Generalizations of the clustering coefficient to weighted complex networks

Saramäki, Jari; Kivelä, Mikko; Onnela, Jukka‐Pekka; Kaski, Kimmo; Kertész, János

doi:10.1103/physreve.75.027105

Cited by 596 publications

(436 citation statements)

References 17 publications

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“…C W (B) being close the C yields to two conclusions : (1) the absence of correlation (randomized network), (2) the network is divided in two sets, one where triples are constituted by larger weights and others by smaller weights. C W (O) being significantly lower is due to the weight normalization by the global max(w) and to a broad distribution of weights in networks [27]. The percentage of species with a betweenness centrality equal to 0 differed between the binary and the weighted niche-overlap graphs (Fig.…”

Section: Resultsmentioning

confidence: 99%

Structure Comparison of Binary and Weighted Niche-Overlap Graphs

et al. 2014

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Abstract. In ecological networks, niche-overlap graphs are considered as complex systems. They represent the competition between two predators that share common resources. The purpose of this paper is to investigate the structural properties of these graphs considered as weighted networks and compare their measures with the ones calculated for the binary networks. To conduct this study, we select four classical network measures : the degree of nodes, the clustering coefficient, the assortativity, and the betweenness centrality. These measures were used to analyse different type of networks such as social networks, biological networks, world wide web, etc. Interestingly, we identify significant differences between the structure of the binary and the weighted niche-overlap graphs. This study indicates that weight information reveals different features that may provide other implications on the dynamics of these networks.

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Section: Resultsmentioning

confidence: 99%

Structure Comparison of Binary and Weighted Niche-Overlap Graphs

et al. 2014

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“…R. Soc. B 281: 20141195 taken into account [54]; the other is the betweenness centrality, a measure of the number of shortest paths going through an agent [55], reflecting its importance in the social network.…”

Section: Resultsmentioning

confidence: 99%

Effects of deception in social networks

et al. 2014

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Honesty plays a crucial role in any situation where organisms exchange information or resources. Dishonesty can thus be expected to have damaging effects on social coherence if agents cannot trust the information or goods they receive. However, a distinction is often drawn between prosocial lies ('white' lies) and antisocial lying (i.e. deception for personal gain), with the former being considered much less destructive than the latter. We use an agentbased model to show that antisocial lying causes social networks to become increasingly fragmented. Antisocial dishonesty thus places strong constraints on the size and cohesion of social communities, providing a major hurdle that organisms have to overcome (e.g. by evolving counter-deception strategies) in order to evolve large, socially cohesive communities. In contrast, white lies can prove to be beneficial in smoothing the flow of interactions and facilitating a larger, more integrated network. Our results demonstrate that these grouplevel effects can arise as emergent properties of interactions at the dyadic level. The balance between prosocial and antisocial lies may set constraints on the structure of social networks, and hence the shape of society as a whole.

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“…Various kinds of average shortest path lengths and average clustering coefficients have been proposed for weighted networks [18,19]. The measures used in this research are as follows:…”

Section: Complex Network Analysismentioning

confidence: 99%

Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data

Deng

et al. 2016

Sustainability

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Spring Festival travel rush is a phenomenon in China that population travel intensively surges in a short time around Chinese Spring Festival. This phenomenon, which is a special one in the urbanization process of China, brings a large traffic burden and various kinds of social problems, thereby causing widespread public concern. This study investigates the spatial-temporal characteristics of Spring Festival travel rush in 2015 through time series analysis and complex network analysis based on multisource big travel data derived from Baidu, Tencent, and Qihoo. The main results are as follows: First, big travel data of Baidu and Tencent obtained from location-based services might be more accurate and scientific than that of Qihoo. Second, two travel peaks appeared at five days before and six days after the Spring Festival, respectively, and the travel valley appeared on the Spring Festival. The Spring Festival travel network at the provincial scale did not have small-world and scale-free characteristics. Instead, the travel network showed a multicenter characteristic and a significant geographic clustering characteristic. Moreover, some travel path chains played a leading role in the network. Third, economic and social factors had more influence on the travel network than geographical location factors. The problem of Spring Festival travel rush will not be effectively improved in a short time because of the unbalanced urban-rural development and the unbalanced regional development. However, the development of the modern high-speed transport system and the modern information and communication technology can alleviate problems brought by Spring Festival travel rush. We suggest that a unified real-time traffic platform for Spring Festival travel rush should be established through the government's integration of mobile big data and the official authority data of the transportation department.

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Generalizations of the clustering coefficient to weighted complex networks

Cited by 596 publications

References 17 publications

Structure Comparison of Binary and Weighted Niche-Overlap Graphs

Structure Comparison of Binary and Weighted Niche-Overlap Graphs

Effects of deception in social networks

Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data

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