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Qingyu Zou
Jilin University

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Identifying Overlapping Communities in Directed Networks via Triangles Qingyu Zou; Fu Liu; Tao Hou; Yihan Jiang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 10: October 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

A lot of complex systems in nature and society can be represented as the form of network. The small-scale subnets topological features are vital to understand the dynamics and function of the networks. Triangles comprised of three nodes are the simplest subnet in the network. Based on the triangle distribution of the complex network, we present a novel approach to detect overlapping community structure in directed networks. Different from previous studies focused on grouping nodes, our method defines communities as groups of links rather than nodes so that nodes naturally belong to more than one community. It can identify a suitable number of overlapping communities without any prior knowledge about the community. We evaluated our approach on several real-networks. Experimental results prove that the algorithm proposed is efficient for detecting overlapping communities in directed networks. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3401 
A Topology-Based Algorithm for Directed Network Alignment Qingyu Zou; Fu Liu; Tao Hou; Yihan Jiang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 10: October 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Network alignment has brought significant advances to our understanding of complex networks, for example, the Worldwide Web, biological networks, and social networks. Triangles comprised of three nodes are the simplest subnet in the directed network. The distribution of triangles is an important indicator of understanding the dynamics and function of directed networks. In this paper, we present a novel alignment algorithm for directed networks only based on topology structure, which can be used for any two networks. The transcriptional regulatory networks (TRNs) of E. coli and S. cerevisiae are used to evaluate the algorithm. Experimental results demonstrate that the algorithm proposed is efficient for aligning directed networks.DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.2951
Overlapping Communities Detection Based on Link Partition in Directed Networks Qingyu Zou; Fu Liu; Tao Hou; Yihan Jiang
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 9: September 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Many complex systems can be described as networks to comprehend both the structure and the function. Community structure is one of the most important properties of complex networks. Detecting overlapping communities in networks have been more attention in recent years, but the most of approaches to this problem have been applied to the undirected networks. This paper presents a novel approach based on link partition to detect overlapping communities structure in directed networks. In contrast to previous researches focused on grouping nodes, our algorithm defines communities as groups of directed links rather than nodes with the purpose of nodes naturally belong to more than one community. This approach can identify a suitable number of overlapping communities without any prior knowledge about the community in directed networks. We evaluate our algorithm on a simple artificial network and several real-networks. Experimental results demonstrate that the algorithm proposed is efficient for detecting overlapping communities in directed networks. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3304
Co-Authors Fu Liu Tao Hou Yihan Jiang