This paper proposed an overlapping community discovery method based on cored nodes and the Latent Allocation Dirichlet (LDA) topic modeling, which is called as CN-LDA. CN-LDA models the complex network with the LDA model, finds the probability of each edge in each community, and then uses statistical methods to calculate the probability value of each node in each community. Furthermore, to determine the community number of the network, we give an algorithm to identify the core nodes of the complex networks with the threshold random walk. CN-LDA also can be used to determine overlapping nodes. We do experiments to Compare CN-LDA with some other community algorithms in several real-world social networks. The experimental results showed that CN-LDA is effective to discover overlapping communities.
Abstract. Query classification can improve the query results of search engine, but the existing query classification methods which use extra web resources to enrich query features easily result in high delay. In this paper, a query classification based on index association rule expansion (IARE-QC) is proposed. IARE-QC uses an index based query classification framework to reduce the response time through transforming the query classification problem in online phase to the equivalent index term classification in offline phase. Moreover, in order to get more accurate feature enrichment of index term, we propose a novel algorithm which called index association expansion based on similarity voting (IARE-SV) to determine the category labels of index term. The experiment results on the search engine simulation environment show that IARE-SV can get much better query classification performance than the common simple voting (SV) method.
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