Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Weisfeiler and Leman Follow the Arrow of Time: Expressive Power of Message Passing in Temporal Event Graphs
An important characteristic of temporal graphs is how the directed arrow of time influences their causal topology, i.e., which nodes can possibly influence each other causally via time-respecting paths. The resulting pat…
Graph ClassificationImproving the Effective Receptive Field of Message-Passing Neural Networks
Message-Passing Neural Networks (MPNNs) have become a cornerstone for processing and analyzing graph-structured data. However, their effectiveness is often hindered by phenomena such as over-squashing, where long-range d…
Graph ClassificationGraph RegressionNode ClassificationGraph Style Transfer for Counterfactual Explainability
Counterfactual explainability seeks to uncover model decisions by identifying minimal changes to the input that alter the predicted outcome. This task becomes particularly challenging for graph data due to preserving str…
counterfactualCounterfactual ExplanationGraph ClassificationStyle Transfer+1Scalable Graph Generative Modeling via Substructure Sequences
Graph neural networks (GNNs) has been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their success, message-passing suffers from fu…
Graph ClassificationGraph LearningNode ClassificationTransfer LearningAddressing the Scarcity of Benchmarks for Graph XAI
While Graph Neural Networks (GNNs) have become the de facto model for learning from structured data, their decisional process remains opaque to the end user, undermining their deployment in safety-critical applications. …
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Graph ClassificationSchreier-Coset Graph Propagation
Graph Neural Networks (GNNs) offer a principled framework for learning over graph-structured data, yet their expressive capacity is often hindered by over-squashing, wherein information from distant nodes is compressed i…
Computational EfficiencyGraph ClassificationRhomboid Tiling for Geometric Graph Deep Learning
Graph Neural Networks (GNNs) have proven effective for learning from graph-structured data through their neighborhood-based message passing framework. Many hierarchical graph clustering pooling methods modify this framew…
ClusteringDeep LearningGraph ClassificationGraph ClusteringEfficient Mixed Precision Quantization in Graph Neural Networks
Graph Neural Networks (GNNs) have become essential for handling large-scale graph applications. However, the computational demands of GNNs necessitate the development of efficient methods to accelerate inference. Mixed p…
Graph ClassificationNode ClassificationQuantizationGraffe: Graph Representation Learning via Diffusion Probabilistic Models
Diffusion probabilistic models (DPMs), widely recognized for their potential to generate high-quality samples, tend to go unnoticed in representation learning. While recent progress has highlighted their potential for ca…
DenoisingGraph ClassificationGraph Representation LearningRepresentation LearningDPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing
Hyperdimensional Computing (HDC) is emerging as a promising approach for edge AI, offering a balance between accuracy and efficiency. However, current HDC-based applications often rely on high-precision models and/or enc…
Graph ClassificationAdversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets
This study investigates the self-rationalization framework constructed with a cooperative game, where a generator initially extracts the most informative segment from raw input, and a subsequent predictor utilizes the se…
Graph Classificationtext-classificationText ClassificationGraph Fourier Transformer with Structure-Frequency Information
Graph Transformers (GTs) have shown advantages in numerous graph structure tasks but their self-attention mechanism ignores the generalization bias of graphs, with existing methods mainly compensating for this bias from …
Graph ClassificationInductive BiasNode ClassificationGraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learning
The accurate categorization of compounds within the anatomical therapeutic chemical (ATC) system is fundamental for drug development and fundamental research. Although this area has garnered significant research focus fo…
Drug ATC ClassificationGraph ClassificationGraph LearningMolecular Property Prediction+2LGRPool: Hierarchical Graph Pooling Via Local-Global Regularisation
Hierarchical graph pooling(HGP) are designed to consider the fact that conventional graph neural networks(GNN) are inherently flat and are also not multiscale. However, most HGP methods suffer not only from lack of consi…
Graph ClassificationInfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data
Diffusion wavelets extract information from graph signals at different scales of resolution by utilizing graph diffusion operators raised to various powers, known as diffusion scales. Traditionally, the diffusion scales …
Graph ClassificationAugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
How can we accurately classify graphs? Graph classification is a pivotal task in data mining with applications in social network analysis, web analysis, drug discovery, molecular property prediction, etc. Graph neural ne…
DiversityDrug DiscoveryGraph ClassificationGraph Representation Learning+4Deterministic Certification of Graph Neural Networks against Graph Poisoning Attacks with Arbitrary Perturbations
Graph neural networks (GNNs) are becoming the de facto method to learn on the graph data and have achieved the state-of-the-art on node and graph classification tasks. However, recent works show GNNs are vulnerable to tr…
Graph ClassificationGraph-Level Label-Only Membership Inference Attack against Graph Neural Networks
Graph neural networks (GNNs) are widely used for graph-structured data but are vulnerable to membership inference attacks (MIAs) in graph classification tasks, which determine if a graph was part of the training dataset,…
Graph ClassificationInference AttackMembership Inference AttackPredictionNetwork Embedding Exploration Tool (NEExT)
Many real-world and artificial systems and processes can be represented as graphs. Some examples of such systems include social networks, financial transactions, supply chains, and molecular structures. In many of these …
Graph ClassificationNetwork EmbeddingA Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have shown excellent performance in graph-structured tasks such as node classification and graph classification. However, recent research has shown that GCNs are vulnerable to a new ty…
Backdoor AttackGraph ClassificationNode Classification