Network cluster detecting in associated bi-graph view

TL;DR

This paper introduces a novel method for network cluster detection by embedding associated bigraphs into a space, enabling effective identification of clusters with clear physical interpretation and acceptable computational complexity.

Contribution

The paper proposes a new approach to network clustering based on associated bigraph embedding, demonstrated on both synthetic and real-world networks.

Findings

Successfully identified clusters in computer-generated networks.

Correctly partitioned the Zachary Karate Club network.

Reasonably partitioned the Dolphin social network.

Abstract

We find there is relationship between the associated bigraph and the cluster (or community) detecting on network. By imbedding the associated bigraph of some network (suppose it has cluster structures) into some space, we can identify the clusters on this network, which is a new method for network cluster detecting. And this method, of which the physical meaning is clear and the time complexity is acceptable, may provide us a new point to understand the structure and character of networks. In this paper, We test the methods on serval computer-generated networks and real networks. A computer-generated network with 128 vertexes and the Zachary Network, which presents the structure of a karate club, can be partitioned correctly by these methods. And the Dolphin Network, which presents the relationship between 62 dolphins on the coast of New Zealand, is partitioned reasonably.