Identifying Clusters with Attribute Homogeneity and Similar Connectivity in Information Networks

Identifying Clusters with Attribute Homogeneity and Similar Connectivity in Information Networks. Papadopoulos, A., Pallis, G., & Dikaiakos, M. In Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on, volume 1, pages 343-350, Nov, 2013.
doi abstract bibtex

With the rapid emergence of the internet world, a lot of information networks become available every day. In many cases, these information networks contain objects connected by multiple links and described by different attributes. In this paper the problem of clustering homogeneous information networks in groups with similar attributes and connections is studied. Clustering such networks is a challenging task due to different importance of links and attributes. In addition, it is not straightforward how to balance the links and attributes information. In this article we describe these challenges and propose a fuzzy clustering model as well as a fuzzy clustering algorithm, HASCOP. Extensive experimentation on real world datasets has shown that HASCOP can be successfully applied in such networks, demonstrating its efficacy and superiority against the state-of-the-art attributed graph clustering methods.

@inproceedings{ Papadopoulos2013,
  author = {Papadopoulos, A. and Pallis, G. and Dikaiakos, M.D.},
  booktitle = {Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on},
  title = {Identifying Clusters with Attribute Homogeneity and Similar Connectivity in Information Networks},
  year = {2013},
  month = {Nov},
  volume = {1},
  pages = {343-350},
  isbn = {978-1-4799-2902-3},
  abstract = {With the rapid emergence of the internet world, a lot of information networks become available every day. 
In many cases, these information networks contain objects connected by multiple links and described by different attributes. 
In this paper the problem of clustering homogeneous information networks in groups with similar attributes and connections is studied. 
Clustering such networks is a challenging task due to different importance of links and attributes. 
In addition, it is not straightforward how to balance the links and attributes information. 
In this article we describe these challenges and propose a fuzzy clustering model as well as a fuzzy clustering algorithm, HASCOP. 
Extensive experimentation on real world datasets has shown that HASCOP can be successfully applied in such networks, 
demonstrating its efficacy and superiority against the state-of-the-art attributed graph clustering methods.},
  keywords = {Internet;fuzzy set theory;graph theory;pattern clustering;HASCOP;Internet world;attribute homogeneity;cluster identification;fuzzy clustering model;homogeneous information networks clustering;similar connectivity;state-of-the-art attributed graph clustering methods;Clustering algorithms;Coherence;Equations;Mathematical model;Social network services;Time complexity;Vectors;Clustering;Information Networks},
  doi = {10.1109/WI-IAT.2013.49},
  durl = {papadopoulos_andreas/publications/pdf/2013-WI-hascop.pdf}
}

