Transcription of Learning Convolutional Neural Networks for Graphs
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Learning Convolutional Neural Networks for Graphs
Learning Convolutional Neural Networks for GraphsMathias Labs Europe, Heidelberg, GermanyAbstractNumerous important problems can be framed aslearning from graph data. We propose a frame-work for Learning Convolutional Neural networksfor arbitrary Graphs . These Graphs may be undi-rected, directed, and with both discrete and con-tinuous node and edge attributes. Analogous toimage-based Convolutional Networks that oper-ate on locally connected regions of the input, wepresent a general approach to extracting locallyconnected regions from Graphs . Using estab-lished benchmark data sets, we demonstrate thatthe learned feature representations are competi-tive with state of the art graph kernels and thattheir computation is highly IntroductionWith this paper we aim to bring Convolutional Neural net-works to bear on a large class of graph -based Learning prob-lems.
malization of neighborhood graphs, that is, a unique map-ping from a graph representation into a vector space rep-resentation. The proposed approach, termed PATCHY-SAN, addresses these two problems for arbitrary graphs. For each input graph, it first determines nodes (and their order) for which neighborhood graphs are created. For each of these
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