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Supervised encoding of graph-of-graphs for classification and regression problems

Journal Article


Abstract


  • This paper introduces a novel approach for processing a general class of structured information, viz., a graph of graphs structure, in which each node of the graph can be described by another graph, and each node in this graph, in turn, can be described by yet another graph, up to a finite depth. This graph of graphs description may be used as an underlying model to describe a number of naturally and artificially occurring systems, e.g. nested hypertexted documents. The approach taken is a data driven method in that it learns from a set of examples how to classify the nodes in a graph of graphs. To the best of

    our knowledge, this is the first time that a machine learning approach is enabled to deal with such structured problem domains. Experimental results on a relatively large scale real world problem indicate that the learning is efficient. This paper presents some preliminary results which show that the classification performance is already close to those provided by the state-of-the-art ones.

Publication Date


  • 2010

Citation


  • Zhang, S., Hagenbuchner, M., Scarselli, F. & Tsoi, A. (2010). Supervised encoding of graph-of-graphs for classification and regression problems. S. Geva, J. Kamps & A. Trotman In INEX 2009:8th International Workshop of the Initiative for the Evaluation of XML Retrieval, Dec 7-9 2009, Brisbane, Australia. Lecture Notes in Computer Science, 6203 (2010), 449-461.

Scopus Eid


  • 2-s2.0-77955334635

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3321

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 449

End Page


  • 461

Volume


  • 6203

Issue


  • 2010

Place Of Publication


  • Germany

Abstract


  • This paper introduces a novel approach for processing a general class of structured information, viz., a graph of graphs structure, in which each node of the graph can be described by another graph, and each node in this graph, in turn, can be described by yet another graph, up to a finite depth. This graph of graphs description may be used as an underlying model to describe a number of naturally and artificially occurring systems, e.g. nested hypertexted documents. The approach taken is a data driven method in that it learns from a set of examples how to classify the nodes in a graph of graphs. To the best of

    our knowledge, this is the first time that a machine learning approach is enabled to deal with such structured problem domains. Experimental results on a relatively large scale real world problem indicate that the learning is efficient. This paper presents some preliminary results which show that the classification performance is already close to those provided by the state-of-the-art ones.

Publication Date


  • 2010

Citation


  • Zhang, S., Hagenbuchner, M., Scarselli, F. & Tsoi, A. (2010). Supervised encoding of graph-of-graphs for classification and regression problems. S. Geva, J. Kamps & A. Trotman In INEX 2009:8th International Workshop of the Initiative for the Evaluation of XML Retrieval, Dec 7-9 2009, Brisbane, Australia. Lecture Notes in Computer Science, 6203 (2010), 449-461.

Scopus Eid


  • 2-s2.0-77955334635

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3321

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 449

End Page


  • 461

Volume


  • 6203

Issue


  • 2010

Place Of Publication


  • Germany