A Framework for Local Community Detection in Heterogeneous Networks
- Resource Type
- Conference
- Authors
- Guo, Zhijian; Luo, Peisong; Men, Wenxue
- Source
- 2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP) Intelligent Computing and Signal Processing (ICSP), 2023 8th International Conference on. :696-700 Apr, 2023
- Subject
- Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Social networking (online)
Signal processing algorithms
Signal processing
Approximation algorithms
Heterogeneous networks
Detection algorithms
heterogeneous network
local community detection
relevance measure
meta path
local modularity
- Language
Community detection is crucial in understanding the structural characteristics and underlying information of social networks. Local community detection algorithms that rely solely on local structural information have gained popularity among scholars as they do not require global information of the network and thus, are efficient. While many algorithms have been proposed for local community detection in homogeneous networks, there is a lack of an effective and efficient algorithm for local community detection in heterogeneous networks. In this paper, we present a novel algorithm framework that addresses this gap. Our framework combines the relevance measure HeteSim with a modified version of local modularity to identify the community to which the source node belongs. We conduct experiments on real data sets to verify the effectiveness and efficiency of our algorithm.