mlsanityData to 7 Oct 2026

Structure Enhanced Graph Neural Networks for Link Prediction

arXiv 2201.05293 ↗First posted 14 Jan 2022cs.LGcited by 6

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Abstract (arXiv)

Graph Neural Networks (GNNs) have shown promising results in various tasks, among which link prediction is an important one. GNN models usually follow a node-centric message passing procedure that aggregates the neighborhood information to the central node recursively. Following this paradigm, features of nodes are passed through edges without caring about where the nodes are located and which role they played. However, the neglected topological information is shown to be valuable for link prediction tasks. In this paper, we propose Structure Enhanced Graph neural network (SEG) for link prediction. SEG introduces the path labeling method to capture surrounding topological information of target nodes and then incorporates the structure into an ordinary GNN model. By jointly training the structure encoder and deep GNN model, SEG fuses topological structures and node features to take full advantage of graph information. Experiments on the OGB link prediction datasets demonstrate that SEG achieves state-of-the-art results among all three public datasets.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 14 Jan 2022.

Builds on

18 of 33 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
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6 more
  1. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  2. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  3. 2015LINE: Large-scale Information Network Embeddingcited by 582
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 1.

6 papers.

  1. 2022Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selectioncited by 26
  2. 2024Edge Classification on Graphs: New Directions in Topological Imbalancecited by 4
  3. 2024Tensor-Fused Multi-View Graph Contrastive Learningcited by 3
  4. 2024Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methodscited by 3
  5. 2023Disentangling Node Attributes from Graph Topology for Improved Generalizability in Link Predictioncited by 0
  6. 2025Understanding the Design Principles of Link Prediction in Directed Settingscited by 0