Dynamic graph convolutional networks using the tensor m-product

Osman Asif Malik, Shashanka Ubaru, Lior Horesh, Misha E. Kilmer, Haim Avron

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Many irregular domains such as social networks, financial transactions, neuron connections, and natural language constructs are represented using graph structures. In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs. In many of the real-world applications, the underlying graph changes over time, however, most of the existing GNNs are inadequate for handling such dynamic graphs. In this paper we propose a novel technique for learning embeddings of dynamic graphs using a tensor algebra framework. Our method extends the popular graph convolutional network (GCN) for learning representations of dynamic graphs using the recently proposed tensor M-product technique. Theoretical results presented establish a connection between the proposed tensor approach and spectral convolution of tensors. The proposed method TM-GCN is consistent with the Message Passing Neural Network (MPNN) framework, accounting for both spatial and temporal message passing. Numerical experiments on real-world datasets demonstrate the performance of the proposed method for edge classification and link prediction tasks on dynamic graphs. We also consider an application related to the COVID-19 pandemic, and show how our method can be used for early detection of infected individuals from contact tracing data.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining, SDM 2021
PublisherSiam Society
Number of pages9
ISBN (Electronic)9781611976700
StatePublished - 2021
Event2021 SIAM International Conference on Data Mining, SDM 2021 - Virtual, Online
Duration: 29 Apr 20211 May 2021

Publication series

NameSIAM International Conference on Data Mining, SDM 2021


Conference2021 SIAM International Conference on Data Mining, SDM 2021
CityVirtual, Online


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