Motif Prediction with Graph Neural Networks
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2021-06-05
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Working Paper
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Abstract
Link prediction is one of the central problems in graph mining. However, recent
studies highlight the importance of higher-order network analysis, where complex
structures called motifs are the first-class citizens. We first show that existing link
prediction schemes fail to effectively predict motifs. To alleviate this, we establish
a general motif prediction problem and we propose several heuristics that assess the
chances for a specified motif to appear. To make the scores realistic, our heuristics
consider – among others – correlations between links, i.e., the potential impact
of some arriving links on the appearance of other links in a given motif. Finally,
for highest accuracy, we develop a graph neural network (GNN) architecture for
motif prediction. Our architecture offers vertex features and sampling schemes
that capture the rich structural properties of motifs. While our heuristics are fast
and do not need any training, GNNs ensure highest accuracy of predicting motifs,
both for dense (e.g., k-cliques) and for sparse ones (e.g., k-stars). We consistently
outperform the best available competitor by more than 10% on average and up to
32% in area under the curve. Importantly, the advantages of our approach over
schemes based on uncorrelated link prediction increase with the increasing motif
size and complexity. We also successfully apply our architecture for predicting
more arbitrary clusters and communities, illustrating its potential for graph mining
beyond motif analysis.
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Cornell University
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03950 - Hoefler, Torsten / Hoefler, Torsten