mlsanityData to 14 Oct 2026

Link prediction on multi-relational graphs from an influence propagation perspective

arXiv 2608.05016 ↗First posted 5 Aug 2026cs.SI cs.LGcited by 0

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Abstract (arXiv)

Predicting the existence and type of links (edges) between nodes in a multi-relational graph is key for applications from social interaction prediction to knowledge relationship identification. Enhancing local features with relevant global information is crucial for accurate link prediction, yet it remains challenging. We address this by modeling the relationship between node pairs as node influence. That is, whether the node influence can be propagated and what type of influence is propagated indicates where and what type the edge is, which will be the most relevant local and global information to predict the edges. To this end, we extend the Susceptible-Infectious-Recovered (SIR) epidemic model to capture the influence propagation of nodes on a large scale through sub-graph structures. Subsequently, these sub-graphs are compressed using virtual edges, thereby substantially reducing the computation associated with utilizing the global graph structure. Finally, we propose the Influential Graph Neural Predictor, referred to as IGNP, a link prediction framework guided by influence propagation. Extensive experiments demonstrate the superiority of the proposed method, which outperforms strong baselines by a large margin on the widely used and real-world datasets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 45 references that are in the map. First posted on arXiv 5 Aug 2026.

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References · 24

  1. 2017Inductive Representation Learning on Large Graphscited by 6,320
  2. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  3. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,694
  4. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
  5. 2018Link Prediction Based on Graph Neural Networkscited by 832
  6. 2015LINE: Large-scale Information Network Embeddingcited by 582
  7. 2020Heterogeneous Graph Transformercited by 582
  8. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 541
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  10. 2017struc2vec: Learning Node Representations from Structural Identitycited by 215
  11. 2020Data Augmentation for Graph Neural Networkscited by 203
  12. 2022Graph Neural Networks for Link Prediction with Subgraph Sketchingcited by 81
12 more
  1. 2023Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarkingcited by 69
  2. 2018GeniePath: Graph Neural Networks with Adaptive Receptive Pathscited by 66
  3. 2023Neural Common Neighbor with Completion for Link Predictioncited by 64
  4. 2014Epidemic processes in complex networkscited by 58
  5. 2017HARP: Hierarchical Representation Learning for Networkscited by 58
  6. 2016Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguationcited by 48
  7. 2024Motif-aware Riemannian Graph Neural Network with Generative-Contrastive Learningcited by 23
  8. 2018Graph Partition Neural Networks for Semi-Supervised Classificationcited by 17
  9. 2024PageRank Bandits for Link Predictioncited by 14
  10. 2020Neural graph embeddings as explicit low-rank matrix factorization for link predictioncited by 4
  11. 2023Local Structure-aware Graph Contrastive Representation Learningcited by 4
  12. 2024Reconsidering the Performance of GAE in Link Predictioncited by 4

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