Generalized scale-free homophilic network

Abstract

We propose a simple network growth process where the preferential attachment contains two essential parameters: homophily, namely, the tendency of sites to link with similar ones, and the number of attaching neighbors. It jointly generalizes the Barab asi-Albert model and the scalefree homophilic model with a control parameter which tunes the importance of the homophily on preferential attachment process. Our results support a detailed discussion about di®erent kinds of correlation, in special a¯tness correlation introduced in this paper, and comparisons between BA model, scale-free homophilic model, and our present model considering its topological properties: degree distribution, time dependence of the connectivity and clustering coe±cient.

Critical properties of the SIS model on the clustered homophilic network

“…From the scaling relations, we obtained that , and . It is worth stressing out that these values are quite different from those reported for the contact process in an unclustered homophilic network [7] , as well as from those predicted by quenched mean-field (QMF) theory considering scale-free networks characterized by , with the same degree exponent [26] .…”

“…The homophilic network is a complex network introduced in 2013 by Almeida et al. [6] , [7] , who modified the Barabàsi–Albert (BA) network model [8] , introducing a similarity factor influencing the connection occurrence. While in the Barabàsi–Albert model the links between sites depend only on the connectivity, in the homophilic one we have the similitude factor.…”

Critical properties of the contact process on a scale-free homophilic network

“…The homophilic network model was first introduced by de Almeida et al [24,25]. Its main feature is a modification in the attachment rule of the Barabàsi-Albert model, allowing comparative studies among network models where homophily presents a substantial eect on their behavior due to dierent sites skill to compete for links.…”

Research on Reliability Measurement Model and Evaluation Method of Distributed Storage Network

“…When mass tasks are uploaded and transported in the network, the network topology or the unreasonable distribution of nodes makes the access efficiency of the whole network low and easily leads to the network congestion [1]. In order to effectively analyse and evaluate the robustness of distributed storage networks and find out the core nodes in the network, many scholars [2][3] abstract the network topology into undirected graphs. However, the network performance based on undirected graph analysis has a certain deviation due to the different tasks in the real storage network, and the upstream and downlink speed is not consistent.…”