The document presents a new greedy incremental approach for community detection in social networks. It begins by calculating the degree of nodes and sorting them in descending order. Initial communities are formed with the highest degree nodes. Then nodes are incrementally added to communities if it increases the community density. The approach is tested on standard datasets and able to detect communities reasonably well in less dense graphs. However, there is scope to improve performance on very dense graphs such as implementing it in parallel processing.
The study about the analysis of responsiveness pair clustering tosocial netwo...acijjournal
In this study, regional (cities, towns and villages
) data and tweet data are obtained from Twitter, an
d
extract information of "purchase information (Where
and what bought)" from the tweet data by
morphological analysis and rule-based dependency an
alysis. Then, the "The regional information" and th
e
"Theinformation of purchase history (Where and wha
t bought information)" are captured as bipartite
graph, and Responsiveness Pair Clustering analysis
(a clustering using correspondence analysis as
similarity measure) is conducted. In this study, si
nce it was found to be difficult to analyze a netwo
rk such
as bipartite graph having limitations in links by u
sing modularity Q, responsiveness is used instead o
f
modularity Q as similarity measure. As a result of
this analysis, "regional information cluster" which
refers
to similar "Theinformation of purchase history" nod
es group is generated. Finally, similar regions are
visualized by mapping the regional information clus
ter on the map. This visualization system is expect
ed to
contribute as an analytical tool for customers’ pur
chasing behaviour and so on.
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
The study about the analysis of responsiveness pair clustering tosocial netwo...acijjournal
In this study, regional (cities, towns and villages
) data and tweet data are obtained from Twitter, an
d
extract information of "purchase information (Where
and what bought)" from the tweet data by
morphological analysis and rule-based dependency an
alysis. Then, the "The regional information" and th
e
"Theinformation of purchase history (Where and wha
t bought information)" are captured as bipartite
graph, and Responsiveness Pair Clustering analysis
(a clustering using correspondence analysis as
similarity measure) is conducted. In this study, si
nce it was found to be difficult to analyze a netwo
rk such
as bipartite graph having limitations in links by u
sing modularity Q, responsiveness is used instead o
f
modularity Q as similarity measure. As a result of
this analysis, "regional information cluster" which
refers
to similar "Theinformation of purchase history" nod
es group is generated. Finally, similar regions are
visualized by mapping the regional information clus
ter on the map. This visualization system is expect
ed to
contribute as an analytical tool for customers’ pur
chasing behaviour and so on.
1. Basics of Social Networks
2. Real-world problem
3. How to construct graph from real-world problem?
4. What graph theory problem getting from real-world problem?
5. Graph type of Social Networks
6. Special properties in social graph
7. How to find communities and groups in social networks? (Algorithms)
8. How to interpret graph solution back to real-world problem?
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
Community analysis using graph representation learning on social networksMarco Brambilla
In a world more and more connected, new and complex interaction
patterns can be extracted in the communication between people.
This is extremely valuable for brands that can better understand
the interests of users and the trends on social media to better target
their products. In this paper, we aim to analyze the communities
that arise around commercial brands on social networks to understand
the meaning of similarity, collaboration, and interaction
among users.We exploit the network that builds around the brands
by encoding it into a graph model.We build a social network graph,
considering user nodes and friendship relations; then we compare
it with a heterogeneous graph model, where also posts and hashtags
are considered as nodes and connected to the different node
types; we finally build also a reduced network, generated by inducing
direct user-to-user connections through the intermediate
nodes (posts and hashtags). These different variants are encoded
using graph representation learning, which generates a numerical
vector for each node. Machine learning techniques are applied to
these vectors to extract valuable insights for each user and for the
communities they belong to. In the paper, we report on our experiments
performed on an emerging fashion brand on Instagram, and
we show that our approach is able to discriminate potential customers
for the brand, and to highlight meaningful sub-communities
composed by users that share the same kind of content on social
networks.
Community search is the problem of finding a good community for a given set of query vertices.
In this work we propose a novel method that it is in general more efficient and effective than state-of-the art, it can handle multiple query vertices, it yields optimal communities, and it is parameter free.
Iterative knowledge extraction from social networks. The Web Conference 2018Marco Brambilla
Knowledge in the world continuously evolves, and ontologies are largely incomplete, especially regarding data belonging to the so-called long tail. We propose a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates. Our method can run iteratively, using the results as new seeds.
