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Discovering Overlapping Groups in Social Media. Xufei Wang , Lei Tang, Huiji Gao, and Huan Liu [email protected] Arizona State University. Social Media. Facebook 500 million active users 50% of users log on to Facebook everyday Twitter 100 million users 300, 000 new users everyday

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Discovering overlapping groups in social media

Discovering Overlapping Groups in Social Media

Xufei Wang, Lei Tang, Huiji Gao, and Huan Liu

[email protected]

Arizona State University

Social media
Social Media

  • Facebook

    • 500 million active users

    • 50% of users log on to Facebook everyday

  • Twitter

    • 100 million users

    • 300, 000 new users everyday

    • 55 million tweets everyday

  • Flickr

    • 12 million members

    • 5 billion photos

Activities in social media
Activities in Social Media

Connect with others to form “Friends”

Interactwith others (comment, discussion, messaging)

Bookmarkwebsites/URLs (StumbleUpon, Delicious)

Joingroupsif explicitly exist (Flickr, YouTube)


Updatestatus(Twitter, Facebook)

Sharecontent (Flickr, YouTube, Delicious)

Community structure
Community Structure

  • Behavior Studying

    • Individual ? Too many users

    • Site level ? Lose too much details

    • Community level. Yes, provide information with vary granularity

Overlapping communities
Overlapping Communities




Related work
Related Work

  • Disjoint Community Detection

    • Modularity Maximization

    • Based on Link Structure, (how to understand ?)

  • Overlapping Community Detection

    • Soft Clustering (Clustering is dense)

    • CFinder (Efficiency and Scalability)

  • Co-clustering

    • Disjoint

    • Understanding groups by words (tags)

Problem statement
Problem Statement










Given a User-Tag subscription matrix M, and the number of clusters k, find koverlappingcommunities which consist of both users and tags.

Our contributions
Our Contributions

  • Extracting overlapping communities that better reflect reality

  • Clustering on a user-tag graph. Tags are informative in identifying user interests

    • Understanding groups by looking at tags within each group

Edge centric view
Edge-centric View



















  • Cluster edges instead of nodes into disjoint groups

    • One node can belong to multiple groups

    • One edge belongs to one group

Edge centric view1
Edge-centric View

In an Edge-centric view

Clustering edges
Clustering Edges

  • We can use any clustering algorithms (e.g., k-means) to group similar edges together

  • Different similarity schemes

Defining edge similarity
Defining Edge Similarity





  • α is set to 0.5, which suggests the equal importance of user and tag

  • Define user-user and tag-tag similarity

Similarity between two edges e and e’ can be defined, but not limited, by

Independent learning
Independent Learning

  • Assume users are independent, tags are independent

Normalized learning
Normalized Learning

Differentiate nodes with varying degrees by normalizing each node with its nodal degree

Correlational learning
Correlational Learning

u Х t

u Х k

  • Compute user-user and tag-tag cosine similarity in the latent space

  • Tags are semantically close

    • Tagscars, automobile, autos,car reviewsare used to describe a blog written by sid0722 on BlogCatalog

Spectral clustering perspective
Spectral Clustering Perspective

  • Graph partition can be solved by the Generalized Eigenvalue problem

Spectral clustering perspective1
Spectral Clustering Perspective

  • U and V are the right and left singular vectors corresponding to the top k largest singular values of user-tag matrix M

Plug in L,W,Z, we obtain

Synthetic data sets
Synthetic Data Sets

  • Synthetic data sets

    • Number of clusters, users, and tags

    • Inner-cluster density and Inter-cluster density (1% of total user-tag links)

    • Normalized mutual Information

      • Between 0 and 1

      • The higher, the better

Synthetic performance
Synthetic Performance

We fix the number of users, tags, and density, but vary the number of clusters

Synthetic performance1
Synthetic Performance

We fixed the number of users, tags, and clusters, but vary the inner-cluster density

Social media data sets
Social Media Data Sets

  • BlogCatalog

    • Tags describing each blog

    • Category predefined by BlogCatalog for each blog

  • Delicious

    • Tags describing each bookmark

    • Select the top 10 most frequently used tags for each person

Inferring personal interests
Inferring Personal Interests

Category information reveals personal interests, view group affiliation as features to infer personal interests via cross-validation

Connectivity study
Connectivity Study

The correlation between the number of co-occurrence of two users in different affiliations and their connectivity in real networks.

The larger the co-occurrence of two users, the more likely they are connected

Understanding groups via tag cloud
Understanding Groups via Tag Cloud

Tag cloud for Category Health

Understanding groups via tag cloud1
Understanding Groups via Tag Cloud

Tag cloud for Cluster Health

Understanding groups via tag cloud2
Understanding Groups via Tag Cloud

Tag cloud for Cluster Nutrition

Conclusions and future work
Conclusions and Future Work

  • Overlapping communities on a User-Tag graph

  • Propose an edge-centric view and define edge similarity

    • Independent Learning

    • Normalized Learning

    • Correlational Learning

  • Evaluate results in synthetic and real data sets

  • Many applications: link prediction, Scalability


I. S. Dhillon, “Co-clustering documents and words using bipartite spectral graph partitioning,” in KDD ’01, NY, USA

L. Tang and H. Liu, “Scalable learning of collective behavior based on sparse social dimensions,” in CIKM’09, NY, USA.

L. Tang and H. Liu, “Community Detection and Mining in Social Media,” Morgan & Claypool Publishers, Synthesis Lectures on Data Mining and Knowledge Discovery, 2010.

G. Palla, I. Dernyi, I. Farkas, and T. Vicsek, “Uncovering the overlapping community structure of complex networks in nature and society,” Nature’05, vol.435, no.7043, p.814

K. Yu, S. Yu, and V. Tresp, “Soft clustering on graphs,” in NIPS, p. 05, 2005.

U. Luxburg, “A tutorial on spectral clustering,” Statistics and Computing, vol. 17, no. 4, pp. 395–416, 2007.

M. E. J. Newman and M. Girvan, “Finding and evaluating community structure in networks,” Phys. Rev. E, vol. 69, no. 2, p. 026113, Feb 2004.

S. Fortunato, “Community detection in graphs,” Physics Reports, vol. 486, no. 3-5, pp. 75 – 174, 2010.

Contact the authors
Contact the Authors