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Social network is becoming indispensable of people's lives in recent years. Community detection on real network continues to be a hotspot in data ming domain. As users may join multiple social circles and interest communities, and an abundance of information can be a reflection of users' preference, heterogeneous information fusion and overlapping community detection are two key issues researchers...
One of the traditional ways for detecting dynamic communities is to find the communities at each interval through the static community detection algorithms. However, it usually leads to high computation complexity. In this paper, a novel algorithm based on the MapReduce model and the label propagation progress with the strategy of incremental related vertices is proposed, which is called PLPIRV (Parallel...
Community detection is one of the most important ways that reflect the structure and mechanism beneath the social network. The overlapping communities are more in line with the reality of social network. In the society, the phenomenon of some members shared membership of different communities reflects as overlapping communities in the network. Facing big data network, it is a challenging and computationally...
We propose a new version of an algorithm based on Dirichlet boundary for community detection under the assumption of partially pre-labelled community members. We present a complete mathematical derivation of this method from the continuous Dirichlet boundary problem making explicit various assumptions underlying its discretization. We show, based on this derivation, how our algorithm differs from...
Community detection has attracted considerable attention crossing many areas as it can be used for discovering the structure and features of complex networks. With the increasing size of social networks in real world, community detection approaches should be fast and accurate. The Label Propagation Algorithm (LPA) is known to be one of the near-linear solutions and benefits of easy implementation,...
Community detection is an effective tool for mining hidden information in social networks. Label propagation algorithms (LPA) have been proved to be very fast, which do not require prior information e.g., the number and the size of the communities. However, the results of these algorithms are random and not stable. In this paper, a novel random-walk based label propagation community detection algorithm...
The ongoing issue in social network is detecting the communities for large data sets efficiently, stabilizing the communities in the network so that we get same structure over different runs for same network data set pose a challenging problem in the research community. Although, there were various attempts in the past to come out with an efficient and cost effective algorithm that can perform an...
As community detection having been a hot issue in recent years, firstly the correlate clustering technologies based on local information are studied and summarized. Secondly, the label propagation algorithm that is short for LPA is researched and analyzed in depth. Finally, for the random strategy of LPA results that the network partition is always not optimal, we introduces the concept of the similarity...
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