By Nasrullah Memon, Jennifer Jie Xu, David L. Hicks (auth.), Nasrullah Memon, Jennifer Jie Xu, David L. Hicks, Hsinchun Chen (eds.)
Social community facts Mining: examine Questions, suggestions, and purposes Nasrullah Memon, Jennifer Xu, David L. Hicks and Hsinchun Chen automated growth of a social community utilizing sentiment research Hristo Tanev, Bruno Pouliquen, Vanni Zavarella and Ralf Steinberger automated mapping of social networks of actors from textual content corpora: Time sequence research James A. Danowski and Noah Cepela A social community dependent recommender approach (SNRS) Jianming He and Wesley W. Chu community research of U.S. air transportation community Guangying Hua, Yingjie solar, and Dominique Haughton making a choice on high-status nodes in wisdom networks Siddharth Kaza and Hsinchun Chen Modularity for bipartite networks Tsuyoshi Murata ONDOCS: Ordering nodes to become aware of overlapping neighborhood constitution Jiyang Chen, Osmar R. Zaiane, J¨org Sander, and Randy Goebel Framework for quick id of group constructions in Large-Scale Social Networks Yutaka I. Leon-Suematsu and Kikuo Yuta Geographically equipped small groups and the hardness of clustering social networks Miklós Kurucz and András A. Benczúr Integrating genetic algorithms and fuzzy common sense for net constitution optimization Iltae Lee, Negar Koochakzadeh, Keivan Kianmehr, Reda Alhajj, and Jon Rokne
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Cepela Fig. 1 Aggregate Reagan cabinet Fig. W. Bush cabinet proportion of zero centrality time periods. Because centrality can be computed only on a connected network, this indicates that in these periods isolated pairs of cabinet members or isolated individuals were treated in the news stories within the observation window. This finding was confirmed by systematic visual examination of the senior Bush administration’s zero centrality periods. Because the White House itself is the primary source of cabinet news it would appear that this recurring deviation from the norm may have been strategic, in that presidential-level political communication is unlikely left to chance.
4 Network Centrality Measures The most often used centrality measure in social network analysis is “betweenness centrality” . Nevertheless, use of this measure developed by Freeman  has been shown to be very often inappropriate in terms of its operational assumptions in relation to conceptual definitions [6, 35]. The betweenness measure assumes that messages flow through a network along a single shortest path, moving sequentially from one node on this path to the next. No weighting is assumed for the strength of each link in terms of its overall frequency of activation, bandwidth, or channel capacity.
Finding strong and weak opinion clauses. Computational Intelligence, 22(2):73–99, 2006. 27. , and Liu, J. Community mining from signed social networks. Transactions on Knowledge and Data Engineering 10:1333–1348, 2007. 28. Yu, H. and Hatzivassiloglou, V. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Morristown, NJ, 129–136, 2003. Chapter 3 Automatic Mapping of Social Networks of Actors from Text Corpora: Time Series Analysis James A.