In this paper we address the following research questions: (1) How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds (2) How does the reconstructed domain knowledge spread geographically (3) Can the method be used to inspect the past, present, and future of knowledge (4) Can the method be used to find emerging knowledge?.
This work was presented at The Web Conference 2018, MSM workshop.
SCALABLE LOCAL COMMUNITY DETECTION WITH MAPREDUCE FOR LARGE NETWORKSIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
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
Community analysis using graph representation learning on social networksMarco Brambilla
In a world more and more connected, new and complex interaction
patterns can be extracted in the communication between people.
This is extremely valuable for brands that can better understand
the interests of users and the trends on social media to better target
their products. In this paper, we aim to analyze the communities
that arise around commercial brands on social networks to understand
the meaning of similarity, collaboration, and interaction
among users.We exploit the network that builds around the brands
by encoding it into a graph model.We build a social network graph,
considering user nodes and friendship relations; then we compare
it with a heterogeneous graph model, where also posts and hashtags
are considered as nodes and connected to the different node
types; we finally build also a reduced network, generated by inducing
direct user-to-user connections through the intermediate
nodes (posts and hashtags). These different variants are encoded
using graph representation learning, which generates a numerical
vector for each node. Machine learning techniques are applied to
these vectors to extract valuable insights for each user and for the
communities they belong to. In the paper, we report on our experiments
performed on an emerging fashion brand on Instagram, and
we show that our approach is able to discriminate potential customers
for the brand, and to highlight meaningful sub-communities
composed by users that share the same kind of content on social
networks.
Community search is the problem of finding a good community for a given set of query vertices.
In this work we propose a novel method that it is in general more efficient and effective than state-of-the art, it can handle multiple query vertices, it yields optimal communities, and it is parameter free.
Iterative knowledge extraction from social networks. The Web Conference 2018Marco Brambilla
Knowledge in the world continuously evolves, and ontologies are largely incomplete, especially regarding data belonging to the so-called long tail. We propose a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates. Our method can run iteratively, using the results as new seeds.
In this paper we address the following research questions: (1) How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds (2) How does the reconstructed domain knowledge spread geographically (3) Can the method be used to inspect the past, present, and future of knowledge (4) Can the method be used to find emerging knowledge?.
This work was presented at The Web Conference 2018, MSM workshop.
SCALABLE LOCAL COMMUNITY DETECTION WITH MAPREDUCE FOR LARGE NETWORKSIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
Clustering in Aggregated User Profiles across Multiple Social Networks IJECEIAES
A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble Kmeans clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.
Optimizing community detection in social networks using antlion and K-medianjournalBEEI
Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.
Community Detection in Networks Using Page Rank Vectors ijbbjournal
Nodes in the real world networks organize in the form of network communities. A community (also referred
to as module or cluster)is defined as where the links are denser inside the nodes and sparser outside the
nodes in the network. Communities in the networks also overlap because the nodes may belong to different
clusters at once. The task of detecting communities in networks becomes an open problem because of lack
of reliable algorithms. In practice all the existing community detection methods work good for nonoverlapping
communities and fail to detect communities with dense overlaps. We developed a novel method
for detecting communities by considering a single seed node. This method successfully captures the
overlapping networks ranging from social to information and from biological to citation networks. We
believe that the proposed system works well for the overlapping communities.
Community Detection in Networks Using Page Rank Vectors ijbbjournal
Nodes in the real world networks organize in the form of network communities. A community (also referred
to as module or cluster)is defined as where the links are denser inside the nodes and sparser outside the
nodes in the network. Communities in the networks also overlap because the nodes may belong to different
clusters at once. The task of detecting communities in networks becomes an open problem because of lack
of reliable algorithms. In practice all the existing community detection methods work good for nonoverlapping
communities and fail to detect communities with dense overlaps. We developed a novel method
for detecting communities by considering a single seed node. This method successfully captures the
overlapping networks ranging from social to information and from biological to citation networks. We
believe that the proposed system works well for the overlapping communities.
EVOLUTIONARY CENTRALITY AND MAXIMAL CLIQUES IN MOBILE SOCIAL NETWORKSijcsit
This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.
