A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs

• URL: http://arxiv.org/abs/2412.05114v1
• Date: Fri, 06 Dec 2024 15:15:18 GMT
• Title: A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
• Authors: Patrick Betz, Nathanael Stelzner, Christian Meilicke, Heiner Stuckenschmidt, Christian Bartelt,
• Abstract summary: We investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion.<n>For the two most common benchmarks, we find that a substantial fraction of the performance difference can be explained by one unique negative pattern on each dataset.
• Score: 10.22476128465699
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this technical report, we investigate the predictive performance differences of a rule-based approach and the GNN architectures NBFNet and A*Net with respect to knowledge graph completion. For the two most common benchmarks, we find that a substantial fraction of the performance difference can be explained by one unique negative pattern on each dataset that is hidden from the rule-based approach. Our findings add a unique perspective on the performance difference of different model classes for knowledge graph completion: Models can achieve a predictive performance advantage by penalizing scores of incorrect facts opposed to providing high scores for correct facts.

Related papers

• DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization [44.291382840373]
  This paper addresses the challenge of out-of-distribution generalization in graph machine learning. Traditional graph learning algorithms falter in real-world scenarios where this assumption fails. A principal factor contributing to this suboptimal performance is the inherent simplicity bias of neural networks.
  arXiv  Detail & Related papers  (2024-08-08T12:08:55Z)
• Improving the interpretability of GNN predictions through conformal-based graph sparsification [9.550589670316523]
  Graph Neural Networks (GNNs) have achieved state-of-the-art performance in solving graph classification tasks. We propose a GNN emphtraining approach that finds the most predictive subgraph by removing edges and/or nodes. We rely on reinforcement learning to solve the resulting bi-level optimization with a reward function based on conformal predictions.
  arXiv  Detail & Related papers  (2024-04-18T17:34:47Z)
• GOODAT: Towards Test-time Graph Out-of-Distribution Detection [103.40396427724667]
  Graph neural networks (GNNs) have found widespread application in modeling graph data across diverse domains. Recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN. This paper introduces a data-centric, unsupervised, and plug-and-play solution that operates independently of training data and modifications of GNN architecture.
  arXiv  Detail & Related papers  (2024-01-10T08:37:39Z)
• Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis [18.11743347414004]
  Rule-based methods significantly outperform state-of-the-art methods based on Graph Neural Networks (GNNs) We study a number of variants of a rule-based approach, which are specifically aimed at addressing the aforementioned issues. We find that the resulting models can achieve a performance which is close to that of NBFNet.
  arXiv  Detail & Related papers  (2023-08-14T21:01:29Z)
• Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis [50.972595036856035]
  We present a code that successfully replicates results from six popular and recent graph recommendation models. We compare these graph models with traditional collaborative filtering models that historically performed well in offline evaluations. By investigating the information flow from users' neighborhoods, we aim to identify which models are influenced by intrinsic features in the dataset structure.
  arXiv  Detail & Related papers  (2023-08-01T09:31:44Z)
• Studying How to Efficiently and Effectively Guide Models with Explanations [52.498055901649025]
  'Model guidance' is the idea of regularizing the models' explanations to ensure that they are "right for the right reasons" We conduct an in-depth evaluation across various loss functions, attribution methods, models, and 'guidance depths' on the PASCAL VOC 2007 and MS COCO 2014 datasets. Specifically, we guide the models via bounding box annotations, which are much cheaper to obtain than the commonly used segmentation masks.
  arXiv  Detail & Related papers  (2023-03-21T15:34:50Z)
• Sub-Graph Learning for Spatiotemporal Forecasting via Knowledge Distillation [22.434970343698676]
  We present a new framework called KD-SGL to effectively learn the sub-graphs. We define one global model to learn the overall structure of the graph and multiple local models for each sub-graph.
  arXiv  Detail & Related papers  (2022-11-17T18:02:55Z)
• Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
  We present a comprehensive study on graph embedded few-shot learning. We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels. Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
  arXiv  Detail & Related papers  (2021-02-14T05:28:13Z)
• Combining Label Propagation and Simple Models Out-performs Graph Neural Networks [52.121819834353865]
  We show that for many standard transductive node classification benchmarks, we can exceed or match the performance of state-of-the-art GNNs. We call this overall procedure Correct and Smooth (C&S) Our approach exceeds or nearly matches the performance of state-of-the-art GNNs on a wide variety of benchmarks.
  arXiv  Detail & Related papers  (2020-10-27T02:10:52Z)
• Explaining and Improving Model Behavior with k Nearest Neighbor Representations [107.24850861390196]
  We propose using k nearest neighbor representations to identify training examples responsible for a model's predictions. We show that kNN representations are effective at uncovering learned spurious associations. Our results indicate that the kNN approach makes the finetuned model more robust to adversarial inputs.
  arXiv  Detail & Related papers  (2020-10-18T16:55:25Z)

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.