Community Search over Big Graphs: Models, algorithms, and Opportunities Xin Huang*, Laks V.S. Lakshmanan, Jianliang Xu UNiversity of british Columbia, Vancouver, canada Hong Kong Baptist University, Hong Kong, China xinhuang@comp. hkbu. edu. hk, laks@ubc. cS. ca, xul@comp. hkbu. edu. hk UBC 浸會 历 1956 BAPTIS
Community Search over Big Graphs: Models, Algorithms, and Opportunities Xin Huang∗† , Laks V.S. Lakshmanan∗ , Jianliang Xu† ∗University of British Columbia, Vancouver, Canada †Hong Kong Baptist University, Hong Kong, China xinhuang@comp.hkbu.edu.hk, laks@ubc.cs.ca, xujl@comp.hkbu.edu.hk
Tutorial outline ntroduction, Motivations, and challenges Networks Community Detection Community Search(4 Parts Densely-connected community search Attributed community search Social circle discovery Querying geo-social groups Future Work Open problems
Tutorial Outline • Introduction, Motivations, and Challenges • Networks & Community Detection • Community Search (4 Parts) – Densely-connected community search – Attributed community search – Social circle discovery – Querying geo-social groups • Future Work & Open Problems 2
Networks Networks are everywhere(e.g. chemistry biology social networks the Web, etc
• Networks are everywhere (e.g. chemistry, biology, social networks, the Web, etc.) 3 Networks
Communities Communities naturally exist in networks. Blogosphere
Communities • Communities naturally exist in networks. Blogosphere 4
Community structure Community structure: Nodes with a shared latent property, densely inter-connected Many reasons for communities to be formed Social Networks Citation Networks World wide web biological Networks
• Community structure: Nodes with a shared latent property, densely inter-connected . • Many reasons for communities to be formed: Social Networks Citation Networks World Wide Web Biological Networks 5 Community Structure