Effectively Detecting Communities by Adjusting Initial Structure via Cores

Mei Chen,Mingwei Leng,Mei Zhang,Zhichong Yang,Ming Li,Xiaofang Wen

doi:10.1155/2019/9764341

Mei Chen, Mingwei Leng + Show 4 more

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https://doi.org/10.1155/2019/9764341

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Abstract

Community detection is helpful to understand useful information in real-world networks by uncovering their natural structures. In this paper, we propose a simple but effective community detection algorithm, called ACC, which needs no heuristic search but has near-linear time complexity. ACC defines a novel similarity which is different from most common similarity definitions by considering not only common neighbors of two adjacent nodes but also their mutual exclusive degree. According to this similarity, ACC groups nodes together to obtain the initial community structure in the first step. In the second step, ACC adjusts the initial community structure according to cores discovered through a new local density which is defined as the influence of a node on its neighbors. The third step expands communities to yield the final community structure. To comprehensively demonstrate the performance of ACC, we compare it with seven representative state-of-the-art community detection algorithms, on small size networks with ground-truth community structures and relatively big-size networks without ground-truth community structures. Experimental results show that ACC outperforms the seven compared algorithms in most cases.

Highlights

Core-based methods [14,15,16] play an important role in community detection
The similarity proposed in ACC considers the number of common neighbors and the exclusion degree between two adjacent nodes. en, based on another expression of the assumption that connections of the nodes in the same community are dense while connections of the nodes in different communities are sparse, ACC regards a node with the max local density in an initial community as the core of that community and adjusts the initial communities according to cores
To demonstrate that ACC can be applied to networks containing different communities, the results of all the algorithms on the networks with ground-truth community structures are exhibited in Figures 1–4, and 6, where colors of nodes indicate different detected communities. e quantitative performances of ACC and its baselines are summarized in Table 2, which are shown at the bottom of the corresponding figures respectively to give visual explanations

Summary

Introduction

Core-based methods [14,15,16] play an important role in community detection. Different from heuristic methods with certain criterion, we propose a simple, yet effective and fast, similarity-based community detection algorithm named ACC (Adjusting initial Community structure via Cores). The similarity proposed in ACC considers the number of common neighbors and the exclusion degree between two adjacent nodes. (1) We present a novel similarity which considers common neighbors between two adjacent nodes and their mutual exclusive degree. (2) We define the influence of a node on its neighbors as its local density, which makes discovering the core in a community much easier. (3) We propose a new community detection algorithm ACC with near-linear time complexity, which can find high-quality communities in different networks.

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