Abstract
The community structure detection in static networks often ignores the dynamic nature of the network and it is difficult to identify the evolution of community structure in dynamic networks. The community structure will converge or split as the nodes and edges change. Understanding the evolution of communities over time is an important issue in the study of social networks. Based on the characteristics of dynamic networks, this paper analyzed the influence of variables in dynamic networks structure. We proposed an Incremental algorithm with Coherent Neighborhood Propinquity in dynamic networks. The algorithm considered the direct and indirect effects of changing nodes in their previous communities. We also considered the coherent neighborhood propinquity and improved the influence range of variable nodes. Comparing with the traditional algorithms, the experimental results showed that the proposed algorithm has better performance and less running time.
Highlights
The internet has developed rapidly in recent years, and the number of users worldwide is close to 3.9 billion
The online social network is a typical dynamic network, such as the we may follow the new blogger, the network has the characteristic of dynamic change, and has the high requirement to the computational complexity with large amount of data that changes at any time
INCREMENTAL ALGORITHM WITH COHERENT NEIGHBORHOOD PROPINQUITY Aiming at the characteristics of real-time change of nodes and edges in dynamic networks, we proposed an Incremental algorithm with Coherent Neighborhood Propinquity (ICNP) to detect the community structure quickly and accurately
Summary
The internet has developed rapidly in recent years, and the number of users worldwide is close to 3.9 billion. Real-life relationship affects user relationships and community structures in social networks. The basic attribute of social network is that the network topology changes dynamically with the change of time, so the traditional static community discovery algorithm cannot meet the demand of social network development and evolution over time. In order to meet the needs of the community dynamic analysis, some researchers simulated social network change by a series of static graphs [7], [8]. They have mapped out different time slices in dynamic networks in order to realize the Community Structure Division of dynamic network. The algorithm synthesizes the direct influence and indirect influence of the nodes to the community and synthesizes the historical data and the new variable data to determine the community division scheme of dynamic network
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