. That
is, an agent chooses adapt its social network if:
(2)
If an agent decides to adapt its network based on the performance value, this adaptation is
given both by performance and by reference. The agent will remove the connection to its
immediate neighbor aj which has the smallest measurement of performance:
The agent asks for a reference to the neighbor with the highest performance. Similarly, this
agent will refer to its neighbor with the highest performance. Is al the agent to whom the
agent ai calls for reference, the agent will establish a new connection with ak, the neighbor
of greater performance of al:
Such strategy represents how the knowledge of the network can be used in a MAS. In this
case, the task agent is making use of the centralization degree of the SNA theory. This is a
measure of power, influence, and which in this case is a measure of performance. Thus, an
agent is able to adapt its social network site so that this attempt to get more success in being
a member of a team.
4.3. Communication Protocols
All SNA-based reasoning may be mapped using a set of communication protocols in order to
establish a standard mechanism for establishing team for solving tasks. This protocol consists of a
set of messages with ACL (Agent Communication Language) perfomatives that indicate what
actions are being performed on the social network of agents.
The process begins when the manager agent sends a propose message to the scheduler agent. The
scheduler agent sends this message to all task agents. If a particular task agent has one of the
skills required by the task, this sends an accept-proposal message to the scheduler agent.
After receiving all the proposals, the scheduler agents reasons using SNA metrics and sends
accept-proposal messages for the agents that were accepted, and reject-proposal
8. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
58
Figure 2: Prototype developed in JADE platform: social network dynamically built by agents, monitoring of
messages exchanged and log of actions.
for those that were rejected. Agents who received the acceptance, are now committed to a team
and will no longer respond to the proposals made by the scheduler. The scheduler agent checks
after a time γ if the team was complete. If it is not complete, this sends a failure message to staff
agents of this team, stating the fact. When a team is complete, the scheduler agent considers that
the task was performed with success, sending a message confirm, releasing the agents in order to
be a member of new teams.
The mechanism of adaptation by performance and reference was implemented as follows: when
an agent reaches a valid value of performance (for example, after 10 attempts of formation of
teams), it sends a query-if message questioning to its neighbors about their values of performance.
The neighbors receive this message and respond to the agent with the value of its respective
performances, with a inform-if message. After receiving all the performances, the agent checks if
is advantageous to perform the change. If yes, this sends a proxy message to its neighbor with
higher performance by asking another agent by reference. After receiving this referenced agent
the agent adds it to its network, removing the neighbor with the lowest performance.
Figure 2 shows the control screen developed in JADE platform, allowing to observe in real-time
the changes in the structure of the social network, the messages exchanged between them, as well
as a detailed action log, which enables us to analyzing the SNA metrics used for decision-making
by each agent.
5. Final Considerations
Multi-agent systems and social networks are closely united. These two theories help to shape and
understand social phenomena in many different ways. This work was concerned to understand
how social networks could improve the multi-agent system modeling process.
9. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
59
The integration between the two areas can be in two ways: macro and micro modeling. The macro
modeling consists in using the existing metrics in SNA to analyze multi-agent behaviors. Given
the inherent distribution of multi-agents systems and the high degree of interaction in the MAS
society, it is important to have a tool that assists in validation of proposed model.
On the other hand, in the micro modeling agents are built making use of social networking
metrics in their behavior. With this, the interaction between the agents can generate new social
phenomena, as well as allow them to take a decision based on your social network. The prototype
used this approach and showed how an agent society can be built to solve a set of tasks where
teams are formed based on the social network structure.
As future work, we intend to improve the modeling of new social behaviors, such as anarchy, lack
of collaboration, and new strategies of relationship between agents that allow a multi-agent
society to act on non-cooperative scenarios.
