The similarity of weights on edges and discovering of community structure

Shen, Yi

doi:10.1016/j.physa.2013.08.063

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…Process of Our Method As a real example, we apply our method to a collaboration network of teachers in a university [20]. The network has 152 nodes and each node represents a research member.…”

Section: B a Collaboration Network To Show The Implementionmentioning

confidence: 99%

“…Newman defined weighted communities as groups of nodes where the weights on the edges are relatively larger than the external weights between them, and proposed a weighted modularity denoted w Q [18]. Recently, several algorithms have been proposed based on optimizing w Q [18], such as the WGN algorithm [18], the WEO algorithm [19], [20], the random walk-based method [21], etc. The WGN algorithm is generalized from its unweighted version GN algorithm [5] by calculating the weighted edge betweenness [18], and the WEO algorithm is generalized from its unweighted version EO algorithm [19] by using w Q [18].…”

Section: Introductionmentioning

confidence: 99%

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Community Detection in Weighted Networks via Recursive Edge-Filtration

Shen¹,

Liu²,

Xing³

2016

JCM

Self Cite

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In this paper, we present a Weighted Filtration Coefficient (WFC)  and a corresponding filtration method to detect the communities in weighted networks. In our method, a weighted network can be divided into groups by recursive filtration operations, and the dividing results are evaluated by the. We prove that optimization on local  enables us to obtain maximal global weighted modularity w Q , which corresponds to the correct communities. For a weighted network with m edges and c communities, the weighted communities can be detected in time ((1))  O c m , which is in linear scale time with the number of edges. Furthermore, the local weighted communities can be detected in an increasing order according to the edge weights between them. This division can reveal different levels of close connections between the nodes.

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Section: B a Collaboration Network To Show The Implementionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Community Detection in Weighted Networks via Recursive Edge-Filtration

Shen¹,

Liu²,

Xing³

2016

JCM

Self Cite

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“…The LFR networks have heterogeneous degree distributions and community sizes, and allow communities to overlap, which are in accordance with the features of real networks, and have been widely used for testing the community detection methods in recent years. [5,[23][24][25][26] The adjustable topology parameters for the LFR benchmark networks are network size N, average degrees k , maximum degrees k max , minimum and maximum community size c min and c max , the degree distributions γ, the community size distributions β , the mixing parameter µ, the number of communities each overlapping node belongs to o m , and the number of overlapping nodes o n . [22] In order to detect the overlapping communities and nodes and make comparisons with other wellknown fuzzy overlapping community detection methods, such as Fuzzyclust [27] and NMF, [17] we first investigate our method on the LFR networks without overlapping nodes to compare with the existing local methods in the detection of disjoint communities.…”

Section: Lfr Networkmentioning

confidence: 99%

A local fuzzy method based on “p-strong” community for detecting communities in networks

et al. 2016

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In this paper, we propose a local fuzzy method based on the idea of "p-strong" community to detect the disjoint and overlapping communities in networks. In the method, a refined agglomeration rule is designed for agglomerating nodes into local communities, and the overlapping nodes are detected based on the idea of making each community strong. We propose a contribution coefficient b c i v to measure the contribution of an overlapping node to each of its belonging communities, and the fuzzy coefficients of the overlapping node can be obtained by normalizing the b c i v to all its belonging communities. The running time of our method is analyzed and varies linearly with network size. We investigate our method on the computergenerated networks and real networks. The testing results indicate that the accuracy of our method in detecting disjoint communities is higher than those of the existing local methods and our method is efficient for detecting the overlapping nodes with fuzzy coefficients. Furthermore, the local optimizing scheme used in our method allows us to partly solve the resolution problem of the global modularity.

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“…In real society, the interactions between individuals are of different strengths for many complex networks, which are called weighted networks. [2,34] For instance, in the air traffic network, the weight of a link is measured by the number of passengers in the related flight. In the router-level of the Internet, the weights of links are generally correlated with the bandwidth of the physical connections or the cost for data transmission between routers.…”

Section: Introductionmentioning

confidence: 99%

Entropy-based link prediction in weighted networks

Sharafat

et al. 2017

Chinese Phys. B

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Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks. In the previous work (Xu et al, 2016 [31]), we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight, and propose a weighted prediction index based on the contributions of paths, namely Weighted Path Entropy (WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three typical weighted indices.

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The similarity of weights on edges and discovering of community structure

Cited by 7 publications

References 42 publications

Community Detection in Weighted Networks via Recursive Edge-Filtration

Community Detection in Weighted Networks via Recursive Edge-Filtration

A local fuzzy method based on “p-strong” community for detecting communities in networks

Entropy-based link prediction in weighted networks

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