Graph Clustering through Attribute Statistics Based Embedding
This work tackles the problem of graph clustering by an explicit embedding of graphs into vector spaces. We use an embedding methodology based on occurrence and co-occurrence statistics of representative elements of the node attributes. This embedding methodology has already been used for graph classification problems. In the current paper we investigate its applicability to the problem of clustering color-attributed graphs. The ICPR 2010 Graph Embedding Contest serves us as an evaluation framework. Explicit and implicit embedding methods are evaluated in terms of their ability to cluster object images represented as attributed graphs. We compare the attribute statistics based embedding methodology to explicit and implicit embedding techniques proposed by the contest participants and show improvements in some of the datasets. We then demonstrate further improvements by means of different vectorial metrics and kernel functions on the embedded graphs.
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