Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
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.
This workshop will introduce some of the main principles and techniques of Social Network Analysis (SNA). We will use examples from organizational and social media-based networks to understand concepts such as network density, diameter, centrality measures, community detection algorithms, etc. The session will also introduce Gephi, a popular program for SNA. Gephi is a free and open-source tool that is available for both Mac and PC computers.
By the end of the session, you will develop a general understanding of what SNA is, what research questions it can help you answer, and how it can be applied to your own research. You will also learn how to use Gephi to visualize and examine networks using various layout and community detection algorithms.
Instructor’s Bio: Dr. Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor at the Ted Rogers School of Management at Ryerson University, and Director of Research at the Social Media Lab. Anatoliy is also a Member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists; a co-editor of a multidisciplinary journal on Big Data and Society; and a founding co-chair of the International Conference on Social Media and Society. His research initiatives explore how social media platforms are changing the ways in which people and organizations communicate, collaborate and disseminate information and how these changes impact the norms and structures of modern society.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
Social Network Analysis Workshop
This talk will be a workshop featuring an overview of basic theory and methods for social network analysis and an introduction to igraph. The first half of the talk will be a discussion of the concepts and the second half will feature code examples and demonstrations.
Igraph is a package in R, Python, and C++ that supports social network analysis and network data visualization.
Ian McCulloh holds joint appointments as a Parson’s Fellow in the Bloomberg School of Public health, a Senior Lecturer in the Whiting School of Engineering and a senior scientist at the Applied Physics Lab, at Johns Hopkins University. His current research is focused on strategic influence in online networks. His most recent papers have been focused on the neuroscience of persuasion and measuring influence in online social media firestorms. He is the author of “Social Network Analysis with Applications” (Wiley: 2013), “Networks Over Time” (Oxford: forthcoming) and has published 48 peer-reviewed papers, primarily in the area of social network analysis. His current applied work is focused on educating soldiers and marines in advanced methods for open source research and data science leadership.
More information about Dr. Ian McCulloh's work can be found at https://ep.jhu.edu/about-us/faculty-directory/1511-ian-mcculloh
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.
This workshop will introduce some of the main principles and techniques of Social Network Analysis (SNA). We will use examples from organizational and social media-based networks to understand concepts such as network density, diameter, centrality measures, community detection algorithms, etc. The session will also introduce Gephi, a popular program for SNA. Gephi is a free and open-source tool that is available for both Mac and PC computers.
By the end of the session, you will develop a general understanding of what SNA is, what research questions it can help you answer, and how it can be applied to your own research. You will also learn how to use Gephi to visualize and examine networks using various layout and community detection algorithms.
Instructor’s Bio: Dr. Anatoliy Gruzd is a Canada Research Chair in Social Media Data Stewardship, Associate Professor at the Ted Rogers School of Management at Ryerson University, and Director of Research at the Social Media Lab. Anatoliy is also a Member of the Royal Society of Canada’s College of New Scholars, Artists and Scientists; a co-editor of a multidisciplinary journal on Big Data and Society; and a founding co-chair of the International Conference on Social Media and Society. His research initiatives explore how social media platforms are changing the ways in which people and organizations communicate, collaborate and disseminate information and how these changes impact the norms and structures of modern society.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Social Media Mining - Chapter 7 (Information Diffusion)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
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.
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
UNIT I- INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
Implementing Link-Prediction for Social Networks in a Database System (DBSoci...Nati Cohen
Our project considers the problem of implementing metrics for link prediction in a social network over different types of database systems (MySQL, Redis and Neo4J). In particular, we study how the features of the database system affect the ease in which link prediction may be performed.
A high-level overview of social network analysis using gephi with your exported Facebook friends network. See more network analysis at http://allthingsgraphed.com.
Social Media Mining - Chapter 7 (Information Diffusion)SocialMediaMining
R. Zafarani, M. A. Abbasi, and H. Liu, Social Media Mining: An Introduction, Cambridge University Press, 2014.
Free book and slides at http://socialmediamining.info/
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.
UNIT II MODELING AND VISUALIZATION
Visualizing Online Social Networks - A Taxonomy of Visualizations - Graph Representation -
Centrality- Clustering - Node-Edge Diagrams - Visualizing Social Networks with Matrix-Based
Representations- Node-Link Diagrams - Hybrid Representations - Modelling and aggregating
social network data – Random Walks and their Applications –Use of Hadoop and Map Reduce -
Ontological representation of social individuals and relationships.
UNIT I- INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
Implementing Link-Prediction for Social Networks in a Database System (DBSoci...Nati Cohen
Our project considers the problem of implementing metrics for link prediction in a social network over different types of database systems (MySQL, Redis and Neo4J). In particular, we study how the features of the database system affect the ease in which link prediction may be performed.
