Tensor Graph Convolutional Networks for Text Classification

Li, Haiping; You, Xinxin; Zhang, Xiao; Wu, Ji; Lv, Ping

doi:10.48550/arxiv.2001.05313

Cited by 2 publications

(1 citation statement)

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“…In particular, the cross graph convolution layer involves computing a parameterized Kronecker sum of the current adjacency matrix with the previously processed adjacency matrix, followed by a GCN layer. Recently, [18] described a tensor version of GCN for text classification, where the text semantics are represented as a three-dimensional graph tensor. This work neither considers time varying graphs, nor the tensor M-product framework.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Graph Convolutional Networks Using the Tensor M-Product

Malik

Ubaru²,

Horesh³

et al. 2021

Proceedings of the 2021 SIAM International Conference on Data Mining (SDM)

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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 Mproduct 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 realworld 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.

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Section: Related Workmentioning

confidence: 99%