@article{
 title = {Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules},
 type = {article},
 year = {2018},
 pages = {1-11},
 websites = {http://arxiv.org/abs/1811.09595},
 id = {5fd66d0c-7397-34ae-9cff-9f469a6ed809},
 created = {2021-08-04T13:05:08.165Z},
 file_attached = {true},
 profile_id = {ad172e55-c0e8-3aa4-8465-09fac4d5f5c8},
 group_id = {1ff583c0-be37-34fa-9c04-73c69437d354},
 last_modified = {2021-08-04T13:05:35.698Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification. In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size. Chebyshev GCNs restrict graphs to have at most one edge between any pair of nodes. To this end, we propose a novel multigraph network that learns from multi-relational graphs. We model learned edges with abstract meaning and experiment with different ways to fuse the representations extracted from annotated and learned edges, achieving competitive results on a variety of chemical classification benchmarks.},
 bibtype = {article},
 author = {Knyazev, Boris and Lin, Xiao and Amer, Mohamed R. and Taylor, Graham W.}
}

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