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Label propagation with α-degree neighborhood impact for network community detection.

Sun H, Huang J, Zhong X, Liu K, Zou J, Song Q - Comput Intell Neurosci (2014)

Bottom Line: In this paper, a novel label propagation approach with α-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks.Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately.The performance of our method is better than other label propagation based methods.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China ; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.

ABSTRACT
Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach with α-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of its α-degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order on the α-degree neighborhood impact of all the nodes. The α-degree neighborhood impact is also taken as the updating weight value, where the parameter impact scope α can be set to a positive integer. Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately. The performance of our method is better than other label propagation based methods.

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A sample network.
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fig1: A sample network.

Mentions: As shown in Figure 1, nodes 2–6, which are the neighbors of node 1 and are called its 1-degree neighborhood nodes, form the 1-degree neighborhood network of node 1 with all the incident edges of those nodes. Node 7 is a 2-degree neighbor of node 1, and the spanning subgraph composed of nodes 1–7 is a 2-degree neighborhood network of node 1. In general, we can view an α-degree network as a complete closed system constituted by an initiating center node and its surrounding counterparts and their incident edges. In this system, starting from a certain node u, we measure and analyze its local connection density via its α-degree neighbors and neighborhood network to yield the average degree of impact on all its surrounding nodes.


Label propagation with α-degree neighborhood impact for network community detection.

Sun H, Huang J, Zhong X, Liu K, Zou J, Song Q - Comput Intell Neurosci (2014)

A sample network.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4265519&req=5

fig1: A sample network.
Mentions: As shown in Figure 1, nodes 2–6, which are the neighbors of node 1 and are called its 1-degree neighborhood nodes, form the 1-degree neighborhood network of node 1 with all the incident edges of those nodes. Node 7 is a 2-degree neighbor of node 1, and the spanning subgraph composed of nodes 1–7 is a 2-degree neighborhood network of node 1. In general, we can view an α-degree network as a complete closed system constituted by an initiating center node and its surrounding counterparts and their incident edges. In this system, starting from a certain node u, we measure and analyze its local connection density via its α-degree neighbors and neighborhood network to yield the average degree of impact on all its surrounding nodes.

Bottom Line: In this paper, a novel label propagation approach with α-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks.Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately.The performance of our method is better than other label propagation based methods.

View Article: PubMed Central - PubMed

Affiliation: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China ; State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China.

ABSTRACT
Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach with α-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of its α-degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order on the α-degree neighborhood impact of all the nodes. The α-degree neighborhood impact is also taken as the updating weight value, where the parameter impact scope α can be set to a positive integer. Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately. The performance of our method is better than other label propagation based methods.

Show MeSH