Density-based Community Detection in Geo-Social Networks

MPhil Thesis Defence


Title: "Density-based Community Detection in Geo-Social Networks"

By

Mr. Kai YAO


Abstract

Communities are basic structures for understanding the organization of many 
real-world networks, such as social networks, knowledge graphs, and biological 
networks. Many approaches have been developed for identifying communities; 
these approaches essentially segment networks based on topological structure or 
the attribute similarity of vertices, while few approaches consider the spatial 
character of the networks. They can yield communities whose members are far 
away from each other. In some location-based services, like setting up events, 
it is important to find groups of people who are both socially and physically 
close to each other. Thus, the relations among vertices are defined not only by 
their connections but also by the spatial distance between them. In this 
thesis, we propose a density-based method of detecting communities in 
geo-social networks to identify communities that are both highly topologically 
connected and spatially clustered. After formally defining the model and the 
geo-social distance measure it relies on, we present efficient algorithms for 
its implementation. Then, we propose efficient optimization techniques to 
reduce computation cost. We evaluate the effectiveness of our model via a case 
study on real data; In addition, we design two quantitative measures, called 
social entropy and community score to evaluate the quality of the discovered 
communities. The efficiency of our algorithms is also evaluated experimentally.


Date:			Wednesday, 13 February 2019

Time:			5:00pm - 7:00pm

Venue:			Room 4472
 			Lifts 25/26

Committee Members:	Prof. Dimitris Papadias (Supervisor)
 			Dr. Ke Yi (Chairperson)
 			Dr. Raymond Wong


**** ALL are Welcome ****