Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,024편 · 필터 해제
Global to Local: Topology-Preserving Adaptive Graph Pooling via Granular-Ball
Graph pooling aims to compress the graph, including both node embeddings and their underlying topological patterns, into a more compact representation. Previous works focus primarily on the overly fine-grained representa…
Graph ClassificationPhysics-Aware Random Walk Fingerprints for Scalable Power Grid Graph Classification
Recent benchmarks such as PowerGraph provide large collections of power-grid graphs for cascading-failure classification. Graph neural networks (GNNs) achieve strong predictive performance on this task, but typically req…
Graph ClassificationInductive Correlation Clustering with Graph Neural Networks
Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and does not require a pre-specified number of clusters. Given $n$ objects …
Graph ClassificationBoosting Data Augmentation with Stochastic Weight Averaging
The symmetries of a learning task have become an important factor in designing modern deep learning solutions. Data augmentation is a straightforward and effective way of incorporating symmetries into a generic neural ne…
Graph ClassificationData AugmentationHP-JEPA: Hierarchical Partitioning for Multi-Resolution Graph Joint-Embedding Predictive Learning
Graph self-supervised learning aims to learn transferable representations from large-scale unlabeled graph data. Joint-embedding predictive architectures (JEPAs) avoid explicit negative-pair construction and raw-input re…
Graph Representation LearningSelf-Supervised LearningGraph ClassificationGraph RegressionTopoFormer: Topology Meets Attention for Graph Learning
We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences. At the core of our method is Topo-Scan, a novel module…
Molecular Property PredictionGraph Representation LearningGraph ClassificationGraph LearningTowards Faithful Graph Explanations with Synergistic Edge Effects via Granular Balls
Instance-level explanations aim to reveal the rationale behind a model's decisions for a specific graph. Previous methods explain graph neural networks (GNNs) by selecting important edges to induce subgraphs, where edge …
Graph ClassificationEdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy
Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information.…
Graph ClassificationNode ClassificationGraph LearningTowards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution
Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive model predictions can enhance trustwort…
Node Property PredictionGraph ClassificationLink PredictionGraph Neural Networks for the Graphical Bootstrap
We study a graph classification problem involving over 20 million graphs, arising from high-order perturbative computations of correlators in planar $\mathcal{N}=4$ super-Yang--Mills, a model closely related to the theor…
Graph ClassificationGraph Classification via Network Usable Information: From Representation Evaluation to Structure Selection
We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning. Unlike conventional graph neural network approaches that rely on end-to-end t…
Graph ClassificationGraph Neural NetworkLearning Graphs through Continuous Information Entropy Fields
Graph theory is inherently descriptive, capturing what relationships exist but not why they arise, because it treats edges as primitive constructs. This paper proposes a new explanatory framework for graph learning, wher…
Graph ClassificationNode ClassificationGraph LearningA Completion-Aware Framework for Impactful Counterfactual Explainability in Graph Neural Networks
In this study, we propose a novel pipeline for generic, model-agnostic, local-level counterfactual explainability in graph neural networks (GNNs). Although counterfactual explainers capable of both adding and removing ed…
Explanation GenerationGraph ClassificationLink PredictionControlled Dynamics Attractor Transformer
Transformer architectures have dramatically advanced representation learning and inference in deep models through self-attention mechanisms. In parallel,associative memory (AM) frameworks map representations onto energy …
Representation LearningGraph Anomaly DetectionGraph ClassificationPAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis
Whilst the vulnerability of graph neural networks (GNNs) to adversarial attacks poses a critical threat to graph representation learning, the understanding of the robust generalization behavior remains a fundamental chal…
Graph Representation LearningAdversarial RobustnessGraph ClassificationConvex Distance Operator Transport: A Convex and Geometry-Preserving Formulation
We introduce Convex Distance Operator Transport (CDOT), the first convex optimal transport framework that aligns distributions across heterogeneous domains by jointly preserving feature correspondence and intrinsic geome…
Graph ClassificationPoint CloudsAbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
Graph classification is a core task in graph data mining with widespread real-world applications. Recent advances in graph neural networks (GNNs) have led to substantial performance improvements for graph classification.…
Graph ClassificationCan Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
Graph Machine Learning as a Service (GMLaaS) platforms increasingly implement explainability interfaces to meet regulatory transparency requirements. However, this transparency creates exploitable vulnerabilities for mod…
Graph ClassificationModel extractionA Generalized Tikhonov Layer for Interpretable-by-design Graph Neural Networks
We propose the Tikhonov layer, a graph neural network layer that is interpretable by design: once trained, its learned parameters directly reveal which node features and which aspects of the graph topology were leveraged…
Graph ClassificationGraph Neural NetworkDistance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
Gromov--Wasserstein (GW) distances compare graphs, shapes, and point clouds through internal distances, without requiring a common coordinate system. This invariance is powerful, but discrete GW is a nonconvex quadratic …
Graph ClassificationTwo-sample testingPoint Clouds