A General Definition of Network Communities and the Corresponding Detection Algorithm

TL;DR

This paper introduces a unified, general framework for defining and detecting communities in various network types, simplifying the process across different fields and applications.

Contribution

It proposes a universal community detection algorithm applicable to multiple network models, reducing the need for ad hoc solutions for specific problems.

Findings

Unified graph model for transmission and similarity networks

General community detection algorithm adaptable to different network types

Demonstration of algorithm effectiveness in practical scenarios

Abstract

Network structures, consisting of nodes and edges, have applications in almost all subjects. A set of nodes is called a community if the nodes have strong interrelations. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper and accurate services. However, all the current detection algorithms are motivated by the practical problems, whose applicabilities in other fields are open to question. Thence, for a new community problem, researchers need to derive algorithms ad hoc, which is arduous and even unnecessary. In this paper, we represent a general procedure to find community structures in practice. We mainly focus on two typical types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a general method to define and detect communities. Readers can specialize our general algorithm to accommodate their problems. In the end, we also give a demonstration to show how the algorithm works.