Abstract
How to accurately characterize similarities of entities is the basis of detecting virtual community structure of an Internet social network. This paper proposes a supernetwork based approach of quantitative similarity evaluation among entities with two indices of friend relation and interest similarity. The supernetwork theory is firstly introduced to model the complex relationship of online social network entities by integrating three basic networks: entity, action, and interest and establishing three kinds of mappings: from entity to action, from action to interest, and from entity to interest, that is, one hidden relation mined through the transfer characteristic of visible mappings. And further similarity degree between two entities is calculated by weighting the values of two indices: friend relation and interest similarity. Experiments show that this model not only can provide a more realistic relation of individual users within an Internet social network, but also, build a weighted social network, that is, a graph in which user entities are vertices and similarities are edges, on which the values record their similarity strength relative to one another.
Highlights
Many systems can be represented as complex networks or graphs, that is, collections of vertices joined in pairs by edges
The supernetwork theory is firstly introduced to model the complex relationship of online social network entities by integrating three basic networks: entity, action, and interest and establishing three kinds of mappings: from entity to action, from action to interest, and from entity to interest, that is, one hidden relation mined through the transfer characteristic of visible mappings
We have proposed an approach of quantitative similarity evaluation of Internet social users based on supernetwork theory utilizing two indices of friend relation and interest similarity
Summary
Many systems can be represented as complex networks or graphs, that is, collections of vertices joined in pairs by edges. Great changes of interpersonal communication have taken place, moving from the real social network to virtual network communities, which provide open platforms for expression and communication so that strangers from real world could share their ideas, form one group with strong influence, and even lead network events. Detecting community structure, which looks on communities as groups of nodes within which there are higher density of edges and between which the edges are sparser, has become one of the hot research topics in the field of complex networks. It can link multitire networks according to many kinds of criteria and provide us with tools to study interrelated networks [16]. It allows for the application of efficient algorithms for computation.
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