1. A Local Seed Selection Algorithm for Overlapping Community Detection 1 A Local Seed Selection Algorithm for Overlapping Community Detection Farnaz Moradi, Tomas Olovsson, Philippas Tsigas

2. A Local Seed Selection Algorithm for Overlapping Community Detection 2 Community detection in large-scale real networks Global and local algorithms Local algorithms for global community detection –Seed expansion –Seed selection Motivation

3. A Local Seed Selection Algorithm for Overlapping Community Detection 3 Community detection algorithms –Global and local algorithms Seed selection –Challenges Proposed seed selection algorithm –Link prediction –Graph coloring Experimental Results Conclusions Outline

4. A Local Seed Selection Algorithm for Overlapping Community Detection 4 Global algorithms require the global structure of the network to be known –High quality communities –Not scalable Local algorithms only need the knowledge of local neighborhood of the seed nodes –Easily paralelizable –Low coverage if seeds are not selected carefully Community Detection Algorithms

5. A Local Seed Selection Algorithm for Overlapping Community Detection 5 Naive approach –All nodes being expanded –Expensive Challenges in local seed selection –Unaccessible global information –Unknown number of seeds –Well distributed over the network –No neighboring seeds Seed Selection

6. A Local Seed Selection Algorithm for Overlapping Community Detection 6 Spread hub (SH) [CIKM 2013] –Highest degree nodes (k or higher) Low conductance cuts (EC) [KDD 2012] –Egonets with low conductance Local maximal degree (MD) [SNA 2012] –Local maximal degree nodes Seed Selection Algorithms Global Local

7. A Local Seed Selection Algorithm for Overlapping Community Detection 7 Properties –Local –Parameter free –Distributed/parallelizable Approach –Link prediction –Graph coloring Proposed Local Seed Selection Algorithm

8. A Local Seed Selection Algorithm for Overlapping Community Detection 8 Predicting the relations that should exist or are very likely to be formed in a network Local similarity indices –CN: Common Neighbors, PA: Preferrential Attachment, HP: Hub Promoted, LHN: Leich-Holme-Newman, RA: Resource Allocation We define a similarity score for seed selection as sum of the similarities of a node with its neighbors Link Prediction

9. A Local Seed Selection Algorithm for Overlapping Community Detection 9 Intution: a node which has high similarity with its neighbors is expected to be in the same community with its neighbors Link Prediction-Based Seeding Similarity score calculation using common neighbors (CN) 10 6 8 7 9 13 11 12 14 5 0 1 2 4 3 15 Local seed selection based on similarity scores SS(5)= CN(5,0)+CN(5,1)+CN(5,2) +CN(5,3)+CN(5,4)+CN(5,6) = 4+4+4+4+4+0 = 20 10 6 8 7 9 13 11 12 14 5 0 1 2 4 3 15 20 12 10 6 8 8 8 6 8 2

10. A Local Seed Selection Algorithm for Overlapping Community Detection 10 Enhancing our seed selection algorithm –Well distributed seeds –No neighboring seeds Steps of the algorithm: 1.Calculate the similarity scores 2.Nodes with the highest local similarity score pick a specific color (in contrast to basic random coloring) 3.Other nodes pick a color at random 4.Color conflicts are resolved locally 5.Nodes with the specific color are selected as seeds Biased Coloring-Based Seeding

11. A Local Seed Selection Algorithm for Overlapping Community Detection 11 Biased Coloring-Based Seeding 10 6 8 7 9 13 11 12 14 5 0 1 2 4 3 15 10 6 8 7 9 13 11 12 14 5 0 1 2 4 3 15 10 6 8 7 9 13 11 12 14 5 0 1 2 4 3 15 20 12 10 6 8 8 8 6 8 2 C1 C2 C3 C4 C5 C6 1. Similarity score calculation using common neighbors 2,3. Local color assignment based on similarity scores 4,5. Local color conflict resolution and seed selection Specific color

12. A Local Seed Selection Algorithm for Overlapping Community Detection 12 The selected seed nodes are expanded into overlapping communities –Local community detection Personalized PageRank-based community detection algorithm –Yang and Leskovec [ICDM 2012] Local Community Detection

13. A Local Seed Selection Algorithm for Overlapping Community Detection 13 Large-scale real networks Compare local seed selection algorithms –Number of seeds –Quality of the communities (F1-score and conductance) –Coverage of the communities –Execution time Experimental Evaluation

14. A Local Seed Selection Algorithm for Overlapping Community Detection 14 Experimental Results Link Prediction-Based Seeding

15. A Local Seed Selection Algorithm for Overlapping Community Detection 15 Experimental Results Biased Coloring-Based Seeding

16. A Local Seed Selection Algorithm for Overlapping Community Detection 16 Experimental Results Execution Time Seeding Community Detection F1-ScoreConductanceCoverage PA+Coloring52 s2 h 38 m0.550.220.99 AmazonAll-17 h 15 m0.510.231.00 DEMON-37 h 40 m0.510.500.79 PA+Coloring2 m 16 s1 h 12 m0.190.300.96 DBLPAll-8 h 42 m0.210.311.00 DEMON-32 h 54 m0.250.630.85

17. A Local Seed Selection Algorithm for Overlapping Community Detection 17 A novel seed selection algorithm –Link prediction-based and biased coloring-based Our biased coloring algorithm can be used to improve existing seed selection algorithms Experiments on large-scale real networks –Well distributed seeds over the network –Communities with high coverage and quality –Reduced execution time Conclusions

18. A Local Seed Selection Algorithm for Overlapping Community Detection 18 7 8 5 6 12 16 13 0 14 4 3 2 1 15 9 11 18 23 17 21 20 19 10 22 7 8 5 6 12 16 13 0 14 4 3 2 1 15 9 11 18 23 17 21 20 19 10 22 7 8 5 6 12 16 13 0 14 4 3 2 1 15 9 11 18 23 17 21 20 19 10 22 1. Similarity score calculation using common neighbors (CN) C1 C2 C3 C4 C5 C6 2,3. Local color assignment based on similarity scores Specific color 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4,5. Local color conflict resolution and seed selection