mlsanityData to 7 Oct 2026

Dual-Primal Graph Convolutional Networks

arXiv 1806.00770 ↗First posted 3 Jun 2018cs.LG cs.AI stat.MLcited by 18

Not on the map. Why?

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.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 7; 2023: 2; 2024: 3; 2025: 1; 2026: 1.

18 papers.

Sort:

  1. 2021Subdivision-Based Mesh Convolution Networkscited by 60
  2. 2022Message passing all the way upcited by 47
  3. 2022OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphscited by 38
  4. 2021Edge Representation Learning with Hypergraphscited by 37
  5. 2022Thermodynamics-informed graph neural networkscited by 31
  6. 2022MeshMAE: Masked Autoencoders for 3D Mesh Data Analysiscited by 31
  7. 2023Deep Learning in Deterministic Computational Mechanicscited by 21
  8. 2021Multi-modal Entity Alignment in Hyperbolic Spacecited by 19
  9. 2023Learning to Count Isomorphisms with Graph Neural Networkscited by 13
  10. 2022Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphscited by 5
  11. 2024Graph Unitary Message Passingcited by 5
  12. 2021Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networkscited by 1
6 more
  1. 2022Entity Alignment For Knowledge Graphs: Progress, Challenges, and Empirical Studiescited by 1
  2. 2024Advancing Graph Neural Networks with HL-HGAT: A Hodge-Laplacian and Attention Mechanism Approach for Heterogeneous Graph-Structured Datacited by 1
  3. 2024A Recipe for Geometry-Aware 3D Mesh Transformerscited by 1
  4. 2022EiX-GNN : Concept-level eigencentrality explainer for graph neural networkscited by 0
  5. 2025Rethinking Graph Super-resolution: Dual Frameworks for Topological Fidelitycited by 0
  6. 2026Dual-Primal Graph VAEs for Noisy Label Aggregationcited by 0