Predictions of links in graphs based on content and information propagations.
Lecture for the M. Sc. Data Science, Sapienza University of Rome, Spring 2016.
Who to follow and why: link prediction with explanationsNicola Barbieri
Presentation @KDD 2014.
In this paper we study link prediction with explanations for user recommendation in social networks. For this problem we propose WTFW (“Who to Follow and Why”), a stochastic topic model for link prediction over directed and nodes-attributed graphs. Our model not only predicts links, but for each predicted link it decides whether it is a “topical” or a “social” link, and depending on this decision it produces a different type of explanation.
Asymmetric Social Proximity Based Private Matching Protocols for Online Socia...1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
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Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
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
Inferring Peer Centrality in Socially-Informed Peer-to-Peer SystemsNicolas Kourtellis
Social applications implemented on a peer-to-peer (P2P) architecture mine the social graph of their users for improved performance in search, recommendations, resource
sharing and others. In such applications, the social graph that connects their users is distributed on the peer-to-peer system: the traversal of the social graph translates to a socially-informed routing in the peer-to-peer layer.
In this work we introduce the model of a projection graph that is the result of mapping a social graph onto a peer-to-peer network. We analytically formulate the relation between metrics in the social graph and in the projection graph. We focus on three such graph metrics: degree centrality, node betweenness centrality, and edge betweenness centrality. We evaluate experimentally the feasibility of estimating these metrics in the projection graph from the metrics of the social graph. Our experiments on real networks show that when mapping communities of 50-150 users on a peer, there is an optimal organization of the projection graph with respect to degree and node betweenness centrality. In this range, the association between the properties of the social graph and the projection graph is the highest, and thus the properties of the (dynamic) projection graph can be inferred from
the properties of the (slower changing) social graph. We discuss the applicability of our findings to aspects of peer-to-peer systems such as data dissemination, social search, peer vulnerability, and data placement and caching.
Inferring Peer Centrality in Socially-Informed Peer-to-Peer Systems. Nicolas Kourtellis and Adriana Iamnitchi. In Proceedings of 11th IEEE International Conference on Peer-to-Peer Computing (P2P'11), Kyoto, Japan, Aug 2011
Existing social network services provide list of friends to users based on their request given. But it will not fulfil the user’s preferences in real life. Due to overloaded memory of the server memory size increases and lacking its efficiency. By implementing the Latent Dirichlet Allocation Algorithm we extracting their lifestyles and sensing the similarity of lifestyles between users by using embedded sensors in the smartphones. Based on friend matching graphs we return a list of people with highest similarity of lifestyles. Feedback mechanism is integrated in this friendbook to get the results of users in choosing friends. We have implemented friendbook on the Android-based smartphones and evaluated its performance on both small scale experiments and large scale simulations. Finally, we reduce the memory size of the server and improving its performance.
Studying user footprints in different online social networksIIIT Hyderabad
With the growing popularity and usage of online social media services, people now have accounts (some times several) on multiple and diverse services like Facebook, LinkedIn, Twitter and YouTube. Publicly available information can be used to create a digital footprint of any user using these social media services. Generating such digital footprints can be very useful for personalization, profile management, detecting malicious behavior of users. A very important application of analyzing users’ online digital footprints is to protect users from potential privacy and security risks arising from the huge publicly available user information. We extracted information about user identities on different social networks through Social Graph API, FriendFeed, and Profilactic; we collated our own dataset to create the digital footprints of the users. We used username, display name, description, location, profile image, and number of connections to generate the digital footprints of the user. We applied context specific techniques (e.g. Jaro Winkler
similarity, Wordnet based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and LinkedIn. In this paper, we present the analysis and results from applying automated classifiers for
disambiguating profiles belonging to the same user from different social networks UserID and Name were found to be the most discriminative features for disambiguating user profiles. Using the most promising set of features and similarity metrics, we
achieved accuracy, precision and recall of 98%, 99%, and 96%, respectively.
2010 Catalyst Conference - Trends in Social Network AnalysisMarc Smith
Review of trends related to social network analysis in the enterprise. Presented at the 2010 Catalyst Conference in San Diego, CA july 29, 2010. Presented with Mike Gotta, Gartner Group.
Identifying Most Relevant Node Path To Increase Connection Probability In Gra...CSCJournals
In social networks, one of the most challenging problems is to find the best way to establish a relationship between two nodes. Different attributes (Topological, Non-Topological) can be used to define friendship score between two nodes which indicates the strength of a relationship. NonTopological attributes can be used to define the strength of a relationship even if two nodes are not connected. The concept of friendship score to define the strength of a relationship between two nodes transforms social network into a complete graph where each node is connected to every other node and where friendship score is used as link attribute. The information on already existing connections in social media network and graph which is formed based on friendship score can be used to find out best way of connecting two different nodes even if no path is in existence in social media network between these nodes.
