mlsanityData to 7 Oct 2026

Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction

arXiv 2607.29115 ↗First posted 31 Jul 2026cs.SI cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Link prediction aims to identify potential or future connections within a given graph structure. Position information is essential for link prediction, as it distinguishes homogeneous nodes through their relative relationships, facilitating the accurate capture of structural patterns and implicit connections. Previous studies derive node positional information as distances to single-granularity landmarks, defined as the centers of homophilic regions, while neglecting the multi-granularity nature of homophilic structures and their hierarchical interrelations. We propose the Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction (MGLP) method to obtain multi-granularity position embedding of graphs. Specifically, MGLP introduces an Adaptive Granular-Ball Graph Refinement mechanism to adaptively refine the graph into homophilic subdomains with optimal levels of granularity. The central nodes within subdomains are treated as landmarks, which form a Hierarchical Central Graph. Moreover, a novel Multi-granularity Hierarchical Distance encoding mechanism is proposed to capture both the homophilic structures within a graph and their hierarchical correlations, improving the discriminative power of nodes. Experimental results demonstrate that the multi-granularity position embedding generated by our method exhibits excellent performance and strong competitiveness compared to baseline algorithms for link prediction. Our codes are available in https://anonymous.4open.science/r/MGLP-D3C5/.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 21 of its 36 references that are in the map. First posted on arXiv 31 Jul 2026.

Builds on

21 of 36 references.

References · 21

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  5. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  6. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  7. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  8. 2016Variational Graph Auto-Encoderscited by 1,373
  9. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  10. 2018Link Prediction Based on Graph Neural Networkscited by 829
  11. 2020Benchmarking Graph Neural Networkscited by 672
  12. 2020A Generalization of Transformer Networks to Graphscited by 653
9 more
  1. 2021Rethinking Graph Transformers with Spectral Attentioncited by 468
  2. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
  3. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  4. 2019Position-aware Graph Neural Networkscited by 203
  5. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
  6. 2019Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systemscited by 107
  7. 2018Adversarially Regularized Graph Autoencoder for Graph Embeddingcited by 84
  8. 2019Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systemscited by 28
  9. 2024Hierarchical Position Embedding of Graphs with Landmarks and Clustering for Link Predictioncited by 3

Built on by

Not cited yet.