Empirical study on clique-degree distribution of networks

Xiao, Wei-Ke; Ren, Jie; Qi, Feng; Song, Zhiwei; Zhu, Muhua; Yang, Hongfeng; Jin, Hu; Wang, Bing–Hong; Zhou, Tao

doi:10.1103/physreve.76.037102

Cited by 21 publications

(11 citation statements)

References 33 publications

(33 reference statements)

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“…Figure 8 The relatively high clustering level detected in the WTW hints to a network architecture that, especially in the binary case, features a peculiar clique structure. To further explore the clique structure of the WTW we computed, in the binary case, the node k-clique degree (NkCD) statistic [62]. The NkCD for node i is defined as the number of k-size fully connected subgraphs containing i.…”

Section: A Shape Moments and Correlation Structure Of Network Statmentioning

confidence: 99%

World-trade web: Topological properties, dynamics, and evolution

2009

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This paper studies the statistical properties of the web of import-export relationships among world countries using a weighted-network approach. We analyze how the distributions of the most important network statistics measuring connectivity, assortativity, clustering, and centrality have coevolved over time. We show that all node-statistic distributions and their correlation structure have remained surprisingly stable in the last 20 years -and are likely to do so in the future. Conversely, the distribution of (positive) link weights is slowly moving from a log-normal density towards a power law. We also characterize the autoregressive properties of network-statistics dynamics. We find that network-statistics growth rates are well-proxied by fat-tailed densities like the Laplace or the asymmetric exponential power. Finally, we find that all our results are reasonably robust to a few alternative, economically meaningful, weighting schemes.

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Section: A Shape Moments and Correlation Structure Of Network Statmentioning

confidence: 99%

World-trade web: Topological properties, dynamics, and evolution

2009

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“…As shown by Liben-Nowell et al [49] and Zhou et al [47], the common neighbor index is a good candidate to account for the topological effects. In addition, Cui et al [138] developed an evolving model driven completely by the common neighborhood, which well reproduces not only the global network properties, but also the local structural features like power-law clique-degree distributions [139] of social and technological networks. Therefore, we simply use the common neighbor index S CN (see Eq.…”

Section: Evaluation Of Network Evolving Mechanismsmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

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2,331

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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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“…ZHOU indicates that the higher order in clique degree no longer has the strict power-law distribution in his paper [7] . According to the theory of six degrees of separation, people can find anyone through six sides in human social networks.…”

Section: Comprehensive Weighted Clique Degree Algorithmmentioning

confidence: 99%

“…These typical nodes ranking algorithms have their own defect. ZHOU proposes the concept of clique degree in his paper [7] . This concept considers the close degree between neighbor nodes, and provides a new way for nodes ranking.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Weighted Clique Degree Ranking Algorithms and Evolutionary Model of Complex Network

Liu

2016

MATEC Web Conf.

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Abstract. This paper analyses the degree ranking (DR) algorithm, and proposes a new comprehensive weighted clique degree ranking (CWCDR) algorithms for ranking importance of nodes in complex network. Simulation results show that CWCDR algorithms not only can overcome the limitation of degree ranking algorithm, but also can find important nodes in complex networks more precisely and effectively. To the shortage of small-world model and BA model, this paper proposes an evolutionary model of complex network based on CWCDR algorithms, named CWCDR model. Simulation results show that the CWCDR model accords with power-law distribution. And compare with the BA model, this model has better average shortest path length, and clustering coefficient. Therefore, the CWCDR model is more consistent with the real network.

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Empirical study on clique-degree distribution of networks

Cited by 21 publications

References 33 publications

World-trade web: Topological properties, dynamics, and evolution

World-trade web: Topological properties, dynamics, and evolution

Link prediction in complex networks: A survey

Comprehensive Weighted Clique Degree Ranking Algorithms and Evolutionary Model of Complex Network

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