Presentation is loading. Please wait.

Presentation is loading. Please wait.

LinkSCAN*: Overlapping Community Detection Using the Link-Space Transformation Sungsu Lim †, Seungwoo Ryu ‡, Sejeong Kwon §, Kyomin Jung ¶, and Jae-Gil.

Similar presentations


Presentation on theme: "LinkSCAN*: Overlapping Community Detection Using the Link-Space Transformation Sungsu Lim †, Seungwoo Ryu ‡, Sejeong Kwon §, Kyomin Jung ¶, and Jae-Gil."— Presentation transcript:

1 LinkSCAN*: Overlapping Community Detection Using the Link-Space Transformation Sungsu Lim †, Seungwoo Ryu ‡, Sejeong Kwon §, Kyomin Jung ¶, and Jae-Gil Lee † † Dept. of Knowledge Service Engineering, KAIST ‡ Samsung Advanced Institute of Technology § Graduate School of Cultural Technology, KAIST ¶ Dept. of Electrical and Computer Engineering, SNU ICDE 2014

2 April 1,20142 Contents Motivation Link-Space Transformation Proposed Algorithm: LinkSCAN* Experiment Evaluation Conclusions

3 April 1,20143 Community Detection Network communities Sets of nodes where the nodes in the same set are similar (more internal links) and the nodes in different sets are dissimilar (less external links) Communities, clusters, modules, groups, etc. Non-overlapping community detection Finding a good partition of nodes Clusters are NOT overlapped

4 April 1,20144 Overlapping Community Detection A person (node) can belong to multiple communities, e.g., family, friends, colleagues, etc. Overlapping community detection allows that a node can be included in different groups family,friends,colleagues,

5 April 1,20145 Existing Methods Node-based: A node overlaps if more than one belonging coefficient values are larger than some threshold Label Propagation (COPRA) [Gregory 2010, Subelj and Bajec 2011] Structure-based: A node overlaps if it participates in multiple base structures with different memberships Clique Percolation (CPM) [Palla et al. 2005, Derenyi et al. 2005] Link Partition [Evans and Lambiotte 2009, Ahn et al. 2010] f(i,c1)=0.35, f(i,c2)=0.05, f(i,c3)=0.4, … f(i,c)=mean(f(j,c)) i i i Base structure: links

6 April 1,20146 Limitations of Existing Methods The existing methods do not perform well for 1. networks with many highly overlapping nodes, 2. networks with various base structures, and 3. networks with many weak-ties i i f(i,c1)=0.2, f(i,c2)=0.15, f(i,c3)=0.25, f(i,c4)=0.2, … c1 c4 c2 c3 i Weak-tie i: overlapping COPRA fails i: non-overlapping CPM fails i: non-overlapping Link partition fails

7 April 1,20147 Contents Motivation Link-Space Transformation Proposed Algorithm: LinkSCAN* Experiment Evaluation Conclusions

8 April 1,20148 Our Solution We propose a new framework called the link-space transformation that transforms a given graph into the link-space graph We develop an algorithm that performs a non- overlapping clustering on the link-space graph, which enables us to discover overlapping clustering Original Graph Overlapping Communities Link Communities Link-Space Graph Link-Space Transformation Non-overlapping Clustering Membership Translation

9 April 1,20149 Overall Procedure We propose an overlapping clustering algorithm using the link-space transformation Original Graph Overlapping Communities Link Communities Link-Space Graph Link-Space Transformation Non-overlapping Clustering Membership Translation

10 April 1, Link-Space Transformation Topological structure Each link of an original graph maps to a node of the link-space graph Two nodes of the links-space graph are adjacent if the corresponding two links of the original graph are incident Weights Weights of links of the link-space graph are calculated from the similarity of corresponding links of the original graph 657 k 8 4 i 12 3 j 0 i1j1 i0 i2 ik j2 j3 j4 jk k5 k8 k6 k7

11 April 1, Overall Procedure Overlapping clustering algorithm using the link- space transformation Original Graph Overlapping Communities Link Communities Link-Space Graph Link-Space Transformation Membership Translation Non-overlapping Clustering

12 April 1, Clustering on Link-Space Graph Applying a non-overlapping clustering algorithm to the link-space graph We use structural clustering that can assign a node into hubs or outliers (neutral membership) Original graph Non-overlapping clustering on the link-space graph / /2 1 1 Another weights are less than 1/3

