Related papers: Shift Aggregate Extract Networks

Shift Aggregate Extract Networks

URL: http://arxiv.org/abs/1703.05537v2
Date: Mon, 18 Mar 2024 11:37:21 GMT
Title: Shift Aggregate Extract Networks
Authors: Francesco Orsini, Daniele Baracchi, Paolo Frasconi,
Abstract summary: We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in kernel methods, enabling nested part-of-part relations. We show empirically that our approach is able to outperform current state-of-the-art graph classification methods on large social network datasets.
Score: 3.3263205689999453
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in kernel methods, enabling nested part-of-part relations. Unlike recursive neural networks, which unroll a template on input graphs directly, we unroll a neural network template over the decomposition hierarchy, allowing us to deal with the high degree variability that typically characterize social network graphs. Deep hierarchical decompositions are also amenable to domain compression, a technique that reduces both space and time complexity by exploiting symmetries. We show empirically that our approach is able to outperform current state-of-the-art graph classification methods on large social network datasets, while at the same time being competitive on small chemobiological benchmark datasets.

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