mlsanityData to 14 Oct 2026

Deep Iterative and Adaptive Learning for Graph Neural Networks

arXiv 1912.07832 ↗First posted 17 Dec 2019cs.LG stat.MLcited by 18

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph structure and graph embeddings simultaneously. We first cast the graph structure learning problem as a similarity metric learning problem and leverage an adapted graph regularization for controlling smoothness, connectivity and sparsity of the generated graph. We further propose a novel iterative method for searching for a hidden graph structure that augments the initial graph structure. Our iterative method dynamically stops when the learned graph structure approaches close enough to the optimal graph. Our extensive experiments demonstrate that the proposed DIAL-GNN model can consistently outperform or match state-of-the-art baselines in terms of both downstream task performance and computational time. The proposed approach can cope with both transductive learning and inductive learning.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Dec 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 6; 2022: 4; 2023: 2; 2024: 5; 2025: 0; 2026: 1.

18 papers.

Sort:

  1. 2021SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networkscited by 110
  2. 2021Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networkscited by 38
  3. 2022Robust Training of Graph Neural Networks via Noise Governancecited by 29
  4. 2021Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognitioncited by 20
  5. 2023Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detectioncited by 12
  6. 2022Spatio-Temporal Latent Graph Structure Learning for Traffic Forecastingcited by 7
  7. 2024Learning to Approximate Adaptive Kernel Convolution on Graphscited by 6
  8. 2021Deep Unsupervised Active Learning on Learnable Graphscited by 4
  9. 2023Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrixcited by 2
  10. 2021Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networkscited by 1
  11. 2022AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approachcited by 1
  12. 2021Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognitioncited by 0
6 more
  1. 2022Learning the Network of Graphs for Graph Neural Networkscited by 0
  2. 2024Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactionscited by 0
  3. 2024Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learningcited by 0
  4. 2024Multi-Knowledge Fusion Network for Time Series Representation Learningcited by 0
  5. 2024Boolean Product Graph Neural Networkscited by 0
  6. 2026Joint Precision Neural Networks: Task-Aware Dependency and Predictive Learningcited by 0