Clustering coefficients of large networks

Li, Yusheng; Shang, Yilun; Yang, Yiting

doi:10.1016/j.ins.2016.12.027

Cited by 54 publications

(32 citation statements)

References 40 publications

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“…Many large-scale networks have special forms of clustering coefficients, although the degree varies. Li et al [32] showed that the average clustering coefficient of the network with large degree accords with asymptotic expression. This provides a new research guide for our next research work.…”

Section: Comparison With Representation Learning Methodsmentioning

confidence: 89%

Link Prediction Based on Deep Convolutional Neural Network

Wang

Huang

et al. 2019

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In recent years, endless link prediction algorithms based on network representation learning have emerged. Network representation learning mainly constructs feature vectors by capturing the neighborhood structure information of network nodes for link prediction. However, this type of algorithm only focuses on learning topology information from the simple neighbor network node. For example, DeepWalk takes a random walk path as the neighborhood of nodes. In addition, such algorithms only take advantage of the potential features of nodes, but the explicit features of nodes play a good role in link prediction. In this paper, a link prediction method based on deep convolutional neural network is proposed. It constructs a model of the residual attention network to capture the link structure features from the sub-graph. Further study finds that the information flow transmission efficiency of the residual attention mechanism was not high, so a densely convolutional neural network model was proposed for link prediction. We evaluate our proposed method on four published data sets. The results show that our method is better than several other benchmark algorithms on link prediction.

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Section: Comparison With Representation Learning Methodsmentioning

confidence: 89%

Link Prediction Based on Deep Convolutional Neural Network

Wang

Huang

et al. 2019

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“…In addition, some research on complex social networks is also used in the field of social recommendation. In these studies, the features of the small world [27] and the clustering coefficients [28] in complex networks are exploited to mine the sociality among users and predict the preferences of target users. For instance, Liu G et al [29] designed an innovative heuristic path mining model based on multiple forecasts that utilized the influence of complex social networks to optimize the user social paths.…”

Section: Related Workmentioning

confidence: 99%

Social recommendation model based on user interaction in complex social networks

Liu

Ren

2019

PLoS ONE

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The user interaction in online social networks can not only reveal the social relationships among users in e-commerce systems, but also imply the social preferences of a target user for recommendation services. However, the current research has rarely explored the impact of social interaction on recommendation performance, especially now that recommender systems face increasing challenges and suffer from poor efficiency due to social data overload. Therefore, applied research on user interaction has become increasingly necessary in the field of social recommendation. In this paper, we develop a novel social recommendation method based on the user interaction in complex social networks, called the SRUI model, to present a basis for improving the efficiency of the recommender systems. Specifically, a weighted social interaction network is first mapped to represent the interactions among social users according to the gathered information about historical user behavior. Thereafter, the complete path set is mined by the complete path mining (CPM) algorithm to find social similar neighbors with tastes similar to those of the target user. Finally, the social similar tendencies of the users on the complete paths are obtained to predict the final ratings of items through the SRUI model. A series of experimental results based on two real public datasets show that our approach performs better than other state-of-the-art methods in terms of recommendation performance.

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“…A distinctive feature of several complex networks (including social ones) is an high density of 'triangles', namely triples of nodes any two of which are linked. Such a density is measured in terms of a [0,1]-ranged ratio by the so-called clustering coefficient, which is in fact the probability that by picking at random a vertex and two of its neighbors these latter are also neighbors of each other [20], [28]. A cluster score function can thus be conceived to measure the density of triangles locally, namely in the subgraphs spanned by vertex subsets.…”

Section: Weights and Common Neighborsmentioning

confidence: 99%

“…In network analysis cc(G) is a key indicator measuring 'transitivity', namely to what extent sharing some common neighbor entails being adjacent, for any two vertices. While in social networks the clustering coefficient is higher than in non-social ones [28], several complex networks display the same asymptotic clustering coefficient as certain strongly regular graphs, in contrast to small-world networks [6], [20]. Now consider the aim to assign scores v(A) to clusters A in a way such that higher values of the clustering coefficient cc(G(A)) over spanned subgraphs provide greater scores.…”

Section: B Score Of Pairs and Common Neighborsmentioning

confidence: 99%

Searching for Network Modules

Rossi

2019

Lecture Notes in Networks and Systems

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The kinds of data meaningfully represented as networks seem ever-increasing, from the social sciences to PPI networks in bioinformatics. When analyzing these networks a key target is to uncover their modular structure, which means searching for a family of modules, namely node subsets spanning each a subnetwork more densely connected than the average. Objective function-based graph clustering procedures rely on optimization techniques such as modularity maximization, and output a partition of nodes, i.e. a family of pair-wise disjoint subsets. But some nodes could be included in multiple modules, thus requiring an overlapping modular structure. The issue is mostly tackled by means of fuzzy clustering approaches, where each node may be included in different modules with different [0,1]-ranged memberships. This work proposes a novel type of objective function for graph clustering, in the form of a multilinear polynomial extension whose coefficients are determined by network topology. It may be thought of as a potential function, to be maximized, taking its values on fuzzy clusterings or families of fuzzy subsets of nodes over which every node distributes a unit membership. When suitably parameterized, this potential is shown to attain its maximum when every node concentrates its all unit membership on some module. The output thus remains a partition, but the original discrete optimization problem is turned into a continuous version allowing to conceive alternative search strategies. The instance of the problem being a pseudo-Boolean function assigning real-valued cluster scores to node subsets, modularity maximization is employed to exemplify a so-called quadratic form, in that the scores of singletons and pairs also fully determine the scores of larger clusters, while the resulting multilinear polynomial potential function has degree 2. After considering further quadratic instances, different from modularity and obtained by interpreting network topology in alternative manners, a greedy local-search strategy for the continuous framework is analytically compared with an existing greedy agglomerative procedure for the discrete case. Overlapping is finally discussed in terms of multiple runs, i.e. several local searches with different initializations.

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Clustering coefficients of large networks

Cited by 54 publications

References 40 publications

Link Prediction Based on Deep Convolutional Neural Network

Link Prediction Based on Deep Convolutional Neural Network

Social recommendation model based on user interaction in complex social networks

Searching for Network Modules

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