Finding prominent features in communities in social networks using ontologycsandit
Community detection is one of the major tasks in social networks. The success of any community
depends upon the features that were selected to form the community. So it is important to have
the knowledge of the main features that may affect the community. In this work we have
proposed a method to find prominent features based on which community can be formed.
Ontology has been used for the said purpose.
Delta-Screening: A Fast and Efficient Technique to Update Communities in Dyna...Subhajit Sahu
Highlighted notes during research with Prof. Dip Sankar Banerjee, Prof. Kishore Kothapalli:
Delta-Screening: A Fast and Efficient Technique to Update Communities in Dynamic Graphs.
https://ieeexplore.ieee.org/document/9384277
There are 3 types of community detection methods:
Divisive, Agglomerative, and Multi-level (usually better).
In this paper, heuristics for skipping out most likely unaffected vertices for a modularity-based community detection method like Louvain and SLM (Smart Local Moving) is given. All edge batches are undirected, and sorted by source vertex id. For edge additions, source vertex i, highest modularity changing edge vertex j*, i's neighbors, and j*'s community are marked as affected. For edge deletions, where i and j must be in the same community, i, j, i's neighbors, and i's community are marked as affected. Performance is compared with static, dynamic baseline (incremental), and this method (both Louvain and SLM). Comparison is also done with "DynaMo" and "Batch" community detection methods.
ABSTRACT
This paper introduces an evolutionary approach to enhance the process of finding central nodes in mobile networks. This can provide essential information and important applications in mobile and social networks. This evolutionary approach considers the dynamics of the network and takes into consideration the central nodes from previous time slots. We also study the applicability of maximal cliques algorithms in mobile social networks and how it can be used to find the central nodes based on the discovered maximal cliques. The experimental results are promising and show a significant enhancement in finding the central nodes.
Community detection of political blogs network based on structure-attribute g...IJECEIAES
Complex networks provide means to represent different kinds of networks with multiple features. Most biological, sensor and social networks can be represented as a graph depending on the pattern of connections among their elements. The goal of the graph clustering is to divide a large graph into many clusters based on various similarity criteria’s. Political blogs as standard social dataset network, in which it can be considered as blog-blog connection, where each node has political learning beside other attributes. The main objective of work is to introduce a graph clustering method in social network analysis. The proposed Structure-Attribute Similarity (SAS-Cluster) able to detect structures of community, based on nodes similarities. The method combines topological structure with multiple characteristics of nodes, to earn the ultimate similarity. The proposed method is evaluated using well-known evaluation measures, Density, and Entropy. Finally, the presented method was compared with the state-of-art comparative method, and the results show that the proposed method is superior to the comparative method according to the evaluations measures.
Application Areas of Community Detection: A Review : NOTESSubhajit Sahu
This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time.
https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0
Communities can be of the following **types**:
- Disjoint
- Overlapping
- Hierarchical
- Local.
The following **static** community detection **methods** exist:
- Spectral-based
- Statistical inference
- Optimization
- Dynamics-based
The following **dynamic** community detection **methods** exist:
- Independent community detection and matching
- Dependent community detection (evolutionary)
- Simultaneous community detection on all snapshots
- Dynamic community detection on temporal networks
**Applications** of community detection include:
- Criminal identification
- Fraud detection
- Criminal activities detection
- Bot detection
- Dynamics of epidemic spreading (dynamic)
- Cancer/tumor detection
- Tissue/organ detection
- Evolution of influence (dynamic)
- Astroturfing
- Customer segmentation
- Recommendation systems
- Social network analysis (both)
- Network summarization
- Privary, group segmentation
- Link prediction (both)
- Community evolution prediction (dynamic, hot field)
<br>
<br>
## References
- [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)
The community detection in complex networks has attracted a growing interest and is the subject of several
researches that have been proposed to understand the network structure and analyze the network
properties. In this paper, we give a thorough overview of different community discovery strategies, we
propose taxonomy of these methods, and we specify the differences between the suggested classes which
helping designers to compare and choose the most suitable strategy for the various types of network
encountered in the real world.
Multi-objective NSGA-II based community detection using dynamical evolution s...IJECEIAES
Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.
