1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 SCAN: A Structural Clustering Algorithm for Networks Xiaowei Xu, Nurcan Yuruk, Zhidan Feng, Thomas Schweiger KDD 2007 Reported by Wen-Chung Liao, 2010/04/07

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines  Motivation  Objective  THE NOTION OF STRUCTURE- CONNECTED CLUSTERS  Algorithm  Evaluation  Conclusions  Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Motivation  These methods tend to cluster networks such that there are a dense set of edges within every cluster and few edges between clusters. ─ Modularity-based algorithms ─ Normalized cut  However, they do not distinguish the roles of the vertices in the networks. ─ Some vertices are members of clusters; ─ some vertices are hubs ─ some vertices are outliers

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Objectives  Propose a new method for network clustering called SCAN  The goal of our method is to find clusters, hubs, and outliers in large networks.  To achieve this goal, we use the neighborhood of the vertices as clustering criteria instead of only their direct connections.  vertices 0 and 5  vertex 13 and vertex 9  vertex 6

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 THE NOTION OF STRUCTURE- CONNECTED CLUSTERS VERTEX STRUCTURE STRUCTURAL SIMILARITY ε-NEIGHBORHOOD CORE DIRECT STRUCTURE REACHABILITY STRUCTURE REACHABILITY STRUCTURE CONNECTIVITY

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 THE NOTION OF STRUCTURE- CONNECTED CLUSTERS OUTLIER ε,μ (v) ⇔ (1) v is not a member of any cluster (2) v does not bridge different clusters

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 O(n)

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Evaluation Vertices : 1,000 to 1,000,000 edges : 2,182 to 2,000,190.

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 ε= 0.5, μ = 2 NCAA Football Bowl Subdivision schedule 180 vertices & 787 edges. 119 Bowl Subdivision schools 115 schools into eleven conferences ( 11 clusters) four independent schools at this top level (hubs) 61 lower division schools (outliers)

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 “conservative”, ■ “neutral”, ■ “liberal”, ■ ε= 0.35, μ = 2 “conservative”, ■ “neutral”, ▲ “liberal”, ● ●▲■●▲■

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Conclusions  Propose a method called SCAN to detect clusters, hubs and outliers in networks.  SCAN clusters vertices based on their common neighbors. ─ Two vertices are assigned to a cluster according to how they share neighbors.  we plan to apply SCAN to analyze biological networks ─ metabolic networks ─ gene co-expression networks.

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 13 Comments  Advantage  Shortage  Applications ─ Viral marketing ─ Epidemiology