Downloads: 0

{"_id":"YqqC4maERmHCQQ2J4","bibbaseid":"papadopoulos-pallis-dikaiakos-identifyingclusterswithattributehomogeneityandsimilarconnectivityininformationnetworks-2013","downloads":0,"creationDate":"2015-11-13T15:34:20.235Z","title":"Identifying Clusters with Attribute Homogeneity and Similar Connectivity in Information Networks","author_short":["Papadopoulos, A.","Pallis, G.","Dikaiakos, M."],"year":2013,"bibtype":"inproceedings","biburl":"http://www.cs.ucy.ac.cy/~andpapad/papadopoulos_andreas/publications/papadopoulos_andreas_publications.bib","bibdata":{"abstract":"With the rapid emergence of the internet world, a lot of information networks become available every day. In many cases, these information networks contain objects connected by multiple links and described by different attributes. In this paper the problem of clustering homogeneous information networks in groups with similar attributes and connections is studied. Clustering such networks is a challenging task due to different importance of links and attributes. In addition, it is not straightforward how to balance the links and attributes information. In this article we describe these challenges and propose a fuzzy clustering model as well as a fuzzy clustering algorithm, HASCOP. Extensive experimentation on real world datasets has shown that HASCOP can be successfully applied in such networks, demonstrating its efficacy and superiority against the state-of-the-art attributed graph clustering methods.","author":["Papadopoulos, A.","Pallis, G.","Dikaiakos, M.D."],"author_short":["Papadopoulos, A.","Pallis, G.","Dikaiakos, M."],"bibtex":"@inproceedings{ Papadopoulos2013,\n author = {Papadopoulos, A. and Pallis, G. and Dikaiakos, M.D.},\n booktitle = {Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on},\n title = {Identifying Clusters with Attribute Homogeneity and Similar Connectivity in Information Networks},\n year = {2013},\n month = {Nov},\n volume = {1},\n pages = {343-350},\n isbn = {978-1-4799-2902-3},\n abstract = {With the rapid emergence of the internet world, a lot of information networks become available every day. \nIn many cases, these information networks contain objects connected by multiple links and described by different attributes. \nIn this paper the problem of clustering homogeneous information networks in groups with similar attributes and connections is studied. \nClustering such networks is a challenging task due to different importance of links and attributes. \nIn addition, it is not straightforward how to balance the links and attributes information. \nIn this article we describe these challenges and propose a fuzzy clustering model as well as a fuzzy clustering algorithm, HASCOP. \nExtensive experimentation on real world datasets has shown that HASCOP can be successfully applied in such networks, \ndemonstrating its efficacy and superiority against the state-of-the-art attributed graph clustering methods.},\n keywords = {Internet;fuzzy set theory;graph theory;pattern clustering;HASCOP;Internet world;attribute homogeneity;cluster identification;fuzzy clustering model;homogeneous information networks clustering;similar connectivity;state-of-the-art attributed graph clustering methods;Clustering algorithms;Coherence;Equations;Mathematical model;Social network services;Time complexity;Vectors;Clustering;Information Networks},\n doi = {10.1109/WI-IAT.2013.49},\n durl = {papadopoulos_andreas/publications/pdf/2013-WI-hascop.pdf}\n}","bibtype":"inproceedings","booktitle":"Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on","doi":"10.1109/WI-IAT.2013.49","durl":"papadopoulos_andreas/publications/pdf/2013-WI-hascop.pdf","id":"Papadopoulos2013","isbn":"978-1-4799-2902-3","key":"Papadopoulos2013","keywords":"Internet;fuzzy set theory;graph theory;pattern clustering;HASCOP;Internet world;attribute homogeneity;cluster identification;fuzzy clustering model;homogeneous information networks clustering;similar connectivity;state-of-the-art attributed graph clustering methods;Clustering algorithms;Coherence;Equations;Mathematical model;Social network services;Time complexity;Vectors;Clustering;Information Networks","month":"Nov","pages":"343-350","title":"Identifying Clusters with Attribute Homogeneity and Similar Connectivity in Information Networks","type":"inproceedings","volume":"1","year":"2013","bibbaseid":"papadopoulos-pallis-dikaiakos-identifyingclusterswithattributehomogeneityandsimilarconnectivityininformationnetworks-2013","role":"author","urls":{},"keyword":["Internet;fuzzy set theory;graph theory;pattern clustering;HASCOP;Internet world;attribute homogeneity;cluster identification;fuzzy clustering model;homogeneous information networks clustering;similar connectivity;state-of-the-art attributed graph clustering methods;Clustering algorithms;Coherence;Equations;Mathematical model;Social network services;Time complexity;Vectors;Clustering;Information Networks"],"downloads":0},"search_terms":["identifying","clusters","attribute","homogeneity","similar","connectivity","information","networks","papadopoulos","pallis","dikaiakos"],"keywords":["internet;fuzzy set theory;graph theory;pattern clustering;hascop;internet world;attribute homogeneity;cluster identification;fuzzy clustering model;homogeneous information networks clustering;similar connectivity;state-of-the-art attributed graph clustering methods;clustering algorithms;coherence;equations;mathematical model;social network services;time complexity;vectors;clustering;information networks"],"authorIDs":[],"dataSources":["k8nsysxwXqu5xXAXn"]}