Similar to Greedy Incremental approach for unfolding of communities in massive networks (20)
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
Greedy Incremental approach for unfolding of communities in massive networks
1. Greedy Incremental approach for unfolding of
communities in massive networks
Mr. Kamal Sutaria Dr. K.H.Wandra Dr.C.K.Bhensdadia Ms Kruti Khalpada Mr.Dipesh Joshi
Research Scholar,
Computer
Engineering Dept,
C.U.SHAH
UNIVERSITY,
Wadhwan, Gujarat
Professor,
Computer
Science Engineering
BITS Education
Campus, Vadodara
Gujarat
Professor,
Computer
Engineering Dept.,
D.D.University
Nadiad,
Gujarat
Assistant Professor,
Computer Engineering
Dept,
Atmiya Institute of
Science &
Technology,Rajkot,Guja
rat
Assistant Professor,
Computer
Engineering Dept,
V.V.P. Engineering
College,Rajkot,Guj
arat
Kamal.sutaria
@gmail.com
Khwandra
@rediffmail.com
Ckbhensdadia
@ddu.ac.in
Krutikhalpada
@yahoo.com
ddipesh4
@gmail.com
Abstract-Social Network Mining has been an area of interesting research due to billions of people using social media.
Community detection is identified as one of the major issues of a social network. Here, a new approach has been
presented for community detection which is greedy as well as incremental in nature. The approach is tested on standard
datasets and the results are presented as well as analyzed.
I. INTRODUCTION
Social media covers the huge platform in the world of internet. It ranges from various blogs to forums to media
sharing and social networking and still, the list continues. With the increase in the social media usage, there is also a
huge increase in the social network usage. That’s why; it has become an interesting topic for researchers around the
world. Social Network is the group of entities in the social media. The social network is made up of different
communities. A community is the set of nodes having links with each other. For an example, a Facebook website
can be called a social network. A group on the Facebook can be called a community. These data related to the social
network are so dynamic and huge. So it becomes important to analyze those data and find some useful information.
Social Network Analysis is the process of demonstrating, exploring and mining meaningful patterns from the social
media data [1]. There are so many interesting applications of social network analysis such as detecting trends in
news, community detection in a social network, brand monitoring for marketing, election results prediction in
politics and so on. These applications make Social network analysis a very interesting area of research.
A. Organization of paper
The next section describes the research work done for the community detection in the social network. Some of the
issues related to the community detection are also discussed in that section. Section III presents the new approach
for community detection. Section IV represents the small example to understand the algorithm and the Section V
presents the results we have derived through the implementation of the proposed system. Section VI covers the
conclusion and the future work. Section VII lists the references which are used for the literature survey.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
257 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
2. II. RELATED WORK
Here, in this section, the research work in the area of social network mining and especially, community detection
has been discussed. In [2], the fundamental concepts are very well explained like basic graph properties, elements of
the graph, how the graph can be used to represent the social network and how partitioning can be performed. If
graphs can be represented using graphs, we can apply all the traditional graph algorithms on it. For simplicity of
operation, the social network is represented in the form of an adjacency matrix. In [3], they discuss the components
of community detection algorithms. According to [3], there are two basic components of any community detection
algorithm; one is the algorithm for detecting communities and the second is the dynamic programming algorithm
which selects small communities to be combined into the large one. The social network is highly dynamic even in
the form of its connections. With time, the connections might get updated. Due to this, the community detection
algorithm has to be applied regularly after some specific time period. Swarm intelligence algorithms are also used
by the researchers for the community detection. In [4], they have used ACO (Ant Colony Optimization) algorithm
for community detection. Swarm intelligence algorithms are based on the real world behavior of insects or animals.
ACO is specially designed based on the behavior of ants. While searching for food, ant lay one chemical named
pheromone on the path so that the other ants can detect the presence of it can follow it in the future to find the
nearest food source. In [5], communities are detected using local neighborhood. Here, the approach does not work
for detecting overlapping communities. As the social networks can be represented using graphs, the traditional graph
algorithms can be used for social networks also. In [6], the spanning tree based community detection method using
max-min modularity has been presented.
III. PROPOSED APPROACH FOR COMMUNITY DETECTION
Here, the GICD (Greedy Incremental approach for Community Detection) approach for detecting communities
from a social network is discussed.
The input to the algorithm is the graph adjacency list. The output is the generated communities.
