Explainable Graph Representation Learning via Graph Pattern Analysis

• URL: http://arxiv.org/abs/2512.04530v1
• Date: Thu, 04 Dec 2025 07:25:01 GMT
• Title: Explainable Graph Representation Learning via Graph Pattern Analysis
• Authors: Xudong Wang, Ziheng Sun, Chris Ding, Jicong Fan,
• Abstract summary: We introduce a framework for learning and explaining graph representations through graph pattern analysis.<n>We show how to learn and explain graph representations for real-world data using pattern analysis.
• Score: 33.539251667469294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explainable artificial intelligence (XAI) is an important area in the AI community, and interpretability is crucial for building robust and trustworthy AI models. While previous work has explored model-level and instance-level explainable graph learning, there has been limited investigation into explainable graph representation learning. In this paper, we focus on representation-level explainable graph learning and ask a fundamental question: What specific information about a graph is captured in graph representations? Our approach is inspired by graph kernels, which evaluate graph similarities by counting substructures within specific graph patterns. Although the pattern counting vector can serve as an explainable representation, it has limitations such as ignoring node features and being high-dimensional. To address these limitations, we introduce a framework (PXGL-GNN) for learning and explaining graph representations through graph pattern analysis. We start by sampling graph substructures of various patterns. Then, we learn the representations of these patterns and combine them using a weighted sum, where the weights indicate the importance of each graph pattern's contribution. We also provide theoretical analyses of our methods, including robustness and generalization. In our experiments, we show how to learn and explain graph representations for real-world data using pattern analysis. Additionally, we compare our method against multiple baselines in both supervised and unsupervised learning tasks to demonstrate its effectiveness.

Related papers

• GraphXAIN: Narratives to Explain Graph Neural Networks [0.0]
  Graph Neural Networks (GNNs) are a powerful technique for machine learning on graph-structured data.<n>Existing GNN explanation methods yield technical outputs that are difficult for non-data scientists to understand.<n>We propose GraphXAIN, a method that generates natural language narratives explaining GNN predictions.
  arXiv  Detail & Related papers  (2024-11-04T19:21:06Z)
• Knowledge Probing for Graph Representation Learning [12.960185655357495]
  We propose a novel graph probing framework (GraphProbe) to investigate and interpret whether the family of graph learning methods has encoded different levels of knowledge in graph representation learning. Based on the intrinsic properties of graphs, we design three probes to systematically investigate the graph representation learning process from different perspectives. We construct a thorough evaluation benchmark with nine representative graph learning methods from random walk based approaches, basic graph neural networks and self-supervised graph methods, and probe them on six benchmark datasets for node classification, link prediction and graph classification.
  arXiv  Detail & Related papers  (2024-08-07T16:27:45Z)
• PlanE: Representation Learning over Planar Graphs [9.697671872347131]
  This work is inspired by the classical planar graph isomorphism algorithm of Hopcroft and Tarjan. PlanE includes architectures which can learn complete invariants over planar graphs while remaining practically scalable.
  arXiv  Detail & Related papers  (2023-07-03T17:45:01Z)
• Probing Graph Representations [77.7361299039905]
  We use a probing framework to quantify the amount of meaningful information captured in graph representations. Our findings on molecular datasets show the potential of probing for understanding the inductive biases of graph-based models. We advocate for probing as a useful diagnostic tool for evaluating graph-based models.
  arXiv  Detail & Related papers  (2023-03-07T14:58:18Z)
• State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
  Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
  arXiv  Detail & Related papers  (2023-01-14T09:15:49Z)
• Learning node embeddings via summary graphs: a brief theoretical analysis [55.25628709267215]
  Graph representation learning plays an important role in many graph mining applications, but learning embeddings of large-scale graphs remains a problem. Recent works try to improve scalability via graph summarization -- i.e., they learn embeddings on a smaller summary graph, and then restore the node embeddings of the original graph. We give an in-depth theoretical analysis of three specific embedding learning methods based on introduced kernel matrix.
  arXiv  Detail & Related papers  (2022-07-04T04:09:50Z)
• Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning [84.35102534158621]
  We study pre-trained language models that generate explanation graphs in an end-to-end manner. We propose simple yet effective ways of graph perturbations via node and edge edit operations. Our methods lead to significant improvements in both structural and semantic accuracy of explanation graphs.
  arXiv  Detail & Related papers  (2022-04-11T00:58:27Z)
• Graph Self-supervised Learning with Accurate Discrepancy Learning [64.69095775258164]
  We propose a framework that aims to learn the exact discrepancy between the original and the perturbed graphs, coined as Discrepancy-based Self-supervised LeArning (D-SLA) We validate our method on various graph-related downstream tasks, including molecular property prediction, protein function prediction, and link prediction tasks, on which our model largely outperforms relevant baselines.
  arXiv  Detail & Related papers  (2022-02-07T08:04:59Z)
• Graph2Graph Learning with Conditional Autoregressive Models [8.203106789678397]
  We present a conditional auto-re model for graph-to-graph learning. We illustrate its representational capabilities via experiments on challenging subgraph predictions from graph algorithmics.
  arXiv  Detail & Related papers  (2021-06-06T20:28:07Z)
• Non-Parametric Graph Learning for Bayesian Graph Neural Networks [35.88239188555398]
  We propose a novel non-parametric graph model for constructing the posterior distribution of graph adjacency matrices. We demonstrate the advantages of this model in three different problem settings: node classification, link prediction and recommendation.
  arXiv  Detail & Related papers  (2020-06-23T21:10:55Z)
• Deep Learning for Learning Graph Representations [58.649784596090385]
  Mining graph data has become a popular research topic in computer science. The huge amount of network data has posed great challenges for efficient analysis. This motivates the advent of graph representation which maps the graph into a low-dimension vector space.
  arXiv  Detail & Related papers  (2020-01-02T02:13:28Z)

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.