Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networks

• URL: http://arxiv.org/abs/2312.09802v2
• Date: Fri, 21 Jun 2024 04:12:56 GMT
• Title: Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networks
• Authors: Xiran Qu, Xuequn Shang, Yupei Zhang,
• Abstract summary: CPRP, concept prerequisite relation prediction, is a fundamental task in using AI for education. We present a permutation-equivariant directed GNN model by introducing the Weisfeiler-Lehman test into directed GNN learning. Our model delivers better prediction performance than the state-of-the-art methods.
• Score: 3.1688996975958306
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper studies the problem of CPRP, concept prerequisite relation prediction, which is a fundamental task in using AI for education. CPRP is usually formulated into a link-prediction task on a relationship graph of concepts and solved by training the graph neural network (GNN) model. However, current directed GNNs fail to manage graph isomorphism which refers to the invariance of non-isomorphic graphs, reducing the expressivity of resulting representations. We present a permutation-equivariant directed GNN model by introducing the Weisfeiler-Lehman test into directed GNN learning. Our method is then used for CPRP and evaluated on three public datasets. The experimental results show that our model delivers better prediction performance than the state-of-the-art methods.

Related papers

• DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment [57.62885438406724]
  Graph neural networks are recognized for their strong performance across various applications. BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. We propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning.
  arXiv  Detail & Related papers  (2024-06-04T07:24:51Z)
• GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks [11.110435047801506]
  We propose a variance-preserving aggregation function (VPA) that maintains expressivity, but yields improved forward and backward dynamics. Our results could pave the way towards normalizer-free or self-normalizing GNNs.
  arXiv  Detail & Related papers  (2024-03-07T18:52:27Z)
• Learning to Reweight for Graph Neural Network [63.978102332612906]
  Graph Neural Networks (GNNs) show promising results for graph tasks. Existing GNNs' generalization ability will degrade when there exist distribution shifts between testing and training graph data. We propose a novel nonlinear graph decorrelation method, which can substantially improve the out-of-distribution generalization ability.
  arXiv  Detail & Related papers  (2023-12-19T12:25:10Z)
• Self-supervision meets kernel graph neural models: From architecture to augmentations [36.388069423383286]
  We improve the design and learning of kernel graph neural networks (KGNNs) We develop a novel structure-preserving graph data augmentation method called latent graph augmentation (LGA) Our proposed model achieves competitive performance comparable to or sometimes outperforming state-of-the-art graph representation learning frameworks.
  arXiv  Detail & Related papers  (2023-10-17T14:04:22Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• MentorGNN: Deriving Curriculum for Pre-Training GNNs [61.97574489259085]
  We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs. We shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs.
  arXiv  Detail & Related papers  (2022-08-21T15:12:08Z)
• Invertible Neural Networks for Graph Prediction [22.140275054568985]
  In this work, we address conditional generation using deep invertible neural networks. We adopt an end-to-end training approach since our objective is to address prediction and generation in the forward and backward processes at once.
  arXiv  Detail & Related papers  (2022-06-02T17:28:33Z)
• Graph Neural Networks with Parallel Neighborhood Aggregations for Graph Classification [14.112444998191698]
  We focus on graph classification using a graph neural network (GNN) model that precomputes the node features using a bank of neighborhood aggregation graph operators arranged in parallel. These GNN models have a natural advantage of reduced training and inference time due to the precomputations. We demonstrate via numerical experiments that the developed model achieves state-of-the-art performance on many diverse real-world datasets.
  arXiv  Detail & Related papers  (2021-11-22T19:19:40Z)
• Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks [33.20913249848369]
  Graph Neural Networks (GNNs) are deep learning models that take graph data as inputs, and they are applied to various tasks such as traffic prediction and molecular property prediction. We extend explainability methods for CNNs, such as Local Interpretable Model-Agnostic Explanations (LIME), Gradient-Based Saliency Maps, and Gradient-Weighted Class Activation Mapping (Grad-CAM) to GNNs. The experimental results indicate that the LIME-based approach is the most efficient explainability method for multiple tasks in the real-world situation, outperforming even the state-of-the
  arXiv  Detail & Related papers  (2021-11-01T06:27:29Z)
• Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking [63.49779304362376]
  Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models. We introduce a post-hoc method for interpreting the predictions of GNNs which identifies unnecessary edges. We show that we can drop a large proportion of edges without deteriorating the performance of the model.
  arXiv  Detail & Related papers  (2020-10-01T17:51:19Z)

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.