13 April 1, Overall Procedure Overlapping clustering algorithm using the link- space transformation Original Graph Overlapping Communities Link Communities Link-Space Graph Link-Space Transformation Membership Translation Non-overlapping Clustering

14 April 1, Membership Translation Memberships of nodes of the link-space graph map to the memberships of links of the original graph Memberships of a node of the original graph are from the memberships of incident links of the node Membership translation Non-overlapping clustering on the link-space graph 1/ /

15 April 1, Advantages of Link-Space Graph Inheriting the advantages of the link-space graph, finding disjoint communities enables us to find overlapping communities where its original structure is preserved since similarity properly reflect the structure of the original graph. Easier to find overlapping communities Preserving the original structure Easier to find overlapping communities while preserving the original structure Link-space graph

16 April 1, Contents Motivation Link-Space Transformation Proposed Algorithm: LinkSCAN* Experiment Evaluation Conclusions

17 April 1, LinkSCAN* We propose an efficient overlapping clustering algorithm using the link-space transformation Original Graph Overlapping Communities Link Communities Link-Space Graph Link-Space Transformation Structural Clustering Membership Translation For a massive graph, it may be dense

18 April 1, LinkSCAN* We propose an efficient overlapping clustering algorithm using the link-space transformation Original Graph Link Communities Link-Space Graph Link-Space Transformation Structural Clustering Overlapping Communities Membership Translation Sampling process

19 April 1, LinkSCAN* We propose an efficient overlapping clustering algorithm using the link-space transformation Original Graph Link Communities Link-Space Graph Link-Space Transformation Structural Clustering Overlapping Communities Membership Translation Sampled Graph Link Sampling

20 April 1, Link Sampling

21 April 1, Contents Motivation Link-Space Transformation Proposed Algorithm: LinkSCAN* Experiment Evaluation Conclusions

22 April 1, Network Datasets Synthetic network: LFR benchmark networks [Lancichinetti and Fortunato 2009] Real network: Social and information networks [snap.stanford.edu/data/ and # nodes# linksAver. degreeClust. Coeff. DBLP1,068,0373,800, Amazon334,863925, Enron- 36,692183, Brightkite58,228214, Facebook63,392816, WWW325,7291,090,

23 April 1, Performance Evaluation When ground-truth is known NMI for overlapping clustering [ancichietti et al. 2009] F-score (performance of identifying overlapping nodes) When ground-truth is unknown Quality (M ov ): Modularity for overlapping clustering [Lazar et al. 2010] Coverage (CC): Clustering coverage [Ahn et al. 2010]

24 April 1, Problem 1 For networks with many highly overlapping nodes, LinkSCAN* outperforms the existing methods.

25 April 1, Problem 2 For networks with various base-structures, our method performs well compared to the existing methods

26 April 1, Problem 3 For networks with many weak ties, the existing methods fail for the following toy networks. But, LinkSCAN* detects all the clusters well

27 April 1, Real Networks For real network datasets, the normalized measure of (Quality + Coverage) indicates that LinkSCAN* is better than the existing methods.

28 April 1, Link Sampling The comparisons between the use of the link-space graph (LinkSCAN) and the use of sampled graphs (LinkSCAN*) show that LinkSCAN* improves efficiency with small errors

29 April 1, Scalability The running time of LinkSCAN ∗ for a set of LFR benchmark networks shows that LinkSCAN ∗ has near-linear scalability

30 April 1, Contents Motivation Link-Space Transformation Proposed Algorithm: LinkSCAN* Experiment Evaluation Conclusions

31 April 1, Conclusions We propose a notion of the link-space transformation and develop a new overlapping clustering algorithms LinkSCAN* that satisfy membership neutrality LinkSCAN* outperforms existing algorithms for the networks with many highly overlapping nodes and those with various base-structures

32 April 1, Acknowledgement Coauthors Funding Agencies This research was supported by National Research Foundation of Korea

33 April 1, Thank You!


Download ppt "LinkSCAN*: Overlapping Community Detection Using the Link-Space Transformation Sungsu Lim †, Seungwoo Ryu ‡, Sejeong Kwon §, Kyomin Jung ¶, and Jae-Gil."

Similar presentations


Ads by Google