1. Evolutionary Clustering and Analysis of Bibliographic Networks Manish Gupta (UIUC) Charu C. Aggarwal (IBM) Jiawei Han (UIUC) Yizhou Sun (UIUC) ASONAM 2011

2. Introduction Information networks are everywhere: social networks, web, academic networks, biological networks. Heterogeneous information networks – Contain multi-typed nodes. – Richer representation compared to homogeneous networks. We study clustering and evolution diagnosis in massive heterogeneous information networks.

3. Contributions We present an evolutionary clustering algorithm for heterogeneous information networks (ENetClus) We define metrics to characterize clustering behavior We perform study of evolution in a bibliographic heterogeneous network: DBLP

4. ENetClus features Multi-typed Evolutionary Temporal smoothness Agglomerative Multiple granularities Based on NetClus Consistency Quality Cluster Sizes Evolution rate Cluster appearance/ disappearance Stability of objects Sociability of objects Social influence Evolution metrics Study over DBLP

5. Problem Formulation Net-Cluster Net-Cluster tree Net-Cluster tree sequence Problem: Given a graph sequence GS, generate a net-cluster tree sequence CTS such that the trees are consistent and represent high-quality clusters. Level 1 Level 2 Level 3 K=3... CT 1 CT 2 CT N CTS

6. Level 1 Level 2 Level 3 K=3 nc

7. ... CT 1 CT 2 CT N

8. Approaches Problem: Perform evolutionary clustering over a sequence of heterogeneous network snapshots Approaches – Use homogeneous clustering techniques Does not exploit rich typed information in network Objects related to same entity may get clustered into different clusters. – Use some heterogeneous network clustering algorithm May provide high snapshot clustering quality But may not provide good consistency between clusterings across snapshots

9. NetClus NetClus is an algorithm to perform clustering over heterogeneous network. It performs iterative ranking of clustering of objects. A probabilistic generative model is used to model the probability of generation of different objects from each cluster. A maximum likelihood technique is used to evaluate the posterior probability of presence of an object in a cluster.

10. NetClus

11. ENetClus For the first time instant, initialization of priors and net clusters is similar to NetClus For other time instants – The prior probability of an object o belonging to cluster c k is defined as its representativeness in the corresponding cluster within the net-cluster tree for the previous time instant. – A target object o is assigned to cluster c k with probability p k where p k is the normalized sum of the prior probabilities of neighboring attribute type objects. Ranking is similar to NetClus except that prior probabilities are also used along with the authority based ranking. Prior weight controls the effect of priors and hence the temporal smoothness.

12. How is ENetClus better than NetClus? NetClus: Inconsistent clusters ENetClus: Consistent clusters Snapshot1 Snapshot2Snapshot3 Snapshot1 Snapshot2Snapshot3

13. Metrics Membership probability of object o to cluster c i is denoted by Consistency: Chained path consistency: product of consistency over each interval in the sequence

14. Metrics Snapshot Quality – Compactness – Entropy

15. Metrics O’: Objects at time y but not at y-1 O’’: Objects at time y O’’’: Objects at time y but not at y+1

16. Metrics Stability of objects – Degree to which an object is stable with respect to its cluster or network Sociability of objects – Degree to which an object interacts with different clusters Effect of social influence: normality – Normality is the degree to which an object follows the cluster trend

17. Experiments Dataset – DBLP 1993 to 2008, 654K papers, 484K authors, 107K title terms and 3900 conferences Number of clusters = 4 Levels of net Cluster tree = 4 Prior weight varied from 0 to 1 – Four_area DM, DB, IR, ML papers 1993 to 2008, 29K papers, 28K authors, 20 conferences

26. Related work Clustering graphs: Mincut, Min-max cut, Spectral, density-based, RankClus [Sun EDBT 09], NetClus [Sun KDD 09] Evolutionary clustering: k-means [Chak KDD06], spectral [Chi KDD07], text streams [Mei KDD05], social network structure [Kuma KDD06] Evolutionary graph studies: GraphScope [Sun KDD07], density-based [Kim VLDB09], analysis [Back KDD06, Lesk KDD05, Lesk KDD08], communities using FacetNet [Lin WWW08], individual objects [Asur KDD07]

27. Conclusion A clustering algorithm for evolution diagnosis of heterogeneous information networks. Metrics for novel insights into the evolution both at the object level and the clustering level Analysis and evolutionary study of DBLP

28. Acknowledgements Research was sponsored in part by the U.S. National Science Foundation under grant IIS-09-05215, and by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053 (NS-CTA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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