Mutual selection model for weighted networks

Abstract

For most networks, the connection between two nodes is the result of their mutual affinity and attachment. In this paper, we propose a mutual selection model to characterize the weighted networks. By introducing a general mechanism of mutual selection, the model can produce powerlaw distributions of degree, weight and strength, as confirmed in many real networks. Moreover, we also obtained the nontrivial clustering coefficient C, degree assortativity coefficient r and degreestrength correlation, depending on a model parameter m. These results are supported by present empirical evidences. Studying the degree-dependent average clustering coefficient C(k) and the degree-dependent average nearest neighbors' degree knn(k) also provide us with a better description of the hierarchies and organizational architecture of weighted networks.

Collective iteration behavior for online social networks

“…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.…”

“…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.…”

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

“…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.…”

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

“…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.…”