Titlebook: Computational Social Networks; Mining and Visualiza Ajith Abraham Book 2012 Springer-Verlag London 2012 Ad Hoc Network Applications and Ser

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Correlation Mining for Web News Information Retrievalm of Multi-correlation Probabilistic Matrix Factorization (MPMF) is proposed to reconstruct it with joint consideration of the three correlations. Third, the result ranking and visualization are conducted to present search results. Experimental results on a news dataset collected from multiple news

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Reliable Online Social Network Data Collectionvices; and for mining data collected from such social networks and applications. This chapter reviews previous research which has looked at social network data collection and user behaviour in these networks. We highlight shortcomings in the methods used in these studies and introduce our own method

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Knowledge Mining from the Twitter Social Network: The Case of Barack Obamated a cluster analysis that helped collecting Barack Obama’s Twitter contents in groups. Studying the results, we perceived that these clusters could be interpreted as a mirror of his political strategy. Finally, we discuss the application of this method for other social networks.

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Mining and Visualizing Research Networks Using the Artefact-Actor-Network Approachtive measures while different types are not directly comparable to each other. Further, our analysis shows that narrowness of a Research Network’s subject area can be predicted using the connectedness of semantic similarity networks. Finally, conclusions are drawn and implications for future researc

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Intelligent-Based Visual Pattern Clustering for Storage Layouts in Virtual Environmentsication hint clustering produces efficiency savings of up to 30% or more over conventional non-OHGC storage solutions, whereas the non-OHGC schemes for retrieve only achieve savings about 20% over conventional storage systems.

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Extraction and Analysis of Facebook Friendship Relationsesent our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations is restricted; thus, we acquired the necessary information directly from the front

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