Detection of Community Structures in Networks with Nodal Features based on Generative Probabilistic Approach
This addresses the problem of more accurate community detection in social, ecological, and e-trading networks by leveraging node features, representing an incremental improvement over methods based solely on connectivity.
The paper tackles community detection in networks by incorporating both network structure and node features, proposing a novel probabilistic graphical model that learns relevant features without prior assumptions and determines their influence, achieving effectiveness over state-of-the-art algorithms on synthetic and benchmark networks.
Community detection is considered as a fundamental task in analyzing social networks. Even though many techniques have been proposed for community detection, most of them are based exclusively on the connectivity structures. However, there are node features in real networks, such as gender types in social networks, feeding behavior in ecological networks, and location on e-trading networks, that can be further leveraged with the network structure to attain more accurate community detection methods. We propose a novel probabilistic graphical model to detect communities by taking into account both network structure and nodes' features. The proposed approach learns the relevant features of communities through a generative probabilistic model without any prior assumption on the communities. Furthermore, the model is capable of determining the strength of node features and structural elements of the networks on shaping the communities. The effectiveness of the proposed approach over the state-of-the-art algorithms is revealed on synthetic and benchmark networks.