1. 3.3 Network-Centric Community Detection  Network-Centric Community Detection –consider the global topology of a network. –It aims to partition nodes of a network into a number of disjoint sets A group in this case is not defined independently  Classification of Network-Centric Community Detection –Vertex Similarity Structurally equivalence, Jaccard similarity, Cosine similarity –Latent Space Model Multi Dimensional Scaling –Block Model Approximation –Spectral Clustering –Modularity Maximization 1

2. 3.3 Network-Centric Community Detection  Vertex Similarity –Vertex similarity is defined in terms of the similarity of their social circles, e.g., the number of friends they share in common –Various Definitions and Methods for vertex similarity Structurally equivalence Automorphic equivalence Regular equivalence k-means algorithm with connection feature Jaccard similarity Cosine similarity 2

3. 3.3 Network-Centric Community Detection 3

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6. 6 0.9 0.7 0.4 0.2 0.9 0.4 0.7 0.2

7. 3.3 Network-Centric Community Detection 7 I. Borg and P. Groenen. Modern Multidimensional Scaling: theory and applications. Springer, 2005.

8. 3.3 Network-Centric Community Detection 8

9.  MDS Example (2/3) 9

10. 3.3 Network-Centric Community Detection  MDS Example (3/3) 10 K-means algorithm