Detection of Community Structures in Networks with Nodal Features based on Generative Probabilistic Approach

TL;DR

This paper introduces a probabilistic graphical model that combines network structure and node features to improve community detection accuracy in social and ecological networks.

Contribution

It presents a novel generative probabilistic approach that leverages both node features and connectivity without prior assumptions, enhancing community detection.

Findings

Outperforms existing algorithms on synthetic networks

Effective in real benchmark networks

Learns the influence of features and structure on communities

Abstract

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.