Clustering coefficient and community structure of bipartite networks

Zhang, Peng; Wang, Jinliang; Li, Xiaojia; Li, Menghui; Di, Zengru; Fan, Ying

doi:10.1016/j.physa.2008.09.006

Cited by 173 publications

(96 citation statements)

References 30 publications

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“…In bipartite graphs, this local density usually tries to capture how the neighbourhoods of nodes tend to overlap each other. Some definitions have been already proposed to study this property in bipartite graphs [19], [10], [20], [21], [22]. In the present work, we rely on two of those metrics to cope with the local density : the bipartite clustering coefficient and the redundancy coefficient [10].…”

Section: B Metrics For Bipartite Graphsmentioning

confidence: 99%

Revealing intricate properties of communities in the bipartite structure of online social networks

Tackx

Guillaume

Tarissan

2015

2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS)

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To cite this version:Raphaël Tackx, Jean-Loup Guillaume, Fabien Tarissan. Revealing intricate properties of communities in the bipartite structure of online social networks. Abstract-Many real-world networks based on human activities exhibit a bipartite structure. Although bipartite graphs seem appropriate to analyse and model their properties, it has been shown that standard metrics fail to reproduce intricate patterns observed in real networks. In particular, the overlapping of the neighbourhood of communities is difficult to capture precisely. In this work, we tackle this issue by analysing the structure of 4 real-world networks coming from online social activities. We first analyse their structure using standard metrics. Surprisingly, the clustering coefficient turns out to be less relevant than the redundancy coefficient to account for overlapping patterns. We then propose new metrics, namely the dispersion and the monopoly coefficients, and show that they help refining the study of bipartite overlaps. Finally, we compare the results obtained on real networks with the ones obtained on random bipartite models. This shows that the patterns captured by the redundancy and the dispersion coefficients are strongly related to the real nature of the observed overlaps.

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Section: B Metrics For Bipartite Graphsmentioning

confidence: 99%

Revealing intricate properties of communities in the bipartite structure of online social networks

Tackx

Guillaume

Tarissan

2015

2015 IEEE 9th International Conference on Research Challenges in Information Science (RCIS)

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“…However, the inter-similarity (i.e. the similarity between nodes of different kinds) in bipartite networks is not yet well-defined [15,16]. In this letter, we employ a well-known hybrid diffusion process from recommender system to calculate inter-similarities in bipartite networks.…”

Section: E-mail Addressesmentioning

confidence: 99%

Identifying missing and spurious connections via the bi-directional diffusion on bipartite networks

2014

Self Cite

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Link prediction and spurious link detection in complex networks have attracted increasing attention from both physical and computer science communities, due to their wide applications in many real systems. Related previous works mainly focus on monopartite networks while these problems in bipartite networks are not yet systematically addressed. Containing two different kinds of nodes, bipartite networks are essentially different from monopartite networks, especially in node similarity calculation: the similarity between nodes of different kinds (called inter-similarity) is not well defined. In this letter, we employ the local diffusion processes to measure the inter-similarity in bipartite networks. We find that the inter-similarity is asymmetric if the diffusion is applied in different directions. Accordingly, we propose a bi-directional hybrid diffusion method which is shown to achieve higher accuracy than the existing diffusion methods in identifying missing and spurious links in bipartite networks.Link prediction and spurious link detection problems are longstanding challenges in network study [1]. They have applications in many fields such as chemistry, biology, sociology and computer science. In many biological networks, such as food webs, proteinprotein interaction networks and metabolic networks, whether a link between two nodes exists must be demonstrated by field or laboratorial experiments, which is usually very costly [2]. In addition, the data in constructing biological and social networks may contain inaccurate information, resulting in spurious links [3]. Correcting the network connections can be very expensive if it is done by laboratorial experiments. To solve this problem, many networkbased methods have been developed and they are shown to have high accuracy in identifying missing and spurious links [4].So far, most related works focus on monopartite networks in which only one type of nodes exists [5]. However, many systems coupled by two different building blocks should be modeled by bipartite networks. For example, the e-commercial systems consisting of online users and items [6], the scientific collaboration system consisting of authors and papers [7], family name inheritance system consisting of babies and names [8] are naturally described by such networks. The link prediction and spurious link detection problems are not yet well addressed in bipartite networks. One close problem is the so-called "network-based * Corresponding authors.

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“…Here we attempt to address the scalability issue of two-mode clustering algorithm by applying parallel genetic algorithm [6] in Cuthill-McKee matrix bandwidth optimization problem [5]. Two-mode clustering has application in several fields, such as metabolomics data analysis [6], multiple trait data stemming [13], community detection in social networks [14], marketing applications [15], graph co-clustering for ontology mapping [16], and many other applications mentioned in [17] such as dimensionality reduction (also known as subspace clustering) in large databases, and collaborative filtering for recommendation system / target marketing (where the data set rows can be customers and columns can be movies).…”

Section: Literature Surveymentioning

confidence: 99%

Scalability of Parallel Genetic Algorithm for Two-mode Clustering

Deb¹,

Srirama²

2014

IJCA

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Data matrix having the same set of entity in the rows and cloumns is known as one-mode data matrix, and traditional one-mode clustering algorithms can be used to cluster the rows (or columns) separately. With the popularity of use of two-mode data matrices where the rows and columns have different sets of entities, the need for simultaneous clustering of rows and columns popularly known as two-mode clustering increased. Additionally, the emergence of large data sets and the prediction of Moore's law slow-down have created the challenge of clustering scalability. In this paper, we address the problem of scalability of organizing an unlabelled twomode dataset into clusters utilizing multicore processor. We propose a parallel genetic algorithm (GA) heuristics based two-mode clustering algorithm, which is an adaptation of the classical Cuthill-McKee Matrix Bandwidth Minimization (MBM) algorithm. The classical MBM method aims at reducing the bandwidth of a sparse symmetric matrix, which we adapted to make it suitable for non-symmetric real-valued matrix. Preliminary results indicate that our algorithm is scalable on multicore processor compared to serial implementation. Future work will include more extensive experiments and evaluations of the system. General TermsMulticore

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Clustering coefficient and community structure of bipartite networks

Cited by 173 publications

References 30 publications

Revealing intricate properties of communities in the bipartite structure of online social networks

Revealing intricate properties of communities in the bipartite structure of online social networks

Identifying missing and spurious connections via the bi-directional diffusion on bipartite networks

Scalability of Parallel Genetic Algorithm for Two-mode Clustering

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