Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs

by   Osman Asif Malik, et al.

Many irregular domains such as social networks, financial transactions, neuron connections, and natural language structures are represented as graphs. In recent years, a variety of graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs. However, in many of the applications, the underlying graph changes over time and existing GNNs are inadequate for handling such dynamic graphs. In this paper we propose a novel technique for learning embeddings of dynamic graphs based on 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 that establish the connection between the proposed tensor approach and spectral convolution of tensors are developed. Numerical experiments on real datasets demonstrate the usefulness of the proposed method for an edge classification task on dynamic graphs.


Network of Tensor Time Series

Co-evolving time series appears in a multitude of applications such as e...

Graph Optimized Convolutional Networks

Graph Convolutional Networks (GCNs) have been widely studied for graph d...

T- Hop: Tensor representation of paths in graph convolutional networks

We describe a method for encoding path information in graphs into a 3-d ...

Instant Graph Neural Networks for Dynamic Graphs

Graph Neural Networks (GNNs) have been widely used for modeling graph-st...

Representing Social Networks as Dynamic Heterogeneous Graphs

Graph representations for real-world social networks in the past have mi...

FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding

Dynamic graphs arise in a plethora of practical scenarios such as social...

Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks

Graph convolutional networks (GCNs) is a class of artificial neural netw...

Please sign up or login with your details

Forgot password? Click here to reset