Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
SHAKE-GNN: Scalable Hierarchical Kirchhoff-Forest Graph Neural Network
Graph Neural Networks (GNNs) have achieved remarkable success across a range of learning tasks. However, scaling GNNs to large graphs remains a significant challenge, especially for graph-level tasks. In this work, we in…
Graph ClassificationGraph Neural NetworkTowards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicology
This research focuses on rational pesticide design, using graph machine learning to accelerate the development of safer, eco-friendly agrochemicals, inspired by in silico methods in drug discovery. With an emphasis on ec…
Graph ClassificationDrug DiscoveryFast, Accurate and Interpretable Graph Classification with Topological Kernels
We introduce a novel class of explicit feature maps based on topological indices that represent each graph by a compact feature vector, enabling fast and interpretable graph classification. Using radial basis function ke…
Graph ClassificationGraph LearningAsynchronous Message Passing for Addressing Oversquashing in Graph Neural Networks
Graph Neural Networks (GNNs) suffer from oversquashing, where structural bottlenecks limit message propagation between distant nodes, hindering tasks that require long-range interactions. Existing remedies are limited: g…
Graph ClassificationGraph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning
Generalizing to unseen graph tasks without task-pecific supervision remains challenging. Graph Neural Networks (GNNs) are limited by fixed label spaces, while Large Language Models (LLMs) lack structural inductive biases…
Reinforcement LearningGraph ClassificationNode ClassificationLink PredictionTwo Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
Graph neural networks (GNNs) are powerful tools on graph data. However, their predictions are mis-calibrated and lack interpretability, limiting their adoption in critical applications. To address this issue, we propose …
Graph ClassificationTowards Faithful Class-level Self-explainability in Graph Neural Networks by Subgraph Dependencies
Enhancing the interpretability of graph neural networks (GNNs) is crucial to ensure their safe and fair deployment. Recent work has introduced self-explainable GNNs that generate explanations as part of training, improvi…
Graph ClassificationGraph Learning via Logic-Based Weisfeiler-Leman Variants and Tabularization
We present a novel approach for graph classification based on tabularizing graph data via new variants of the Weisfeiler-Leman algorithm and then applying methods for tabular data. The variants are obtained by modifying …
Graph ClassificationGraph LearningOver-Squashing in GNNs and Causal Inference of Rewiring Strategies
Graph neural networks (GNNs) have exhibited state-of-the-art performance across wide-range of domains such as recommender systems, material design, and drug repurposing. Yet message-passing GNNs suffer from over-squashin…
Graph ClassificationNode ClassificationCausal InferenceExact Verification of Graph Neural Networks with Incremental Constraint Solving
Graph neural networks (GNNs) are increasingly often employed in high-stakes applications, such as fraud detection or healthcare, but are susceptible to adversarial attacks. A number of techniques have been proposed to pr…
Adversarial RobustnessGraph ClassificationNode ClassificationFraud DetectionTANGO: Graph Neural Dynamics via Learned Energy and Tangential Flows
We introduce TANGO -- a dynamical systems inspired framework for graph representation learning that governs node feature evolution through a learned energy landscape and its associated descent dynamics. At the core of ou…
Graph Representation LearningGraph Neural NetworkGraph ClassificationGraph LearningGraphProp: Training the Graph Foundation Models using Graph Properties
This work focuses on training graph foundation models (GFMs) that have strong generalization ability in graph-level tasks such as graph classification. Effective GFM training requires capturing information consistent acr…
Domain GeneralizationGraph ClassificationFew-Shot LearningSymbolic Graph Intelligence: Hypervector Message Passing for Learning Graph-Level Patterns with Tsetlin Machines
We propose a multilayered symbolic framework for general graph classification that leverages sparse binary hypervectors and Tsetlin Machines. Each graph is encoded through structured message passing, where node, edge, an…
Graph ClassificationGraph Attention Specialized Expert Fusion Model for Node Classification: Based on Cora and Pubmed Datasets
Graph node classification is a fundamental task in graph neural networks (GNNs), aiming to assign predefined class labels to nodes. On the PubMed citation network dataset, we observe significant classification difficulty…
Graph ClassificationNode ClassificationSamGoG: A Sampling-Based Graph-of-Graphs Framework for Imbalanced Graph Classification
Graph Neural Networks (GNNs) have shown remarkable success in graph classification tasks by capturing both structural and feature-based representations. However, real-world graphs often exhibit two critical forms of imba…
Graph ClassificationIncremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures
The escalating threat of cyberattacks on real-time critical infrastructures poses serious risks to public safety, demanding detection methods that effectively capture complex system interdependencies and adapt to evolvin…
Graph ClassificationAnomaly DetectionGraph LearningA Spectral Interpretation of Redundancy in a Graph Reservoir
Reservoir computing has been successfully applied to graphs as a preprocessing method to improve the training efficiency of Graph Neural Networks (GNNs). However, a common issue that arises when repeatedly applying layer…
Graph ClassificationGraph Neural NetworkLearnable quantum spectral filters for hybrid graph neural networks
In this paper, we describe a parameterized quantum circuit that can be considered as convolutional and pooling layers for graph neural networks. The circuit incorporates the parameterized quantum Fourier circuit where th…
Graph ClassificationGraph Neural NetworkDensity-aware Walks for Coordinated Campaign Detection
Coordinated campaigns frequently exploit social media platforms by artificially amplifying topics, making inauthentic trends appear organic, and misleading users into engagement. Distinguishing these coordinated efforts …
Graph ClassificationPositional Encoding meets Persistent Homology on Graphs
The local inductive bias of message-passing graph neural networks (GNNs) hampers their ability to exploit key structural information (e.g., connectivity and cycles). Positional encoding (PE) and Persistent Homology (PH) …
Graph ClassificationGraph Representation LearningInductive BiasOut-of-Distribution Generalization+2