Abstract
The preferential attachment mechanism that forms scale-free network cannot display assortativity, i.e., the degree of one node is positively correlated with that of their neighbors in the network. Given the attributes of network nodes, a cultural trait-matching mechanism is further introduced in this paper. Both theoretical analysis and simulation results indicate that the higher selection probability of such mechanism, the more obvious the assortativity is shown in networks. Further, the degree of nodes presents a positive logarithm correlation with that of adjacent ones. Finally, this study discusses the theoretical and practical significances of the introduction of such a cultural trait-matching mechanism.
Highlights
Erefore, a lot of scholars have investigated the issues as to the formation and characteristics of assortativity [5, 9,10,11,12,13,14,15,16,17,18,19]
They proved that since a social network could be divided into communities, community structure in turn resulted in positive correlation of node degree
Ey argued that the assortativity of social networks, which could not be found in nonsocial networks, may be attributable to community structure
Summary
One is the preferential attachment mechanism [31], in which individuals mainly focus on the degree of nodes being connected To be concrete, they are more concerned about the influences of target connection nodes, rather than whether or not the cultural traits of the target node match theirs. E other is the cultural trait-matching mechanism With this mechanism, individuals are more likely to connect with nodes bearing higher matching degree with their cultural features [5, 25, 27]. (3) e new node selects a preferential attachment mechanism with the probability p and adopts a cultural trait-matching connection mechanism according to probability 1 − p. If the new node chooses a preferential attachment mechanism, it is connected to the existing node i with the probability in equation (1), where j denotes arbitrary node in the network except the newly introduced one [31]. (4) Repeat steps (2) and (3) until the network scale reaches N
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