Local Spectral Clustering for Overlapping Community Detection
Abstract
Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms for mining communities have focused on global graph structure, and often run in time proportional to the size of the entire graph. As we explore networks with millions of vertices and find communities of size in the hundreds, it becomes important to shift our attention from macroscopic structure to microscopic structure in large networks. A growing body of work has been adopting local expansion methods in order to identify communities from a few exemplary seed members.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 10, 2018
- Source ID
- 10.1145/3106370
Entities
People
- David Bindel
- John Hopcroft
- Kun He
- Kyle Kloster
- Yixuan Li
Organizations
- Army Research Office
- Cornell University
- Huazhong University of Science and Technology
- National Natural Science Foundation of China
- North Carolina State University