Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling

• URL: http://arxiv.org/abs/2411.07482v2
• Date: Fri, 22 Nov 2024 00:48:57 GMT
• Title: Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling
• Authors: Jinming Xing, Ruilin Xing,
• Abstract summary: Fuzzy Graph Attention Networks (FGAT) is a novel approach integrating fuzzy rough sets for dynamic negative sampling. FNS selects high-quality negative edges based on fuzzy similarities, improving training efficiency. Experiments on two research collaboration networks demonstrate FGAT's superior link prediction accuracy, outperforming state-of-the-art baselines.
• Score: 0.0
• License:
• Abstract: Link prediction is crucial for understanding complex networks but traditional Graph Neural Networks (GNNs) often rely on random negative sampling, leading to suboptimal performance. This paper introduces Fuzzy Graph Attention Networks (FGAT), a novel approach integrating fuzzy rough sets for dynamic negative sampling and enhanced node feature aggregation. Fuzzy Negative Sampling (FNS) systematically selects high-quality negative edges based on fuzzy similarities, improving training efficiency. FGAT layer incorporates fuzzy rough set principles, enabling robust and discriminative node representations. Experiments on two research collaboration networks demonstrate FGAT's superior link prediction accuracy, outperforming state-of-the-art baselines by leveraging the power of fuzzy rough sets for effective negative sampling and node feature learning.

Related papers

• Dual-Frequency Filtering Self-aware Graph Neural Networks for Homophilic and Heterophilic Graphs [60.82508765185161]
  We propose Dual-Frequency Filtering Self-aware Graph Neural Networks (DFGNN) DFGNN integrates low-pass and high-pass filters to extract smooth and detailed topological features. It dynamically adjusts filtering ratios to accommodate both homophilic and heterophilic graphs.
  arXiv  Detail & Related papers  (2024-11-18T04:57:05Z)
• Curriculum Negative Mining For Temporal Networks [33.70909189731187]
  Temporal networks are effective in capturing the evolving interactions of networks over time. CurNM is a model-aware curriculum learning framework that adaptively adjusts the difficulty of negative samples. Our method outperforms baseline methods by a significant margin.
  arXiv  Detail & Related papers  (2024-07-24T07:55:49Z)
• Diffusion-based Negative Sampling on Graphs for Link Prediction [8.691564173331924]
  Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems. We propose a novel strategy of multi-level negative sampling that enables negative node generation with flexible and controllable hardness'' levels from the latent space. Our method, called Conditional Diffusion-based Multi-level Negative Sampling (DMNS), leverages the Markov chain property of diffusion models to generate negative nodes in multiple levels of variable hardness.
  arXiv  Detail & Related papers  (2024-03-25T23:07:31Z)
• Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias [75.44877675117749]
  We propose an efficient label regularization technique, namely Label Deconvolution (LD), to alleviate the learning bias by a novel and highly scalable approximation to the inverse mapping of GNNs. Experiments demonstrate LD significantly outperforms state-of-the-art methods on Open Graph datasets Benchmark.
  arXiv  Detail & Related papers  (2023-09-26T13:09:43Z)
• Energy-based Out-of-Distribution Detection for Graph Neural Networks [76.0242218180483]
  We propose a simple, powerful and efficient OOD detection model for GNN-based learning on graphs, which we call GNNSafe. GNNSafe achieves up to $17.0%$ AUROC improvement over state-of-the-arts and it could serve as simple yet strong baselines in such an under-developed area.
  arXiv  Detail & Related papers  (2023-02-06T16:38:43Z)
• Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching [68.35685422301613]
  We propose a novel non-parametric subgraph matching framework, dubbed MatchExplainer, to explore explanatory subgraphs. It couples the target graph with other counterpart instances and identifies the most crucial joint substructure by minimizing the node corresponding-based distance. Experiments on synthetic and real-world datasets show the effectiveness of our MatchExplainer by outperforming all state-of-the-art parametric baselines with significant margins.
  arXiv  Detail & Related papers  (2023-01-07T05:14:45Z)
• Relation-aware Graph Attention Model With Adaptive Self-adversarial Training [29.240686573485718]
  This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. We particularly address two building blocks in the pipeline, namely heterogeneous graph representation learning and negative sampling. We introduce a parameter-free negative sampling technique -- adaptive self-adversarial (ASA) negative sampling.
  arXiv  Detail & Related papers  (2021-02-14T16:11:56Z)
• Cascade Network with Guided Loss and Hybrid Attention for Finding Good Correspondences [33.65360396430535]
  Given a putative correspondence set of an image pair, we propose a neural network which finds correct correspondences by a binary-class classifier. We propose a new Guided Loss that can directly use evaluation criterion (Fn-measure) as guidance to dynamically adjust the objective function. We then propose a hybrid attention block to extract feature, which integrates the Bayesian context normalization (BACN) and channel-wise attention (CA)
  arXiv  Detail & Related papers  (2021-01-31T08:33:20Z)
• Structure Aware Negative Sampling in Knowledge Graphs [18.885368822313254]
  A crucial aspect of contrastive learning approaches is the choice of corruption distribution that generates hard negative samples. We propose Structure Aware Negative Sampling (SANS), an inexpensive negative sampling strategy that utilizes the rich graph structure by selecting negative samples from a node's k-hop neighborhood.
  arXiv  Detail & Related papers  (2020-09-23T19:57:00Z)
• Bayesian Graph Neural Networks with Adaptive Connection Sampling [62.51689735630133]
  We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) The proposed framework not only alleviates over-smoothing and over-fitting tendencies of deep GNNs, but also enables learning with uncertainty in graph analytic tasks with GNNs.
  arXiv  Detail & Related papers  (2020-06-07T07:06:35Z)
• Understanding Negative Sampling in Graph Representation Learning [87.35038268508414]
  We show that negative sampling is as important as positive sampling in determining the optimization objective and the resulted variance. We propose Metropolis-Hastings (MCNS) to approximate the positive distribution with self-contrast approximation and accelerate negative sampling by Metropolis-Hastings. We evaluate our method on 5 datasets that cover extensive downstream graph learning tasks, including link prediction, node classification and personalized recommendation.
  arXiv  Detail & Related papers  (2020-05-20T06:25:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.