In this paper, we propose a novel way of estimating friendship score using non-topological attributes based on available information in social media network and algorithm to find out best way of connecting two nodes in the form of chain of reference. The chain of reference between node X1 and Xn is a path X1->X2->….->Xn-1->Xn where each link Xi->Xj is having high friendship score. The chain of reference indicates how X1 can be connected to Xn even if no path exists between X1 and Xn in social media network.
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
Alluding Communities in Social Networking Websites using Enhanced Quasi-cliqu...IJMTST Journal
Social media is attracting global crowd rapidly. In websites such as Facebook, twitter etc one can share, view, like posts, such as images, videos, texts. Users also interact with each other. Communities are part of few such social networking websites. In a community people can learn more about their area of interest, share information on those topics, discuss about their perspectives etc. This paper recommends how community can be suggested to a user based on enhanced quasi clique technique.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
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.
1. LINK PREDICTION IN
SOCIAL NETWORKING
SUBMITTED BY:
Umang Chaudhary (10103408)
Sanyam Gupta (10103405)
Under the Guidance Of: Dr. Buddha Singh
2. WHAT IS SOCIAL NETWORKING?
Social networking is the grouping of individuals into specific
groups, like small rural communities or a neighbourhood
subdivision, if you will. Although social networking is possible
in person, especially in the workplace, universities, and high
schools, it is most popular online.
3. CHALLENGE…!!
Social networks are highly dynamic objects; they
grow and change quickly over time through the
addition of new edges. Its solution is LINK
PREDICTION.
4. WHAT IS LINK
PREDICTION?
Link prediction is an important task for analysing
social networks. It is the method to predict link
between the given nodes using various algorithms.
5. APPLICATIONS
Identifying the structure of a criminal
network by predicting missing links in a
criminal network using incomplete data.
Automatic web hyperlink creation
Website hyperlink prediction
Build recommendation systems (e-
commerce)
6. Protien-protien interaction (bio-informatics)
Annotate PPI graph (bio-informatics)
Identify hidden group of terrorists (security)
Overcoming the data-sparsity problem in
recommender systems using collaborative
filtering.
7. PROBLEM STATEMENT
The network is dynamic as it keeps on
expanding because the users keep
getting added exponentially so
predicting link between the users is a
big challenge. So we are going to
implement algorithms like common
neighbours, jaccard coefficient and
adamic/adar which can predict link
efficiently,
8. Our social networking application is about where
anyone can register and become a member and stay
connected to all their friends and to other users. Our
application includes all the features of a social
networking application like:
We have developed the social networking
system which is client-server based.
We have developed the algorithms for link
prediction that includes prediction on the
basis of common neighbours, common
features, members of the same community
etc.
9. To chek user’s strength we have
calculated betweeness, closeness and
degree of centrality.
We have implemented our proposed
algorithm using python programming
language, GLADE(for GUI) and SQL (for
database).
We have evaluated the performance of
our proposed algorithm.
10. FEATURES OF OUR
APPLICATION
New user can register on the application and can update their
profile and also can view other’s profile.
Users have a unique profile visible to the friends and users of
the application, where they can upload there pictures and
personal information.
Users can add other users.
Most famous person in the group can be predicted in the
application.
We are able to find each user’s strength by calculating
closeness, betweeness and degree of centrality.
Users can upload their status and comment on the status as
well.
11. Users can join communities created by them
in their field of interest.
Users can join groups, create groups.
There is link prediction on the basis of
common or mutual friends so the users will
get friend suggestion on the basis of mutual
friends
The users get friend suggestion also from the
person who are added in the same
community if there are many features
common in them.
12. Users also get friend suggestions of those
persons who have many features in common.
Users can chat with each others.
The application contains basic features of any
social networking website such as liking the
pages, adding friends, uploading status, follow
people etc.
