Mutual selection model for weighted networks

Wang, Wen-Xu; Hu, Bo; Zhou, Tao; Wang, Bing–Hong; Xie, Yi

doi:10.1103/physreve.72.046140

Cited by 64 publications

(31 citation statements)

References 26 publications

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“…We only consider one possible preferential interaction, not comparing with the models such as random-walk-based model [47,48] and structural-based network model [41,42], and the properties of statistical physics still need to be deeply discussed. Besides, it also should be examined the effect of Dunbar number to social signature.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Moreover, the mechanisms of human interactions can be characterized by the weighted networks. Several models like the mutual selection model [41], generalized local-world models [42] provide the insights of the structure of interactions. Notably, different with the traditional statistical properties, social signature found in mobile phone [6] could be a general pattern which may be suitable for OSNs, but the statistical patterns and generation mechanism are still little known.…”

Section: Introductionmentioning

confidence: 99%

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Collective iteration behavior for online social networks

Liu

Guo

et al. 2018

Physica A: Statistical Mechanics and its Applications

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h i g h l i g h t s• There are robust social signatures of interactions no matter how friend changes over time.• The model shows that new users intend to look for the old users with strong ties while old users have tendency to interact with new friends.• The presented model can reproduce the heterogeneity of social signature by adjusting the number of communicating targets and the max number of interactions. Keywords: Social signature Ego networks Preferential interactionUnderstanding the patterns of collective behavior in online social network (OSNs) is critical to expanding the knowledge of human behavior and tie relationship. In this paper, we investigate a specific pattern called social signature in Facebook and Wiki users' online communication behaviors, capturing the distribution of frequency of interactions between different alters over time in the ego network. The empirical results show that there are robust social signatures of interactions no matter how friends change over time, which indicates that a stable commutation pattern exists in online communication. By comparing a random null model, we find the that commutation pattern is heterogeneous between ego and alters. Furthermore, in order to regenerate the pattern of the social signature, we present a preferential interaction model, which assumes that new users intend to look for the old users with strong ties while old users have tendency to interact with new friends. The experimental results show that the presented model can reproduce the heterogeneity of social signature by adjusting 2 parameters, the number of communicating targets m and the max number of interactions n, for Facebook users, m = n = 5, for Wiki users, m = 2 and n = 8. This work helps in deeply understanding the regularity of social signature.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collective iteration behavior for online social networks

Liu

Guo

et al. 2018

Physica A: Statistical Mechanics and its Applications

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“…The most popular explanation of scale-free networks is preferential attachment: A newly created node is connected to a pre-existing one with a probability proportional to the number of edges of the target node. Other models include connecting nearest-neighbor model and mutual selection model [18], both of which explain the emergence of scale-free networks. In the latter model, a new edge is added based on mutual affinity between nodes, which can be considered as a simple multi-agent system.…”

Section: Related Workmentioning

confidence: 99%

Emergence of global network property based on multi-agent voting model

Shinoda

Matsuo

Nakashima

2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

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Recent studies have shown that various models can explain the emergence of complex networks, such as scale-free and small-world networks. This paper presents a different model to generate complex networks using a multi-agent approach. Each node is considered as an agent. Based on voting by all agents, edges are added repeatedly. We use four different kinds of centrality measures as a utility functions for agents. Depending on the centrality measure, the resultant networks differ considerably: typically closeness centrality generates a scale-free network, degree centrality produces a random graph, betweenness centrality favors a regular graph, and eigenvector centrality brings a complete subgraph. The importance of the network structure among agents is widely noted in the multi-agent research literature. This paper contributes new insights into the connection between agents' local behavior and the global property of the network structure. We describe a detailed analysis on why these structures emerge, and present a discussion of the possible expansion and application of the model.

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“…Although BA model can generate the power-law degree distributions, its assortative coefficient r equals to zero in the limit of large size thus fail to reproduce the disassortative property that extensively exists in the real-world networks. Recently, some models that can generate either assortative or disassortative networks have been reported [30,31,32,33,34,35]. Wang et al presented a mutual attraction model for both assortative and disassortative weighted networks.…”

Section: Introductionmentioning

confidence: 99%

Growing scale-free small-world networks with tunable assortative coefficient

Guo

Zhou

Liu

et al. 2006

Physica A: Statistical Mechanics and its Applications

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In this paper, we propose a simple rule that generates scale-free small-world networks with tunable assortative coefficient. These networks are constructed by twostage adding process for each new node. The model can reproduce scale-free degree distributions and small-world effect. The simulation results are consistent with the theoretical predictions approximately. Interestingly, we obtain the nontrivial clustering coefficient C and tunable degree assortativity r by adjusting the parameter: the preferential exponent β. The model can unify the characterization of both assortative and disassortative networks.

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Mutual selection model for weighted networks

Cited by 64 publications

References 26 publications

Collective iteration behavior for online social networks

Collective iteration behavior for online social networks

Emergence of global network property based on multi-agent voting model

Growing scale-free small-world networks with tunable assortative coefficient

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