Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Transforming PageRank into an Infinite-Depth Graph Neural Network
Popular graph neural networks are shallow models, despite the success of very deep architectures in other application domains of deep learning. This reduces the modeling capacity and leaves models unable to capture long-…
Graph ClassificationGraph Neural NetworkBAGEL: A Benchmark for Assessing Graph Neural Network Explanations
The problem of interpreting the decisions of machine learning is a well-researched and important. We are interested in a specific type of machine learning model that deals with graph data called graph neural networks. Ev…
BIG-bench Machine LearningGraph ClassificationGraph Neural NetworkStructural Entropy Guided Graph Hierarchical Pooling
Following the success of convolution on non-Euclidean space, the corresponding pooling approaches have also been validated on various tasks regarding graphs. However, because of the fixed compression quota and stepwise p…
Classificationglobal-optimizationGraph ClassificationNode ClassificationSimilarity-aware Positive Instance Sampling for Graph Contrastive Pre-training
Graph instance contrastive learning has been proved as an effective task for Graph Neural Network (GNN) pre-training. However, one key issue may seriously impede the representative power in existing works: Positive insta…
Contrastive LearningGraph ClassificationGraph Neural NetworkNode ClassificationAgent-based Graph Neural Networks
We present a novel graph neural network we call AgentNet, which is designed specifically for graph-level tasks. AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of th…
Graph ClassificationGraph Neural NetworkTowards OOD Detection in Graph Classification from Uncertainty Estimation Perspective
The problem of out-of-distribution detection for graph classification is far from being solved. The existing models tend to be overconfident about OOD examples or completely ignore the detection task. In this work, we co…
ClassificationGraph ClassificationOut-of-Distribution DetectionOut of Distribution (OOD) Detection0/1 Deep Neural Networks via Block Coordinate Descent
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsic characteristics (e.g., discontinuity a…
10-shot image generation16k2D Object Detection+92Boosting Graph Structure Learning with Dummy Nodes
With the development of graph kernels and graph representation learning, many superior methods have been proposed to handle scalability and oversmoothing issues on graph structure learning. However, most of those strateg…
Graph ClassificationGraph Representation LearningGraph structure learningRepresentation LearningLong Range Graph Benchmark
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer. In principle, such networks are not abl…
BenchmarkingGraph ClassificationGraph LearningGraph Regression+2DiffWire: Inductive Graph Rewiring via the Lovász Bound
Graph Neural Networks (GNNs) have been shown to achieve competitive results to tackle graph-related tasks, such as node and graph classification, link prediction and node and graph clustering in a variety of domains. Mos…
Graph ClassificationNode ClassificationSemi-Supervised Hierarchical Graph Classification
Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph usually represents a real-world entity, e…
ClassificationGraph ClassificationGraph LearningNode ClassificationFundamental Limits in Formal Verification of Message-Passing Neural Networks
Output reachability and adversarial robustness are among the most relevant safety properties of neural networks. We show that in the context of Message Passing Neural Networks (MPNN), a common Graph Neural Network (GNN) …
Adversarial RobustnessClassificationGraph ClassificationGraph Neural Network+2Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over traditional heuristic methods in various tasks such as node c…
Graph ClassificationLink PredictionNode ClassificationPredictionMetric Based Few-Shot Graph Classification
Many modern deep-learning techniques do not work without enormous datasets. At the same time, several fields demand methods working in scarcity of data. This problem is even more complex when the samples have varying str…
ClassificationData AugmentationFew-Shot LearningGraph Classification+3A Simple yet Effective Method for Graph Classification
In deep neural networks, better results can often be obtained by increasing the complexity of previously developed basic models. However, it is unclear whether there is a way to boost performance by decreasing the comple…
ClassificationGraph ClassificationShortest Path Networks for Graph Property Prediction
Most graph neural network models rely on a particular message passing paradigm, where the idea is to iteratively propagate node representations of a graph to each node in the direct neighborhood. While very prominent, th…
Graph ClassificationGraph Neural NetworkGraph Property PredictionPrediction+1Multi-scale Wasserstein Shortest-path Graph Kernels for Graph Classification
Graph kernels are conventional methods for computing graph similarities. However, the existing R-convolution graph kernels cannot resolve both of the two challenges: 1) Comparing graphs at multiple different scales, and …
Graph ClassificationApproximate Network Motif Mining Via Graph Learning
Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets. However, the high computational complexity of identifying motif sets in arbitrary datasets (motif m…
BIG-bench Machine LearningGraph ClassificationGraph LearningAutomatic Relation-aware Graph Network Proliferation
Graph neural architecture search has sparked much attention as Graph Neural Networks (GNNs) have shown powerful reasoning capability in many relational tasks. However, the currently used graph search space overemphasizes…
Graph ClassificationGraph LearningGraph RegressionLink Prediction+3Template based Graph Neural Network with Optimal Transport Distances
Current Graph Neural Networks (GNN) architectures generally rely on two important components: node features embedding through message passing, and aggregation with a specialized form of pooling. The structural (or topolo…
Graph ClassificationGraph MatchingGraph Neural Network