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.

@inproceedings{nazir2008tripartite, author = {Nazir, F. and Takeda, H.}, title = {Extraction and analysis of tripartite relationships from Wikipedia}, booktitle = {IEEE International Symposium on Technology and Society}, year = {2008}, pages = {1--13}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4559785}, doi = {10.1109/ISTAS.2008.4559785}, isbn = {978-1-4244-1669-1}, keywords = {analysis, tripartite, wiki, wikipedia}, abstract = {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.} }

%0 = inproceedings %A = Nazir, F. and Takeda, H. %B = IEEE International Symposium on Technology and Society %D = 2008 %T = Extraction and analysis of tripartite relationships from Wikipedia %U = http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4559785

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.

@inproceedings{paper:lambiotte:2006, author = {Lambiotte, Renaud and Ausloos, Marcel}, title = {Collaborative Tagging as a Tripartite Network}, booktitle = {Computational Science – ICCS 2006}, publisher = {Springer Berlin / Heidelberg}, year = {2006}, pages = {1114-1117}, keywords = {analysis, collaborative, folksonomy, networks, sna, social, tagging, tripartite}, abstract = {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.} }

%0 = inproceedings %A = Lambiotte, Renaud and Ausloos, Marcel %B = Computational Science – ICCS 2006 %D = 2006 %I = Springer Berlin / Heidelberg %T = Collaborative Tagging as a Tripartite Network

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.

@misc{Lambiotte2005, author = {Lambiotte, R. and Ausloos, M.}, title = {Collaborative tagging as a tripartite network}, year = {2005}, note = {cite arxiv:cs.DS/0512090 }, url = {http://arxiv.org/abs/cs/0512090}, keywords = {folksonomy, hypergraph, network, tripartite}, abstract = { 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. } }

%0 = misc %A = Lambiotte, R. and Ausloos, M. %D = 2005 %T = Collaborative tagging as a tripartite network %U = http://arxiv.org/abs/cs/0512090

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.

@inproceedings{1066236, author = {Zhao, Lizhuang and Zaki, Mohammed J.}, title = {TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data}, booktitle = {SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data}, publisher = {ACM}, address = {New York, NY, USA}, year = {2005}, pages = {694--705}, url = {http://portal.acm.org/citation.cfm?id=1066157.1066236}, doi = {http://doi.acm.org/10.1145/1066157.1066236}, isbn = {1-59593-060-4}, keywords = {clustering, community, detection, tricluster, tripartite}, abstract = {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.} }

%0 = inproceedings %A = Zhao, Lizhuang and Zaki, Mohammed J. %B = SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data %C = New York, NY, USA %D = 2005 %I = ACM %T = TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data %U = http://portal.acm.org/citation.cfm?id=1066157.1066236