Tutorial: A new SOP for accurate and efficient community detection

Place: 統計数理研究所　セミナー室2 (3階)
/ 3F Seminar Room2 @ The Institute of Statistical Mathematics

Speaker: Frederick K. H. Phoa
(Institute of Statistical Science, Academia Sinica, Taiwan)

Schedule

10:30-11:30
Talk 1: Network Exploration by Complements of Graphs with Graph Coloring

11:30-11:50
Discussion

11:50-13:30
Lunch break

13:30-14:30
Talk 2: A Scanning Method for Detecting Communities in Social Networks

14:30-14:50
Discussion

15:00-16:00
Talk 3: Focus Statistics for Network Centrality and Metaheuristic Approach for Shape Fine-Tune

16:00-16:20
Discussion

Abstract: Community is one of the most important features in social networks. There were many traditional methods in the literature to detect communities in network science and sociological studies, but few were able to identify the statistical significance of the detected communities. Even worse, these methods were computationally infeasible for networks with large numbers of nodes and edges. In these talks, we introduce a new SOP for detecting communities in a social network accurately and efficiently. It consists of four main steps. First, a screening stage is proposed to roughly divide the whole network into communities via complement graph coloring. Then a likelihood-based statistical test is introduced to test for the significance of the detected communities. Once these significant communities are detected, another likelihood-based statistical test is introduced to check for the focus centrality of each community. Finally, a metaheuristic swarm intelligence based (SIB) method is proposed to fine tune the range of each community from its original circular setting. Some famous networks are used as empirical data to demonstrate the process of this new SOP.