Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
AnyGraph: Graph Foundation Model in the Wild
The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities. However, current approaches often struggle to effectively extr…
Graph LearningMixture-of-ExpertsmodelZero-Shot LearningAsymmetric Graph Error Control with Low Complexity in Causal Bandits
In this paper, the causal bandit problem is investigated, in which the objective is to select an optimal sequence of interventions on nodes in a causal graph. It is assumed that the graph is governed by linear structural…
Change DetectionGraph LearningFederated Graph Learning with Structure Proxy Alignment
Federated Graph Learning (FGL) aims to learn graph learning models over graph data distributed in multiple data owners, which has been applied in various applications such as social recommendation and financial fraud det…
Federated LearningFraud DetectionGraph LearningNode ClassificationE-CGL: An Efficient Continual Graph Learner
Continual learning has emerged as a crucial paradigm for learning from sequential data while preserving previous knowledge. In the realm of continual graph learning, where graphs continuously evolve based on streaming gr…
Continual LearningGraph LearningGrassNet: State Space Model Meets Graph Neural Network
Designing spectral convolutional networks is a formidable task in graph learning. In traditional spectral graph neural networks (GNNs), polynomial-based methods are commonly used to design filters via the Laplacian matri…
Graph LearningGraph Neural NetworkmodelState Space ModelsCorrAdaptor: Adaptive Local Context Learning for Correspondence Pruning
In the fields of computer vision and robotics, accurate pixel-level correspondences are essential for enabling advanced tasks such as structure-from-motion and simultaneous localization and mapping. Recent correspondence…
Graph LearningSimultaneous Localization and MappingBattery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation
Battery life estimation is critical for optimizing battery performance and guaranteeing minimal degradation for better efficiency and reliability of battery-powered systems. The existing methods to predict the Remaining …
Graph LearningPrognosisRelational ReasoningMulti-task Heterogeneous Graph Learning on Electronic Health Records
Learning electronic health records (EHRs) has received emerging attention because of its capability to facilitate accurate medical diagnosis. Since the EHRs contain enriched information specifying complex interactions be…
Causal InferenceDenoisingGraph LearningGraph Neural Network+2DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs
Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation (link prediction) or early detection of cancer cells (classification). Inspired by the success of st…
Dynamic Link PredictionDynamic Node ClassificationGraph LearningLanguage Modeling+5Joint Graph Rewiring and Feature Denoising via Spectral Resonance
When learning from graph data, the graph and the node features both give noisy information about the node labels. In this paper we propose an algorithm to jointly denoise the features and rewire the graph (JDR), which im…
DenoisingGraph LearningNode ClassificationA Structural Feature-Based Approach for Comprehensive Graph Classification
The increasing prevalence of graph-structured data across various domains has intensified greater interest in graph classification tasks. While numerous sophisticated graph learning methods have emerged, their complexity…
ClassificationGraph ClassificationGraph LearningModeling Multi-Step Scientific Processes with Graph Transformer Networks
This work presents the use of graph learning for the prediction of multi-step experimental outcomes for applications across experimental research, including material science, chemistry, and biology. The viability of geom…
Efficient ExplorationGraph LearningGraph Neural NetworkPath-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation
Unified graph representation learning aims to produce node embeddings, which can be applied to multiple downstream applications. However, existing studies based on graph neural networks and language models either suffer …
Graph LearningGraph Representation LearningLanguage ModelingLanguage Modelling+4Federated Hypergraph Learning: Hyperedge Completion with Local Differential Privacy
As the volume and complexity increase, graph-structured data commonly need to be split and stored across distributed systems. To enable data mining on subgraphs within these distributed systems, federated graph learning …
Federated LearningGraph LearningNode Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning
Textual graphs are ubiquitous in real-world applications, featuring rich text information with complex relationships, which enables advanced research across various fields. Textual graph representation learning aims to g…
Graph LearningGraph Representation LearningLanguage ModellingLink Prediction+2DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
This paper addresses the challenge of out-of-distribution (OOD) generalization in graph machine learning, a field rapidly advancing yet grappling with the discrepancy between source and target data distributions. Traditi…
Graph ClassificationGraph LearningModel SelectionNatural Language Understanding+1Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion
Graph clustering, a classical task in graph learning, involves partitioning the nodes of a graph into distinct clusters. This task has applications in various real-world scenarios, such as anomaly detection, social netwo…
Anomaly DetectionClusteringContrastive LearningGraph Clustering+1Knowledge Probing for Graph Representation Learning
Graph learning methods have been extensively applied in diverse application areas. However, what kind of inherent graph properties e.g. graph proximity, graph structural information has been encoded into graph representa…
Graph ClassificationGraph LearningGraph Representation LearningKnowledge Probing+3Deep Generative Models for Subgraph Prediction
Graph Neural Networks (GNNs) are important across different domains, such as social network analysis and recommendation systems, due to their ability to model complex relational data. This paper introduces subgraph queri…
Bayesian OptimizationGraph LearningLink PredictionNode Classification+2PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous dense vector space by preserving the unde…
Graph LearningNetwork Embedding