As the algorithm uses the local best choice for putting a particular node into the community, it can be termed
greedy. The communities are formed one by one. Initially, there are n number of communities where n is the number
of nodes. Eventually, nodes are added to the suitable group. So it is incremental also.
Following is the procedure GICD.
1. Calculate degree of all the nodes and find the average degree Davg
2. Arrange the nodes in the descending order of their degree
3. Unmark all the nodes
4. Consider single/all the node/(s) with the highest degree
5. x=1
6. IC = {Form initial communities same as the number of nodes with the highest degree (n)}, mark those
nodes, IC = Initial community
7. Do
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
258 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
3. a. i= xth
node from IC (node number of IC)
i. For j = 1 to m (number of adjust nodes of I )
If degree (jth
node) >= Davg && degree (jth
node ) < degree(i) then
a. Add this node in the community Ci
b. Mark the node
Else if degree (jth
node) = = degree (i)
a. Check whether the density of the temporary community is increased by
adding the jth
node or not
b. If density increases, add the node to community Ci,
c. Mark the node
Else
a. Put it in the IC
ii. Remove the node i from the IC
iii. x=x+1
While IC is empty
In the first step, we calculate the degree of all the nodes in the given graph. The degree of the node is the number
of outgoing edges connected with the particular node. So if a node is connected with other three nodes, the degree of
the node is three. In the second step, the nodes are arranged in descending order of their degrees. As the strategy is a
greedy strategy, the community formation will be started from the highest degree node. The third step is to unmark
all the nodes so that we can track which nodes are already considered and which are not. In the fourth step, the
highest degree node (or nodes) is (/are) chosen and the initial community is formed with only one node within it. In
the fifth step, the variable x is initialized to 1, which is used to keep track of the node number in a process and works
as a counter. In the sixth step, we initialize the variable IC (Initial Community) same as the number of nodes. In the
seventh step, the process of selecting the appropriate community for a particular node is done based on the
parameter density [2]. By adding a particular node to a community, if the density increases, the node is included in
the community; otherwise, it is not. The selected node is then removed from the IC. The step number seven is
continued until all the nodes are processed and the list IC is empty.
IV. ALGORITHM EXAMPLE
Here, the example shows the working of the GICD algorithm. For simplification of the example, the small
examples are selected.
Figure 1. Example
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
259 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
4. The above example shows the example graph with thirteen nodes. The edges show the interconnection between
the nodes. The following is the result of how the algorithm works in the above example. The following table shows
the degree of all the nodes.
TABLE I
SAMPLE DATASET
Sr. No. Node Degree Adjacent Nodes
1 A 4 {B,C,D,E}
2 B 3 {A,C,D}
3 C 4 {A,B,D,E}
4 D 5 {A,B,C,E,F}
5 E 4 {A,B,C,D}
6 F 2 {D,G}
7 G 2 {F,H}
8 H 4 {G,I,J,K}
9 I 5 {H,J,K,L,M}
10 J 4 {H,I,L,M}
11 K 4 {H,I,L,M}
12 L 4 {I,J,K,M}
13 M 4 {I,J,K,L}
Following is the outcome generated by the algorithm. It detects three communities for the given example which is
but natural.
Figure 2. Output of Sample Dataset
V. RESULTS
TABLE II
IMPLEMENTATION PLATFORM DETAILS
Sr. No Resource Name Specification
1 Compiler Python (3.5.2)
2 CPU Intel Core i3-4005U CPU @ 1.70 GHz 1.70 GHz
3 RAM 4GB
4 OS Ubuntu 16.04
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
260 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
5. The results are taken for the 13 different datasets which are presented in the following table. For each dataset, no
of nodes and no of edges are specified. In the last two columns, it shows time taken by proposed approach as well
as a number of communities detected by the algorithm.
TABLE III
DATASET INFORMATION
Sr.
