Copyright Notice:

The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a noncommercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Publications of SPCL

M. Besta, R. Grob, C. Miglioli, N. Bernold, G. Kwasniewski, G. Gjini, R. Kanakagiri, S. Ashkboos, L. Gianinazzi, N. Dryden, T. Hoefler:

 Motif Prediction with Graph Neural Networks

(May 2021)

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.

Documents

download article:
access preprint on arxiv:
 

BibTeX

@article{,
  author={Maciej Besta and Raphael Grob and Cesare Miglioli and Nicola Bernold and Grzegorz Kwasniewski and Gabriel Gjini and Raghavendra Kanakagiri and Saleh Ashkboos and Lukas Gianinazzi and Nikoli Dryden and Torsten Hoefler},
  title={{Motif Prediction with Graph Neural Networks}},
  year={2021},
  month={05},
}