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0 results for “graph neural networks”
From Abductive Explanations to Global Logical Rules for Node Classification in SGCs
A new research paper introduces a logic-based framework that extracts compact, globally applicable logical rules from Simple Graph Convolution (SGC) models by using minimal abductive explanations as an intermediate step, aiming to improve explainability without sacrificing fidelity.
Aug 19, 2026
Exploring Oversmoothing with Householder Matrices
A new graph neural network architecture called HouseGNN is proposed to mitigate oversmoothing in deep GNNs by using Householder reflectors and GroupSort to preserve node-wise Euclidean norms across layers.
Aug 14, 2026
Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey
A new arXiv preprint (2607.09666v1) publishes a comprehensive, taxonomy-driven survey of Graph Neural Network (GNN) applications across the full knowledge graph (KG) technology lifecycle — from construction to reasoning to applications — identifying gaps, strengths, limitations, and future research directions.
Jul 14, 2026
PRecG: Legal Precedent Retrieval with Graph Neural Networks and Rhetorical Role Segmentation
A new AI research paper proposes PRecG, a graph-based method for legal precedent retrieval that segments judgments by rhetorical role and builds knowledge graphs per segment to improve semantic matching.
Jul 13, 2026