No
Dataset Name No of nodes No of edges Time No of Communities
1 SAWMILLE 36 62 0.04 1
2 KARATE 34 78 0.16 5
3 MEXICAN DATA 35 117 0.17 2
4 DOLPHINS 62 159 0.18 6
5 POLLBOOK 105 441 0.23 5
6 FOOTBALL 115 1232 0.47 14
7 CELEGANS METABOLIC 453 4596 118 14
8 JAZZ 198 5484 8 3
9 EMAIL 1133 10903 6.8 2
10 EMAIL-EU-CORE 1005 25571 399 20
11 P2PGNUTELLA04 10876 39994 232 29
12 CA-HEPTH 9877 51971 387 515
13 CA-CONDMAT 23133 186936 4899 672
VI. CONCLUSION & FUTURE SCOPE
With this, the new Greedy incremental approach for community detection has been presented implemented and
tested on various standard data sets and other random example sets. The outcome produced by the approach is the
number of communities. The result is compared with the well-known results available. For the graphs which are not
too dense, this algorithm performs nearer to the standard number of communities. From that, we can conclude that
GICD cannot work well with a too much dense graph so there is a scope for improvement there. Apart from this, as
the social network data is tremendously huge, one can go ahead with the parallel implementation of the same.
ACKNOWLEDGMENT
The authors very much appreciate the financial and infrastructure supports by the V.V.P. Engineering College,
Rajkot.
REFERENCES
[1] Yiannis Kompatsiaris, (2013), “Social Networks Mining for Innovative Applications and Users Well-Being”.
[2] Fortunato, Santo "Community detection in graphs." Physics Reports 486.3 (2010): 75-174.
[3] Nguyen, Nam P., et al. "Adaptive algorithms for detecting community structure in dynamic social networks." INFOCOM, 2011 Proceedings
IEEE. IEEE, 2011.
[4] Sadi, Sercan, Şima Etaner-Uyar, and Şule Gündüz-Öğüdücü.( 2009) "Community detection using ant colony optimization techniques.”
[5] Justine Eustace, Xingyuan Wang and Yaozu Cui “Community detection using local neighborhood in complex networks” in Faculty of
Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China
[6] Ranjan Kumar Behera, S. K. Rath and Monalisa Jena “Spanning Tree Based Community Detection using Min-Max Modularity” in 6th
International Conference On Advances In Computing & Communications, ICACC 2016, 6-8 September 2016, Cochin, India.
[7] Lancichinetti, Andrea, and Santo Fortunato. "Community detection algorithms: a comparative analysis." Physical review E 80.5 (2009):
056117.
[8] Ke Xu, Xinfang Zhang ,Mining Community in Mobile Social Network, Procedia Engineering 29 (2012) 3080 – 3084
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
261 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
6. [9] Sadi, Sercan, Sima Etaner-Uyar, and Sule Gündüz-Öğüdücü.( 2009) "Community detection using ant colony optimization techniques."
Proc. Int. Conf. Soft Computing (MENDEL’09).
[10] Michalis Vazirgiannis, Christos Giatsidis and Fragkiskos D. Malliaros “Graph Mining Tools for Community Detection and Evaluation in
Social Networks and the Web” in 22nd International World Wide Web Conference (WWW), May 13-17, 2013 | Rio de Janeiro, Brazil
[11] Guy, I., Avraham, U., Carmel, D., Ur, S., Jacovi, M., & Ronen, I. (2013, May). Mining expertise and interests from social media.
InProceedings of the 22nd international conference on World Wide Web (pp. 515-526). International World Wide Web Conferences
Steering Committee.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
262 https://sites.google.com/site/ijcsis/
ISSN 1947-5500
![Greedy Incremental approach for unfolding of
communities in massive networks
Mr. Kamal Sutaria Dr. K.H.Wandra Dr.C.K.Bhensdadia Ms Kruti Khalpada Mr.Dipesh Joshi
Research Scholar,
Computer
Engineering Dept,
C.U.SHAH
UNIVERSITY,
Wadhwan, Gujarat
Professor,
Computer
Science Engineering
BITS Education
Campus, Vadodara
Gujarat
Professor,
Computer
Engineering Dept.,
D.D.University
Nadiad,
Gujarat
Assistant Professor,
Computer Engineering
Dept,
Atmiya Institute of
Science &
Technology,Rajkot,Guja
rat
Assistant Professor,
Computer
Engineering Dept,
V.V.P. Engineering
College,Rajkot,Guj
arat
Kamal.sutaria
@gmail.com
Khwandra
@rediffmail.com
Ckbhensdadia
@ddu.ac.in
Krutikhalpada
@yahoo.com
ddipesh4
@gmail.com
Abstract-Social Network Mining has been an area of interesting research due to billions of people using social media.
