Geom-GCN: Geometric Graph Convolutional Networks
Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN (Geometric Graph Convolutional Networks), to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs. Code is available at https://github.com/graphdml-uiuc-jlu/geom-gcn.
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Datasets
Introduced in the Paper:
Chameleon (48%/32%/20% fixed splits) Squirrel (48%/32%/20% fixed splits) Cornell (48%/32%/20% fixed splits) Cora (48%/32%/20% fixed splits) Wisconsin (48%/32%/20% fixed splits) PubMed (48%/32%/20% fixed splits) Citeseer (48%/32%/20% fixed splits) Texas (48%/32%/20% fixed splits) Film(48%/32%/20% fixed splits)Used in the Paper:
Pubmed
Cora
Citeseer
Wiki Squirrel
WebKB
Cornell
Wisconsin(60%/20%/20% random splits)
Squirrel (60%/20%/20% random splits)
Cornell (60%/20%/20% random splits)
Film (60%/20%/20% random splits)
Texas(60%/20%/20% random splits)
PubMed (60%/20%/20% random splits)
Chameleon(60%/20%/20% random splits)
