TY - CONF AU - Nazir, F. AU - Takeda, H. A2 - T1 - Extraction and analysis of tripartite relationships from Wikipedia T2 - IEEE International Symposium on Technology and Society PB - CY - PY - 2008/06 M2 - VL - IS - SP - 1 EP - 13 UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4559785 M3 - 10.1109/ISTAS.2008.4559785 KW - analysis KW - tripartite KW - wiki KW - wikipedia L1 - SN - 978-1-4244-1669-1 N1 - N1 - AB - Social aspects are critical in the decision making process for social actors (human beings). Social aspects can be categorized into social interaction, social communities, social groups or any kind of behavior that emerges from interlinking, overlapping or similarities between interests of a society. These social aspects are dynamic and emergent. Therefore, interlinking them in a social structure, based on bipartite affiliation network, may result in isolated graphs. The major reason is that as these correspondences are dynamic and emergent, they should be coupled with more than a single affiliation in order to sustain the interconnections during interest evolutions. In this paper we propose to interlink actors using multiple tripartite graphs rather than a bipartite graph which was the focus of most of the previous social network building techniques. The utmost benefit of using tripartite graphs is that we can have multiple and hierarchical links between social actors. Therefore in this paper we discuss the extraction, plotting and analysis methods of tripartite relations between authors, articles and categories from Wikipedia. Furthermore, we also discuss the advantages of tripartite relationships over bipartite relationships. As a conclusion of this study we argue based on our results that to build useful, robust and dynamic social networks, actors should be interlinked in one or more tripartite networks. ER - TY - CONF AU - Lambiotte, Renaud AU - Ausloos, Marcel A2 - T1 - Collaborative Tagging as a Tripartite Network T2 - Computational Science – ICCS 2006 PB - Springer Berlin / Heidelberg CY - PY - 2006/ M2 - VL - IS - SP - 1114 EP - 1117 UR - M3 - KW - analysis KW - collaborative KW - folksonomy KW - networks KW - sna KW - social KW - tagging KW - tripartite L1 - SN - N1 - N1 - AB - We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed. ER - TY - GEN AU - Lambiotte, R. AU - Ausloos, M. A2 - T1 - Collaborative tagging as a tripartite network JO - PB - AD - PY - 2005/ VL - IS - SP - EP - UR - http://arxiv.org/abs/cs/0512090 M3 - KW - folksonomy KW - hypergraph KW - network KW - tripartite L1 - N1 - [cs/0512090] Collaborative tagging as a tripartite network N1 - AB - We describe online collaborative communities by tripartite networks, the nodes being persons, items and tags. We introduce projection methods in order to uncover the structures of the networks, i.e. communities of users, genre families... To do so, we focus on the correlations between the nodes, depending on their profiles, and use percolation techniques that consist in removing less correlated links and observing the shaping of disconnected islands. The structuring of the network is visualised by using a tree representation. The notion of diversity in the system is also discussed. ER - TY - CONF AU - Zhao, Lizhuang AU - Zaki, Mohammed J. A2 - T1 - TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data T2 - SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data PB - ACM CY - New York, NY, USA PY - 2005/ M2 - VL - IS - SP - 694 EP - 705 UR - http://portal.acm.org/citation.cfm?id=1066157.1066236 M3 - http://doi.acm.org/10.1145/1066157.1066236 KW - clustering KW - community KW - detection KW - tricluster KW - tripartite L1 - SN - 1-59593-060-4 N1 - N1 - AB - In this paper we introduce a novel algorithm called TRICLUSTER, for mining coherent clusters in three-dimensional (3D) gene expression datasets. TRICLUSTER can mine arbitrarily positioned and overlapping clusters, and depending on different parameter values, it can mine different types of clusters, including those with constant or similar values along each dimension, as well as scaling and shifting expression patterns. TRICLUSTER relies on graph-based approach to mine all valid clusters. For each time slice, i.e., a gene×sample matrix, it constructs the range multigraph, a compact representation of all similar value ranges between any two sample columns. It then searches for constrained maximal cliques in this multigraph to yield the set of bi-clusters for this time slice. Then TRICLUSTER constructs another graph using the biclusters (as vertices) from each time slice; mining cliques from this graph yields the final set of triclusters. Optionally, TRICLUSTER merges/deletes some clusters having large overlaps. We present a useful set of metrics to evaluate the clustering quality, and we show that TRICLUSTER can find significant triclusters in the real microarray datasets. ER -