Papers Graph Classification
“Graph Classification” 태그가 달린 논문 1,025편 · 필터 해제
Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization
Extracting a small subset of crucial rationales from the full input is a key problem in explainability research. The most widely used fundamental criterion for rationale extraction is the maximum mutual information (MMI)…
Graph Classificationtext-classificationText ClassificationStructural Entropy Guided Unsupervised Graph Out-Of-Distribution Detection
With the emerging of huge amount of unlabeled data, unsupervised out-of-distribution (OOD) detection is vital for ensuring the reliability of graph neural networks (GNNs) by identifying OOD samples from in-distribution (…
Contrastive LearningGraph ClassificationOut-of-Distribution DetectionOut of Distribution (OOD) Detection+1Performance Heterogeneity in Graph Neural Networks: Lessons for Architecture Design and Preprocessing
Graph Neural Networks have emerged as the most popular architecture for graph-level learning, including graph classification and regression tasks, which frequently arise in areas such as biochemistry and drug discovery. …
Drug DiscoveryGraph ClassificationLarge Engagement Networks for Classifying Coordinated Campaigns and Organic Twitter Trends
Social media users and inauthentic accounts, such as bots, may coordinate in promoting their topics. Such topics may give the impression that they are organically popular among the public, even though they are astroturfi…
ClassificationGraph ClassificationNeural Network Graph Similarity Computation Based on Graph Fusion
Graph similarity learning, crucial for tasks such as graph classification and similarity search, focuses on measuring the similarity between two graph-structured entities. The core challenge in this field is effectively …
Graph ClassificationGraph SimilarityLearning Backbones: Sparsifying Graphs through Zero Forcing for Effective Graph-Based Learning
This paper introduces a novel framework for graph sparsification that preserves the essential learning attributes of original graphs, improving computational efficiency and reducing complexity in learning algorithms. We …
Computational EfficiencyGraph ClassificationGraph Self-Supervised Learning with Learnable Structural and Positional Encodings
Traditional Graph Self-Supervised Learning (GSSL) struggles to capture complex structural properties well. This limitation stems from two main factors: (1) the inadequacy of conventional Graph Neural Networks (GNNs) in r…
Computational EfficiencyGraph ClassificationSelf-Supervised LearningPre-training Graph Neural Networks on Molecules by Using Subgraph-Conditioned Graph Information Bottleneck
This study aims to build a pre-trained Graph Neural Network (GNN) model on molecules without human annotations or prior knowledge. Although various attempts have been proposed to overcome limitations in acquiring labeled…
Graph ClassificationGraph Neural NetworkGraph RegressionMolecular Property Prediction+1Non-Euclidean Hierarchical Representational Learning using Hyperbolic Graph Neural Networks for Environmental Claim Detection
Transformer-based models dominate NLP tasks like sentiment analysis, machine translation, and claim verification. However, their massive computational demands and lack of interpretability pose challenges for real-world a…
Claim VerificationDependency ParsingGraph ClassificationMachine Translation+2Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin
Graph neural networks are gaining attention in fifth-generation (5G) core network digital twins, which are data-driven complex systems with numerous components. Analyzing these data can be challenging due to rare failure…
Graph ClassificationGraph Neural NetworkGraph Representation Learningimbalanced classification+1Classification of Temporal Graphs using Persistent Homology
Temporal graphs effectively model dynamic systems by representing interactions as timestamped edges. However, analytical tools for temporal graphs are limited compared to static graphs. We propose a novel method for anal…
ClassificationGraph ClassificationUnlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence
Message-passing Graph Neural Networks (GNNs) are often criticized for their limited expressiveness, issues like over-smoothing and over-squashing, and challenges in capturing long-range dependencies, while Graph Transfor…
Graph ClassificationGraph Property PredictionGraph RegressionNode ClassificationBridging Domain Adaptation and Graph Neural Networks: A Tensor-Based Framework for Effective Label Propagation
Graph Neural Networks (GNNs) have recently become the predominant tools for studying graph data. Despite state-of-the-art performance on graph classification tasks, GNNs are overwhelmingly trained in a single domain unde…
Domain AdaptationGraph ClassificationGraph Neural NetworkGraph Neural Networks at a Fraction
Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of graph-structured data. In addition to real-valued GNNs, quaternion GNNs also perform well on tasks on graph-structured data. Wit…
Graph ClassificationLink PredictionNode ClassificationBeyond Message Passing: Neural Graph Pattern Machine
Graph learning tasks often hinge on identifying key substructure patterns -- such as triadic closures in social networks or benzene rings in molecular graphs -- that underpin downstream performance. However, most existin…
Graph ClassificationGraph LearningGraph RegressionLink Prediction+2Molecular Fingerprints Are Strong Models for Peptide Function Prediction
We study the effectiveness of molecular fingerprints for peptide property prediction and demonstrate that domain-specific feature extraction from molecular graphs can outperform complex and computationally expensive mode…
Graph ClassificationGraph RegressionProperty PredictionDAGPrompT: Pushing the Limits of Graph Prompting with a Distribution-aware Graph Prompt Tuning Approach
The pre-train then fine-tune approach has advanced GNNs by enabling general knowledge capture without task-specific labels. However, an objective gap between pre-training and downstream tasks limits its effectiveness. Re…
General KnowledgeGraph ClassificationA Unified Invariant Learning Framework for Graph Classification
Invariant learning demonstrates substantial potential for enhancing the generalization of graph neural networks (GNNs) with out-of-distribution (OOD) data. It aims to recognize stable features in graph data for classific…
ClassificationGraph ClassificationGraph LearningCatch Causal Signals from Edges for Label Imbalance in Graph Classification
Despite significant advancements in causal research on graphs and its application to cracking label imbalance, the role of edge features in detecting the causal effects within graphs has been largely overlooked, leaving …
Graph ClassificationWeakly Supervised Learning on Large Graphs
Graph classification plays a pivotal role in various domains, including pathology, where images can be represented as graphs. In this domain, images can be represented as graphs, where nodes might represent individual nu…
ClassificationGraph AttentionGraph ClassificationWeakly-supervised Learning