Abstract
Treatment of general structured information by neural networks is an emerging research topic. Here we show how representations for graphs preserving all the information can be devised by Recursive Principal Components Analysis learning. These representations are derived from eigenanalysis of extended vectorial representations of the input graphs. Experimental results performed on a set of chemical compounds represented as undirected graphs show the feasibility and effectiveness of the proposed approach. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Micheli, A., & Sperduti, A. (2007). Recursive principal component analysis of graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4669 LNCS, pp. 826–835). Springer Verlag. https://doi.org/10.1007/978-3-540-74695-9_85
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