# Deep Iterative and Adaptive Learning for Graph Neural Networks

arXiv: https://arxiv.org/abs/1912.07832 · HTML: https://mlsanity.com/paper/1912.07832/

## Key facts

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

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

- [SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks](https://mlsanity.com/paper/2102.05034/) (2021)
- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Robust Training of Graph Neural Networks via Noise Governance](https://mlsanity.com/paper/2211.06614/) (2022)
- [Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/2112.03328/) (2021)
- [Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection](https://mlsanity.com/paper/2303.17486/) (2023)
- [Spatio-Temporal Latent Graph Structure Learning for Traffic Forecasting](https://mlsanity.com/paper/2202.12586/) (2022)
- [Learning to Approximate Adaptive Kernel Convolution on Graphs](https://mlsanity.com/paper/2401.11840/) (2024)
- [Deep Unsupervised Active Learning on Learnable Graphs](https://mlsanity.com/paper/2111.04286/) (2021)
- [Robust Graph Structure Learning with the Alignment of Features and Adjacency Matrix](https://mlsanity.com/paper/2307.02126/) (2023)
- [Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks](https://mlsanity.com/paper/2104.04255/) (2021)
- [AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach](https://mlsanity.com/paper/2208.08200/) (2022)
- [Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.05482/) (2021)
- [Learning the Network of Graphs for Graph Neural Networks](https://mlsanity.com/paper/2210.03907/) (2022)
- [Topological Neural Networks: Mitigating the Bottlenecks of Graph Neural Networks via Higher-Order Interactions](https://mlsanity.com/paper/2402.06908/) (2024)
- [Probability Passing for Graph Neural Networks: Graph Structure and Representations Joint Learning](https://mlsanity.com/paper/2407.10688/) (2024)
- [Multi-Knowledge Fusion Network for Time Series Representation Learning](https://mlsanity.com/paper/2408.12423/) (2024)
- [Boolean Product Graph Neural Networks](https://mlsanity.com/paper/2409.14001/) (2024)
- [Joint Precision Neural Networks: Task-Aware Dependency and Predictive Learning](https://mlsanity.com/paper/2610.06023/) (2026)

## 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.