Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Analysis and Approximate Inference of Large Random Kronecker Graphs
Random graph models are playing an increasingly important role in various fields ranging from social networks, telecommunication systems, to physiologic and biological networks. Within this landscape, the random Kronecke…
Graph ClassificationRecommendation SystemsSelf-supervised Learning and Graph Classification under Heterophily
Self-supervised learning has shown its promising capability in graph representation learning in recent work. Most existing pre-training strategies usually choose the popular Graph neural networks (GNNs), which can be see…
ClassificationGraph ClassificationGraph Representation LearningMolecular Property Prediction+4Explainable and Position-Aware Learning in Digital Pathology
Encoding whole slide images (WSI) as graphs is well motivated since it makes it possible for the gigapixel resolution WSI to be represented in its entirety for the purpose of graph learning. To this end, WSIs can be brok…
Graph AttentionGraph ClassificationGraph LearningPosition+1Graph Mixup with Soft Alignments
We study graph data augmentation by mixup, which has been used successfully on images. A key operation of mixup is to compute a convex combination of a pair of inputs. This operation is straightforward for grid-like data…
Data AugmentationGraph ClassificationPath Neural Networks: Expressive and Accurate Graph Neural Networks
Graph neural networks (GNNs) have recently become the standard approach for learning with graph-structured data. Prior work has shed light into their potential, but also their limitations. Unfortunately, it was shown tha…
Graph ClassificationGraph RegressionCoCo: A Coupled Contrastive Framework for Unsupervised Domain Adaptive Graph Classification
Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to …
Contrastive LearningDomain AdaptationGraph ClassificationGraph Representation Learning+1XInsight: Revealing Model Insights for GNNs with Flow-based Explanations
Progress in graph neural networks has grown rapidly in recent years, with many new developments in drug discovery, medical diagnosis, and recommender systems. While this progress is significant, many networks are `black …
Drug DiscoveryGraph ClassificationMedical DiagnosisRecommendation SystemsCIN++: Enhancing Topological Message Passing
Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, they face significant limitations in expressive power, struggling with long-range interactions and lackin…
Graph ClassificationGraph RegressionGraph Classification Gaussian Processes via Spectral Features
Graph classification aims to categorise graphs based on their structure and node attributes. In this work, we propose to tackle this task using tools from graph signal processing by deriving spectral features, which we t…
ClassificationDecision MakingGaussian ProcessesGraph ClassificationRandomized Schur Complement Views for Graph Contrastive Learning
We introduce a randomized topological augmentor based on Schur complements for Graph Contrastive Learning (GCL). Given a graph laplacian matrix, the technique generates unbiased approximations of its Schur complements an…
Contrastive LearningGraph ClassificationNode ClassificationExplaining and Adapting Graph Conditional Shift
Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution shift. There is still significant ambiguit…
Domain AdaptationGraph ClassificationNode ClassificationUnsupervised Domain AdaptationLearning Probabilistic Symmetrization for Architecture Agnostic Equivariance
We present a novel framework to overcome the limitations of equivariant architectures in learning functions with group symmetries. In contrary to equivariant architectures, we use an arbitrary base model such as an MLP o…
Graph ClassificationGraph RegressionLink PredictionMessage-passing selection: Towards interpretable GNNs for graph classification
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines. Unlike the most existing expla…
ClassificationGraph ClassificationEPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structure. To address this issue, we propose EP…
Data AugmentationGraph ClassificationTowards Semi-supervised Universal Graph Classification
Graph neural networks have pushed state-of-the-arts in graph classifications recently. Typically, these methods are studied within the context of supervised end-to-end training, which necessities copious task-specific la…
ClassificationGraph ClassificationGraph Neural NetworkIs Rewiring Actually Helpful in Graph Neural Networks?
Graph neural networks compute node representations by performing multiple message-passing steps that consist in local aggregations of node features. Having deep models that can leverage longer-range interactions between …
Graph ClassificationGraph Inductive Biases in Transformers without Message Passing
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing …
Graph ClassificationGraph RegressionInductive BiasNode ClassificationGIMM: InfoMin-Max for Automated Graph Contrastive Learning
Graph contrastive learning (GCL) shows great potential in unsupervised graph representation learning. Data augmentation plays a vital role in GCL, and its optimal choice heavily depends on the downstream task. Many GCL m…
Contrastive LearningData AugmentationGraph ClassificationGraph Representation Learning+1Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies
Despite the increasing relevance of explainable AI, assessing the quality of explanations remains a challenging issue. Due to the high costs associated with human-subject experiments, various proxy metrics are often used…
Graph ClassificationRobust Ante-hoc Graph Explainer using Bilevel Optimization
Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions. This is particularly true in the case of models f…
AttributeBilevel OptimizationGraph Classification