Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
HyperAggregation: Aggregating over Graph Edges with Hypernetworks
HyperAggregation is a hypernetwork-based aggregation function for Graph Neural Networks. It uses a hypernetwork to dynamically generate weights in the size of the current neighborhood, which are then used to aggregate th…
Graph ClassificationGraph RegressionNode ClassificationGraph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks
Graph neural networks (GNNs) are widely applied in graph data modeling. However, existing GNNs are often trained in a task-driven manner that fails to fully capture the intrinsic nature of the graph structure, resulting …
Graph ClassificationNode ClassificationPrompt LearningMolecular Topological Profile (MOLTOP) -- Simple and Strong Baseline for Molecular Graph Classification
We revisit the effectiveness of topological descriptors for molecular graph classification and design a simple, yet strong baseline. We demonstrate that a simple approach to feature engineering - employing histogram aggr…
Feature EngineeringGraph ClassificationRethinking the Effectiveness of Graph Classification Datasets in Benchmarks for Assessing GNNs
Graph classification benchmarks, vital for assessing and developing graph neural networks (GNNs), have recently been scrutinized, as simple methods like MLPs have demonstrated comparable performance. This leads to an imp…
BenchmarkingDataset GenerationGraph ClassificationGraph LearningUnveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining, leading to significant advances across various domains. Stemmed from the node-wise representations of GNNs, existing explanation studie…
ClusteringGraph ClassificationGraph ClusteringGraph MiningCore Knowledge Learning Framework for Graph Adaptation and Scalability Learning
Graph classification is a pivotal challenge in machine learning, especially within the realm of graph-based data, given its importance in numerous real-world applications such as social network analysis, recommendation s…
ClassificationDomain AdaptationFew-Shot LearningGraph Classification+2MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
Recent works have introduced GNN-to-MLP knowledge distillation (KD) frameworks to combine both GNN's superior performance and MLP's fast inference speed. However, existing KD frameworks are primarily designed for node cl…
ClassificationGraph ClassificationKnowledge DistillationNode ClassificationKolmogorov-Arnold Graph Neural Networks
Graph neural networks (GNNs) excel in learning from network-like data but often lack interpretability, making their application challenging in domains requiring transparent decision-making. We propose the Graph Kolmogoro…
Decision MakingGraph ClassificationLink PredictionNode ClassificationKAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood aggregation (a.k.a., message-passing) lay…
Graph ClassificationGraph LearningGraph RegressionKolmogorov-Arnold Networks+2TopoGCL: Topological Graph Contrastive Learning
Graph contrastive learning (GCL) has recently emerged as a new concept which allows for capitalizing on the strengths of graph neural networks (GNNs) to learn rich representations in a wide variety of applications which …
Contrastive LearningGraph ClassificationFast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
We present a new class of fast polylog-linear algorithms based on the theory of structured matrices (in particular low displacement rank) for integrating tensor fields defined on weighted trees. Several applications of t…
Graph ClassificationNext Level Message-Passing with Hierarchical Support Graphs
Message-Passing Neural Networks (MPNNs) are extensively employed in graph learning tasks but suffer from limitations such as the restricted scope of information exchange, by being confined to neighboring nodes during eac…
Graph ClassificationGraph LearningGraph Property PredictionGraph Regression+1Edge Classification on Graphs: New Directions in Topological Imbalance
Recent years have witnessed the remarkable success of applying Graph machine learning (GML) to node/graph classification and link prediction. However, edge classification task that enjoys numerous real-world applications…
ClassificationEdge ClassificationGraph ClassificationLink Prediction+1On GNN explanability with activation rules
GNNs are powerful models based on node representation learning that perform particularly well in many machine learning problems related to graphs. The major obstacle to the deployment of GNNs is mostly a problem of socie…
Graph ClassificationRepresentation LearningRobustness Inspired Graph Backdoor Defense
Graph Neural Networks (GNNs) have achieved promising results in tasks such as node classification and graph classification. However, recent studies reveal that GNNs are vulnerable to backdoor attacks, posing a significan…
backdoor defenseGraph ClassificationNode ClassificationA Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks. Despite the recent surge in graph pooling approaches, there is a lack of standardized ex…
Graph ClassificationGraph RegressionNode ClassificationMotif-driven Subgraph Structure Learning for Graph Classification
To mitigate the suboptimal nature of graph structure, Graph Structure Learning (GSL) has emerged as a promising approach to improve graph structure and boost performance in downstream tasks. Despite the proposal of numer…
ClassificationGraph ClassificationGraph structure learningHow Interpretable Are Interpretable Graph Neural Networks?
Interpretable graph neural networks (XGNNs ) are widely adopted in various scientific applications involving graph-structured data. Existing XGNNs predominantly adopt the attention-based mechanism to learn edge or node i…
Graph ClassificationRethinking the impact of noisy labels in graph classification: A utility and privacy perspective
Graph neural networks based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training dat…
ClassificationContrastive LearningData AugmentationGraph Classification+2GENIE: Watermarking Graph Neural Networks for Link Prediction
Graph Neural Networks (GNNs) have become invaluable intellectual property in graph-based machine learning. However, their vulnerability to model stealing attacks when deployed within Machine Learning as a Service (MLaaS)…
Backdoor AttackDrug DiscoveryGraph ClassificationLink Prediction+3