# Dual-Primal Graph Convolutional Networks

arXiv: https://arxiv.org/abs/1806.00770 · HTML: https://mlsanity.com/paper/1806.00770/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2018.

## Built on by (most cited first, 18 of 18)

- [Subdivision-Based Mesh Convolution Networks](https://mlsanity.com/paper/2106.02285/) (2021)
- [Message passing all the way up](https://mlsanity.com/paper/2202.11097/) (2022)
- [OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs](https://mlsanity.com/paper/2205.15117/) (2022)
- [Edge Representation Learning with Hypergraphs](https://mlsanity.com/paper/2106.15845/) (2021)
- [Thermodynamics-informed graph neural networks](https://mlsanity.com/paper/2203.01874/) (2022)
- [MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis](https://mlsanity.com/paper/2207.10228/) (2022)
- [Deep Learning in Deterministic Computational Mechanics](https://mlsanity.com/paper/2309.15421/) (2023)
- [Multi-modal Entity Alignment in Hyperbolic Space](https://mlsanity.com/paper/2106.03619/) (2021)
- [Learning to Count Isomorphisms with Graph Neural Networks](https://mlsanity.com/paper/2302.03266/) (2023)
- [Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs](https://mlsanity.com/paper/2210.00032/) (2022)
- [Graph Unitary Message Passing](https://mlsanity.com/paper/2403.11199/) (2024)
- [Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks](https://mlsanity.com/paper/2112.07743/) (2021)
- [Entity Alignment For Knowledge Graphs: Progress, Challenges, and Empirical Studies](https://mlsanity.com/paper/2205.08777/) (2022)
- [Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Data](https://mlsanity.com/paper/2403.06687/) (2024)
- [A Recipe for Geometry-Aware 3D Mesh Transformers](https://mlsanity.com/paper/2411.00164/) (2024)
- [EiX-GNN : Concept-level eigencentrality explainer for graph neural networks](https://mlsanity.com/paper/2206.03491/) (2022)
- [Rethinking Graph Super-resolution: Dual Frameworks for Topological Fidelity](https://mlsanity.com/paper/2511.08853/) (2025)
- [Dual-Primal Graph VAEs for Noisy Label Aggregation](https://mlsanity.com/paper/2608.11473/) (2026)

## Abstract (arXiv)

In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to learn both vertex- and edge features and generalizes the previous graph attention (GAT) model. We provide extensive experimental validation showing state-of-the-art results on a variety of tasks tested on established graph benchmarks, including CORA and Citeseer citation networks as well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender systems.