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In real life networks like social and biological networks, the network is said to have community structure if the vertices of the network can be partitioned into groups of nodes such that each group contains the nodes that are densely connected in the original graph that represents this network. These groups are referred to as communities. Detecting and identifying communities in networks is essential...
In this paper, an Aggregation-Division Model (ADM) is proposed to simulate the clustering characteristics of nodes in cyberspace and to describe the dynamic evolution process of the community system structure. Based on Cluster-Cluster Aggregation (CCA) model, using the random walk and collision of discrete single particle to simulate the communication and relationship establishment of nodes in social...
Community structure is a common feature in real-world network. Overlap community detection is an important method to analyze topology structure and function of the network. Most algorithms are based on the network structure, without considering the node attributes. In this paper, we propose an overlapping community detection algorithm based on node convergence degree which combines the network topology...
Community detection has become one of the most important methods for studying social networks. However, most of the existing community detection algorithms may not be applicable to mobile social networks due to their complexity. To solve this problem, we present a parallel algorithm to conduct community detection based on general stochastic block (GSB) model. We first model a mobile social network...
In recent years community detection has been a hot research topic in network science, which helps to explain the characteristics of the network structure. This paper analyzed the effect of vertices with high influence in community detection, and found that such vertices have different roles in different networks. A variable influence community detection algorithm based on PageRank is proposed in this...
Social network community detection has occupied an important place in many scientific fields like biology, sociology, or computer science. This problem still attracted a lot of work. The challenge is how to identify inside these networks, groups of persons strongly linked and sharing the same preferences. As in the literature, there are many works trying to detect communities we tried in this paper...
Much of the data of scientific interest, particularly when independence of data is not assumed, can be represented in the form of networks where data nodes are joined together to form edges corresponding to some kind of associations or relationships. Such information networks abound, like protein interactions in biology, web page hyperlink connections in information retrieval on the Web, cellphone...
Many algorithms have been designed to detect community structure in social networks. However, most algorithms can only detect disjoint communities effectively. A new overlapping community structure detecting algorithm is proposed in this paper, which adopts modularity to community clustering. In order to evaluate the algorithm, Modularity by Newman and the NMI (Normalized Mutual Information) by Lancichinetti...
Large-scale social networks emerged rapidly in recent years. Social networks have become complex networks. The structure of social networks is an important research area and has attracted much scientific interest. Community is an important structure in social networks. In this paper, we propose a community detection algorithm based on seed nodes. First, we introduce how to find seed nodes based on...
In this paper we study the ILPnet2 co-authorship network. The ILPnet2 on-line library (www.cs.bris.ac.uk/ILPnet2/Tools/Reports/) is a repository of more than 1,000 ILP-related articles by well over 500 authors, published between 1970 and 2003. Co-authorship networks constitute a specific view on bibliographic data, in which scientific publications are modeled as vertices, and two vertices are connected...
We propose a simplified model which exhibits community structure, power-law degree distribution and high clustering. Every vertex is a social one with a social identity. The preferential attachment of Barabasi-Albert model is incorporated with social similarity. When a newly added vertex makes a new link, it first selects a certain group of vertices with a probability by considering the social distances...
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