mlsanityData to 7 Oct 2026

AutoGEL: An Automated Graph Neural Network with Explicit Link Information

arXiv 2112.01064 ↗First posted 2 Dec 2021cs.LG cs.AIcited by 17

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Abstract (arXiv)

Recently, Graph Neural Networks (GNNs) have gained popularity in a variety of real-world scenarios. Despite the great success, the architecture design of GNNs heavily relies on manual labor. Thus, automated graph neural network (AutoGNN) has attracted interest and attention from the research community, which makes significant performance improvements in recent years. However, existing AutoGNN works mainly adopt an implicit way to model and leverage the link information in the graphs, which is not well regularized to the link prediction task on graphs, and limits the performance of AutoGNN for other graph tasks. In this paper, we present a novel AutoGNN work that explicitly models the link information, abbreviated to AutoGEL. In such a way, AutoGEL can handle the link prediction task and improve the performance of AutoGNNs on the node classification and graph classification task. Specifically, AutoGEL proposes a novel search space containing various design dimensions at both intra-layer and inter-layer designs and adopts a more robust differentiable search algorithm to further improve efficiency and effectiveness. Experimental results on benchmark data sets demonstrate the superiority of AutoGEL on several tasks.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 50 of its 83 references that are in the map. First posted on arXiv 2 Dec 2021.

Builds on

50 of 83 references.

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Cited by year

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

17 papers.

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  1. 2022A Survey on Graph Representation Learning Methodscited by 66
  2. 2022NAS-Bench-Graph: Benchmarking Graph Neural Architecture Searchcited by 25
  3. 2023Auto-HeG: Automated Graph Neural Network on Heterophilic Graphscited by 23
  4. 2021Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspectivecited by 19
  5. 2023Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Taskscited by 13
  6. 2022Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directionscited by 8
  7. 2024Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modelingcited by 8
  8. 2023Search to Capture Long-range Dependency with Stacking GNNs for Graph Classificationcited by 7
  9. 2023Heterogeneous Graph Neural Architecture Search with GPT-4cited by 7
  10. 2024Combinatorial Optimization with Automated Graph Neural Networkscited by 5
  11. 2024Calibrated Dataset Condensation for Faster Hyperparameter Searchcited by 4
  12. 2024Customized Subgraph Selection and Encoding for Drug-drug Interaction Predictioncited by 4
5 more
  1. 2025LLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Searchcited by 3
  2. 2022Search to Pass Messages for Temporal Knowledge Graph Completioncited by 2
  3. 2023Graph Neural Architecture Search with GPT-4cited by 1
  4. 2024Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Modelscited by 1
  5. 2025Beyond Model Base Retrieval: Weaving Knowledge to Master Fine-grained Neural Network Designcited by 0