Community detection is identified as one of the major issues of a social network. Here, a new approach has been
presented for community detection which is greedy as well as incremental in nature. The approach is tested on standard
datasets and the results are presented as well as analyzed.
I. INTRODUCTION
Social media covers the huge platform in the world of internet. It ranges from various blogs to forums to media
sharing and social networking and still, the list continues. With the increase in the social media usage, there is also a
huge increase in the social network usage. That’s why; it has become an interesting topic for researchers around the
world. Social Network is the group of entities in the social media. The social network is made up of different
communities. A community is the set of nodes having links with each other. For an example, a Facebook website
can be called a social network. A group on the Facebook can be called a community. These data related to the social
network are so dynamic and huge. So it becomes important to analyze those data and find some useful information.
Social Network Analysis is the process of demonstrating, exploring and mining meaningful patterns from the social
media data [1]. There are so many interesting applications of social network analysis such as detecting trends in
news, community detection in a social network, brand monitoring for marketing, election results prediction in
politics and so on. These applications make Social network analysis a very interesting area of research.
A. Organization of paper
The next section describes the research work done for the community detection in the social network. Some of the
issues related to the community detection are also discussed in that section. Section III presents the new approach
for community detection. Section IV represents the small example to understand the algorithm and the Section V
presents the results we have derived through the implementation of the proposed system. Section VI covers the
conclusion and the future work. Section VII lists the references which are used for the literature survey.
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
257 https://sites.google.com/site/ijcsis/
ISSN 1947-5500](https://image.slidesharecdn.com/31paper31071784ijcsiscamerareadypp-180207063738/85/Greedy-Incremental-approach-for-unfolding-of-communities-in-massive-networks-1-320.jpg)
![II. RELATED WORK
Here, in this section, the research work in the area of social network mining and especially, community detection
has been discussed. In [2], the fundamental concepts are very well explained like basic graph properties, elements of
the graph, how the graph can be used to represent the social network and how partitioning can be performed. If
graphs can be represented using graphs, we can apply all the traditional graph algorithms on it. For simplicity of
operation, the social network is represented in the form of an adjacency matrix. In [3], they discuss the components
of community detection algorithms. According to [3], there are two basic components of any community detection
algorithm; one is the algorithm for detecting communities and the second is the dynamic programming algorithm
which selects small communities to be combined into the large one. The social network is highly dynamic even in
the form of its connections. With time, the connections might get updated. Due to this, the community detection
algorithm has to be applied regularly after some specific time period. Swarm intelligence algorithms are also used
by the researchers for the community detection. In [4], they have used ACO (Ant Colony Optimization) algorithm
for community detection. Swarm intelligence algorithms are based on the real world behavior of insects or animals.
ACO is specially designed based on the behavior of ants. While searching for food, ant lay one chemical named
pheromone on the path so that the other ants can detect the presence of it can follow it in the future to find the
nearest food source. In [5], communities are detected using local neighborhood. Here, the approach does not work
for detecting overlapping communities. As the social networks can be represented using graphs, the traditional graph
algorithms can be used for social networks also. In [6], the spanning tree based community detection method using
max-min modularity has been presented.
III. PROPOSED APPROACH FOR COMMUNITY DETECTION
Here, the GICD (Greedy Incremental approach for Community Detection) approach for detecting communities
from a social network is discussed.
The input to the algorithm is the graph adjacency list. The output is the generated communities.
As the algorithm uses the local best choice for putting a particular node into the community, it can be termed
greedy. The communities are formed one by one. Initially, there are n number of communities where n is the number
of nodes. Eventually, nodes are added to the suitable group. So it is incremental also.
Following is the procedure GICD.
1. Calculate degree of all the nodes and find the average degree Davg
2. Arrange the nodes in the descending order of their degree
3. Unmark all the nodes
4. Consider single/all the node/(s) with the highest degree
5. x=1
6. IC = {Form initial communities same as the number of nodes with the highest degree (n)}, mark those
nodes, IC = Initial community
7. Do
International Journal of Computer Science and Information Security (IJCSIS),
Vol. 15, No. 9, September 2017
258 https://sites.google.com/site/ijcsis/
ISSN 1947-5500](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)