The advent of the Bitcoin system has brought another boom in the Internet era. In a very short time, many Blockchain systems come into being successively, whose decentration, consensus mechanisms, intelligent contract...
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Rough set theory has been widely and successfully used in data mining, especially in classification field. But most existing rough set based classification approaches require computing optimal attribute reduction, whi...
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A Cloud may be seen as a type of flexible computing infrastructure consisting of many compute nodes, where resizable computing capacities can be provided to different customers. To fully harness the power of the Cloud...
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In order to improve the efficiency of the communication networks, we used the Kruskal algorithm and the Prim algorithm through algorithm comparison and analysis methods of data structure. A dynamic framework for the c...
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Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to a...
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As a typical social media in Web 2.0, blogs have attracted a surge of researches. Unlike the traditional studies, the social networks mined from Internet are very large, which makes a lot of social network analyzing a...
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ISBN:
(纸本)9781605586762
As a typical social media in Web 2.0, blogs have attracted a surge of researches. Unlike the traditional studies, the social networks mined from Internet are very large, which makes a lot of social network analyzing algorithms to be intractable. According to this phenomenon, this paper addresses the novel problem of efficient social networks analyzing on blogs. This paper turns to account the structural characteristics of real large-scale complex networks, and proposes a novel shortest path approximate algorithm to calculate the distance and shortest path between nodes efficiently. The approximate algorithm then is incorporated with social network analysis algorithms and measurements for large-scale social networks analysis. We illustrate the advantages of the approximate analysis through the centrality measurements and community mining algorithms. The experiments demonstrate the effectiveness of the proposed algorithms on blogs, which indicates the necessity of taking account of the structural characteristics of complex networks when optimizing the analysis algorithms on large-scale social networks. Copyright 2009 ACM.
Peer to peer backup systems store data on "unreliable" peers that can leave the system at any moment. In this case, the only way to assure durability of the data is to add redundancy using either replication...
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With the increase of location-based services, Web contents are being geo-tagged, and spatial keyword queries that retrieve objects satisfying both spatial and keyword conditions are gaining in prevalence. Unfortunatel...
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In this article we describe a method for selecting informative genes from microarray data. The method is based on clustering, namely, it first find similar genes, group them and then select informative genes from thes...
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The deep neural named entity recognition model automatically learns and extracts the features of entities and solves the problem of the traditional model relying heavily on complex feature engineering and obscure prof...
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