References
[1] Axelrod, R.: Advancing the art of simulation in the social sciences. Working Papers 97-05-048, Santa
Fe Institute (May 1997)
[2] dasGraças Bruno Marietto, M.: DefiniçãoDinâmica de EstratégiasInstrucionaisemSistemas de
TutoriaInteligente: Uma AbordagemMultiagentesna WWW. PhD thesis, InstitutoTecnol´ogico de
Aeron´autica (2000)
[3] de Lira Lino, N.: Modelo de percep¸c˜ao de agentesbaseados no sistema de emo¸c˜oes (2006)
[4] Castelfranchi, C., Falcone, R.: From automaticity to autonomy: The frontier of artificial agents. In
Hexmoor, H., Castelfranchi, C., Falcone, R., eds.: Agent Autonomy. Volume 7 of Multiagent
Systems, Artificial Societies, and Simulated Organizations. Springer US (2003) 103–136
[5] Jiang, Y., Jiang, J.: Understanding social networks from a multiagent coordination perspective. IEEE
Transactions on Parallel and Distributed Systems 99(PrePrints) (2013) 1
[6] Sless, L., Hazon, N., Kraus, S., Wooldridge, M.: Forming coalitions and facilitating relationships for
completing tasks in social networks. In: Proceedings of the 2014 International Conference on
Autonomous Agents and Multi-agent Systems. AAMAS ’14, Richland, SC, International Foundation
for Autonomous Agents and Multiagent Systems (2014) 261–268
[7] Pujol, J.M., Sanguesa, R., Delgado, J.: Extracting reputation in multi agent systems¨ by means of
social network topology. In: Proceedings of the First International Joint Conference on Autonomous
Agents and Multiagent Systems: Part 1. AAMAS ’02, New York, NY, USA, ACM (2002) 467–474
[8] Pitt, J., Ramirez-Cano, D., Draief, M., Artikis, A.: Interleaving multi-agent systems and social
networks for organized adaptation. Comput. Math. Organ. Theory 17(4) (November 2011) 344–378
[9] Hu, X., Zhao, J., Zhou, D., Leung, V.C.: A semantics-based multi-agent framework for vehicular
social network development. In: Proceedings of the First ACM International Symposium on Design
and Analysis of Intelligent Vehicular Networks and Applications. DIVANet ’11, New York, NY,
USA, ACM (2011) 87–96
[10] Aziz, A.A., Ahmad, F.: A multi-agent model for supporting exchange dynamics in social support
networks during stress. In: Proceedings of the International Confer-
ence on Brain and Health Informatics - Volume 8211. BHI 2013, New York, NY, USA, Springer-
Verlag New York, Inc. (2013) 103–114
[11] Heppenstall, A.J., McFarland, O.E., Evans, A.J.: Application of multi-agent systems and social
network theory to petrol pricing on uk motorways. In: Proceedings of the 4th International Central
and Eastern European Conference on Multi-Agent Systems and Applications. CEEMAS’05, Berlin,
Heidelberg, Springer-Verlag (2005) 551–554
[12] Ma, L., Zhang, X.: Hierarchical social network analysis using a multi-agent system: A school system
case. Int. J. Agent Technol. Syst. 5(3) (July 2013) 14–32
[13] Kumokawa, S., Kryssanov, V.V., Ogawa, H.: Sona: A multi-agent system to support human
navigation in a community, based on social network analysis. In: Proceedings of the 8th International
Conference on Intelligent Virtual Agents. IVA ’08, Berlin, Heidelberg, Springer-Verlag (2008) 509–
510
10. International Journal of Artificial Intelligence & Applications (IJAIA) Vol. 6, No. 3, May 2015
60
[14] Hamill, L., Gilbert, N.: A simple but more realistic agent-based model of a social network. The Centre
for Research in Social Simulation (2008)
[15] Ko, T.H., Berry, N.M.: Agent-based modeling with social networks for terrorist recruitment. In
McGuinness, D.L., Ferguson, G., eds.: AAAI, AAAI Press / The MIT Press (2004) 1016–1017
[16] Ronald, N., Arentze1, T., Timmermans, H.: An agent-based framework for modelling social influence
on travel behaviour. In: MODSIM. (2009) 2955–2961
[17] Wellman, B.: The persistence and transformation of community: From neighbourhood groups to
social networks (2001)
[18] Gaston, M.E., Desjardins, M.: Agent-organized networks for dynamic team formation. In: In
Proceedings of clustering diameter the Fourth International Joint Conference on Autonomous Agents
and Multiagent Systems. (2005)