13. METHODS FOR LINK
PREDICTION
There can be many methods that can be
used for predicting the link. Some of them
are:
1. Jaccard coefficient
2. Adamic/adar
3. Common neighbors
4. Graph distance
5. Katz
6. Hitting time
7. Friends measure
8. Preferential attachment score
9. Bayesian algorithm, etc.
14. The link prediction problem
Given a snapshot of a social network
at time t, we seek to accurately predict
the edges that will be added to the
network during the interval (t, t’)
15. Methods we are using for the
prediction
COMMON NEIGHBOURS
JACCARD COEFFICIENT
ADAMIC/ADAR
16. COMMON NEIGHBOURS
A and C have 2 common neighbors,
more likely to collaborate
A
B
C
D
E
18. ADAMIC/ADAR
weighting rarer neighbors more
heavily (gives more weightage to
neighbours that are not shared with
many others)
A
B
C
D
E
19. Methods we are using to find
strengths of users
1. Degree of Centrality: Centrality is
regarded as one of the most important and
commonly used conceptual tools for
exploring actor roles in social networks. A
node’s degree centrality, in an un-directed
graph, is defined as the number of nodes that
are connected to that node.
The definition dictates that “central actors
must be the most active in the sense that
they
have the most ties to other actors in the
network or graph”
20. 2. Closeness: Closeness centrality indicates the
influence of a node on the entire network, and thus
discipline centrality in this research can tell how
“close” each discipline is to the other disciplines
and the influence that a discipline puts on the
entire network.
3. Betweenness: According to the definition
of betweenness, betweenness centrality
reflects the bridge role of a discipline in a
knowledge communication network. The larger
the discipline betweenness, the more control
that the discipline has over the interaction
between other disconnected disciplines.
25. TEST PLAN
We have implemented many test cases
on the modules that have been
developed so far which are as follows:
When we check the prediction on the
basis of common features, we see that
the right person gets predicted.
On changing the features of any users,
new links should pe predicted which is
as per the adamic and adar algorithm.
26. Testing the user login by entering the user
name and password. If the user name and
password is correct then the user will login
otherwise login failed. Below are the
snapshots indicating the login successful and
failure.
If all the fields have been filled for registering
the user, then the user will be registered with
a different id which is the primary key.
On adding friends we test that the friend list
should get updated successfully.
27. REFERENCES
ULRIK BRANDES†.2001. A Faster Algorithm For Betweenness Centrality*. Published in
Journal of Mathematical Sociology
Kazuya Okamoto1, Wei Chen, and Xiang-Yang Li.2008. Ranking of Closeness Centrality
for Large-Scale Social Networks. FAW '08 Proceedings of the 2nd annual international
workshop on Frontiers in Algorithmics ( Pages 186-195)
Luca Maria Aiello, Alain Barrat, Rossano Schifanella, Ciro Cattuto, Benjamin Markines,
Filippo Menczer. 2011. Friendship prediction and homophily in social media. ACM
journal.
Purnamrita Sarkar, Deepayan Chakrabartiy, Michael I. Jordanz. 2012. Nonparametric
Link Prediction in Dynamic Networks. ICML, UK,2012, PAGE NO. 1-8
Zhengdong Lu, Berkant Savas, Wei Tang, Inderjit Dhillon. 2010. Supervised Link
Prediction Using Multiple Sources. 29 th International Conference on Machine Learning,
Edinburgh, Scotland, UK.
Suphakit Niwattanakul, Jatsada Singthongchai, Ekkachai Naenudorn and Supachanun
Wanapu.2013. Using of Jaccard Coefficient with keyword similarity. IMECS
AIROLDI, E., BLEI, D., FIENBERG, S., XING, E., AND JAAKKOLA, T. 2006. Mixed
28. AVIN, C., LOTKER, Z., AND PIGNOLET, Y. 2011. On the elite of social networks. Arxiv preprint
arXiv:1111.3374.
BARABASI, A.-L. AND ALBERT, R. 1999. Emergence of scaling in random networks. Science 286,
509–512.
BLONDEL, V., GUILLAUME, J., LAMBIOTTE, R., AND LEFEBVRE, E. 2008. Fast unfolding of
communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008,
P10008.
CHA, M., HADDADI, H., BENEVENUTO, F., AND GUMMADI, K. P. 2010. Measuring user influence
in twitter: The million follower fallacy. In In Proceedings of the 4th International AAAI Conference on
Weblogs and Social Media (ICWSM). Washington DC, USA.
CHAWLA, N., JAPKOWICZ, N., AND KOTCZ, A. 2004. Editorial: special issue on learning from
imbalanced data sets. ACM SIGKDD Explorations Newsletter 6, 1, 1–6.
CHO, E., MYERS, S., AND LESKOVEC, J. 2011. Friendship and mobility: user movement in
location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference
on Knowledge discovery and data mining. ACM, 1082–1090.
A.-L. Barab´asi and R. Albert, “Emergence of Scaling in Random Networks,” Science, vol. 286, no.
5439, pp. 509-512, Oct. 1999.
D. Wang, Dashun. Pedreschi and A. Barab´asi, “Human Mobility, Social Ties, and Link Prediction,” in
KDD ’11. ACM, 2011, pp. 1100–1108.