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

Conference Paper


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. Jia., Hagenbuchner, M., Scarcelli, F. & Tsoi, A. Chung. (2010). Supervised encoding of graph-of-graphs for classification and regression problems. In S. Geva, J. Kamps & A. Trotman (Eds.), 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2009 (pp. 449-461). Berlin, Heidelberg: Springer-Verlag.

Start Page


  • 449

End Page


  • 461

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. Jia., Hagenbuchner, M., Scarcelli, F. & Tsoi, A. Chung. (2010). Supervised encoding of graph-of-graphs for classification and regression problems. In S. Geva, J. Kamps & A. Trotman (Eds.), 8th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2009 (pp. 449-461). Berlin, Heidelberg: Springer-Verlag.

Start Page


  • 449

End Page


  • 461