Abstract
Deep models can be made scale-invariant when trained with multi-scale information. Images can be easily made multi-scale, given their grid-like structures. Extending this to generic graphs poses major challenges. For example, in link prediction tasks, inputs are represented as graphs consisting of nodes and edges. Currently, the state-of-the-art model for link prediction uses supervised heuristic learning, which learns graph structure features centered on two target nodes. It then learns graph neural networks to predict the existence of links based on graph structure features. Thus, the performance of link prediction models highly depends on graph structure features. In this work, we propose a novel node aggregation method that can transform the enclosing subgraph into different scales and preserve the relationship between two target nodes for link prediction. A theory for analyzing the information loss during the re-scaling procedure is also provided. Graphs in different scales can provide scale-invariant information, which enables graph neural networks to learn invariant features and improve link prediction performance. Our experimental results on 14 datasets from different areas demonstrate that our proposed method outperforms the state-of-the-art methods by employing multi-scale graphs without additional parameters.
Publisher
Association for the Advancement of Artificial Intelligence (AAAI)
Cited by
35 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Towards explaining graph neural networks via preserving prediction ranking and structural dependency;Information Processing & Management;2024-03
2. Preserving Potential Neighbors for Low-Degree Nodes via Reweighting in Link Prediction;Neural Information Processing;2023-11-15
3. JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning;2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA);2023-10-09
4. Using DeepWalk to Link Prediction in Subgraph;2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP)18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2023);2023-09-25
5. A comprehensive survey of link prediction methods;The Journal of Supercomputing;2023-09-07