# Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

arXiv: https://arxiv.org/abs/2401.07065 · HTML: https://mlsanity.com/paper/2401.07065/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 13 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Dynamic Graph Convolutional Networks](https://mlsanity.com/paper/1704.06199/) (2017)
- [word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data](https://mlsanity.com/paper/2003.12590/) (2020)
- [Learning Conjoint Attentions for Graph Neural Nets](https://mlsanity.com/paper/2102.03147/) (2021)
- [Spatiotemporal Propagation Learning for Network-Wide Flight Delay Prediction](https://mlsanity.com/paper/2207.06959/) (2022)
- [A Nonlinear PID-Enhanced Adaptive Latent Factor Analysis Model](https://mlsanity.com/paper/2208.02513/) (2022)

## Built on by (most cited first, 8 of 8)

- [End-to-end Graph Learning Approach for Cognitive Diagnosis of Student Tutorial](https://mlsanity.com/paper/2411.00845/) (2024)
- [Fed-LDR: Federated Local Data-infused Graph Creation with Node-centric Model Refinement](https://mlsanity.com/paper/2411.04936/) (2024)
- [MDS-GNN: A Mutual Dual-Stream Graph Neural Network on Graphs with Incomplete Features and Structure](https://mlsanity.com/paper/2408.04845/) (2024)
- [Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2408.02704/) (2024)
- [Enhanced Traffic Flow Prediction with Multi-Segment Fusion Tensor Graph Convolutional Networks](https://mlsanity.com/paper/2408.04232/) (2024)
- [Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2408.04838/) (2024)
- [High-order Graph Neural Networks with Common Neighbor Awareness for Link Prediction](https://mlsanity.com/paper/2504.18758/) (2025)
- [CacheDyG: Decoupling Temporal Propagation for Efficient Dynamic Graph Learning](https://mlsanity.com/paper/2609.25814/) (2026)

## Abstract (arXiv)

Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form; b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance.