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The connected component of an undirected graph plays an important part in graph theory. It is straightforward to compute the connected components of a graph in linear time using either breadth-first search or depth-first search. However when confronted with large scale data, both of the two algorithms are hard to execute. In this paper, we introduce a recently proposed community detection technique...
The continued exponential growth in volume of literature data is giving birth to a new challenge to the bibliographic analysis service and the traditional features such as keyword search, author search and statistics services could not satisfy researchers for in-depth analysis. The emerging of community analysis in social networks is becoming a hot topic in many domains and disciplines such as sociology,...
Structure mining plays an important part in the researches in biology, physics, Internet and telecommunications in recently emerging network science. As a main task in this area, the problem of structure mining on graph has attracted much interest and been studied in variant avenues in prior works. However, most of these works mainly rely on single chip computational capacity and have been constrained...
The data set of scientific literature contains abundant knowledge. Currently, most bibliography search engines provide only keyword search or similarity search functions for retrieving information directly stored in the database but neglect mining hidden information. In this article, we design a visual analytic tool called LiterMiner to extract entities such as article, author, affiliation, and keyword...
Community detection and tracking in social network is an important research area for many applications which are widely applied in complex systems. Recently there has been a surge of investigation in this area, fueled largely by interest in social networks, but also by interest in bibliographic citations and telecommunication records. However, due to the computational cost of the traditional algorithm...
Detecting the community of complex networks became the hot research fields of Graph Ming in recent years and most community detecting methods current try to find correct community structure basing on optimization of Modularity Q. In this article, the author constructs a new theoretic model of Q based on information entropy by simulation and evaluation on some classic dataset and comparison with the...
Researches have discovered that rich interactions among entities in nature and human society bring about complex networks with community structures. In this paper, we propose a novel algorithm BiTector (bi-community detector) to mine the overlapping communities in large-scale sparse bipartite networks. We apply the algorithm to various real-world datasets, showing that BiTector can identify the overlapping...
An interesting property of network is that the information is not only contained in the entities, but also in the links between them. As the structure of the co-authorship network can greatly influence its function and reflect how the internal information is exchanged. We attempt to get deep insight of the features in a co-authorship network at a university. This is done by the following two steps...
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