Locating Structural Centers: A Density-Based Clustering Method for Community Detection

<div><p>Uncovering underlying community structures in complex networks has received considerable attention because of its importance in understanding structural attributes and group characteristics of networks. The algorithmic identification of such structures is a significant challenge. Local expanding methods have proven to be efficient and effective in community detection, but most methods are sensitive to initial seeds and built-in parameters. In this paper, we present a local expansion method by density-based clustering, which aims to uncover the intrinsic network communities by locating the structural centers of communities based on a proposed structural centrality. The structural centrality takes into account local density of nodes and relative distance between nodes. The proposed algorithm expands a community from the structural center to the border with a single local search procedure. The local expanding procedure follows a heuristic strategy as allowing it to find complete community structures. Moreover, it can identify different node roles (cores and outliers) in communities by defining a border region. The experiments involve both on real-world and artificial networks, and give a comparison view to evaluate the proposed method. The result of these experiments shows that the proposed method performs more efficiently with a comparative clustering performance than current state of the art methods.</p></div

A schematic example to illustrate the idea of our method.

<p>(A)The Zachary’s karate club network with two clusters. (B)The decision graph for the nodes in the network.</p

Comparison on GN benchmarks.

<p>The NMI value is averaged over 10 networks generated with the same parameters.</p

Comparison of different community detection algorithms on LFR benchmark networks with N = 10,000.

<p>(A) Benchmark networks with communities of small size. (B) Benchmark networks with communities of big size.</p

Location of structural centers on synthetic LFR network with 1,000 nodes and 9 ground-truth communities.

<p>(A)The node distribution in the decision graph. (B)The plot of structural centrality sorted in decreasing order as a function of node number for the network.</p

Comparison of security parameters.

<p>Comparison of security parameters.</p

Communication cost analysis.

<p>Communication cost analysis.</p

Proposed scheme.

<p>Proposed scheme.</p

Computational cost analysis.

<p>Computational cost analysis.</p

An illustration of node assignment with an abstract graph.

<p>An illustration of node assignment with an abstract graph.</p