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The large number of network media sharing platform are rapid increasing with the development of computer network technology. Network media has the media attribute and social function, the user communicates with each other by publishing the online information, then, the virtual network community is established. Along with the rapid increase of users, the network community influence on real society is more and more large. Therefore, to correctly understand the effect of the individual node in the virtual network community is very important for management of virtual network community and real society.
In recent years, many scholars pay attention to the research of the social network topology analysis, the relationship between the network topology and the dynamics behavior based on the complex networks, etc. The research on the influence of the individual nodes is mainly focus on the diffused contents, the relation between the individual nodes and the influence diffusion, etc. The influential nodes are very important to dynamic evolution of complex networks. It is significantly meaningful for theory and practical application that to identify the most influential nodes (Albert andBarabási, 2002; Newman 2003; Boccaletti, 2006; J. Zhang, 2010; A. Zeng, 2011). For example, the action taken on some influential key nodes can effective can effectively inhibit the spread of rumors and disease or create a larger market for certain services or products in the economic field.
The present researches generally use the centralized indicator, e.g., degree, closeness, betweenness and K-shell to evaluate influence. Such as: Albert (2000) and Pastor-Satorras (2001) measure the most influential nodes by degree centricity; Freeman (1979) uses the betweenness centrality to evaluate influence. Betweenness centrality is the quantity of the shortest path through a node; Kool (2013) use closeness indicator to describe the difficulty that one node to others.
These articles study the influence of the individual nodes in the network from the aspect of internal properties (degree, closeness, betweenness, etc.), and they ignore the external properties (such as the size of the community, community closeness, etc.).This article considers that the influence of individual node closely related with the individual node in the network community, besides, the size of the community, community closeness and the average degree of community can enhance the influence of the individual node in the community.
This article establish local Weight Index and Community Superposition model (LWCS) based on local Weight Index raising by Dr. jun-jun Cheng. And it has carried on the simulation analysis in the real social network. The experiment proved that the proposed method can help to find the most influential nodes.