Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Instance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning
Graph Neural Networks (GNN) endure catastrophic forgetting, undermining their capacity to preserve previously acquired knowledge amid the assimilation of novel information. Rehearsal-based techniques revisit historical e…
Contrastive LearningGraph LearningNode ClassificationFedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations
Due to the highly sensitive nature of certain data in cross-border sharing, collaborative cross-border recommendations and data sharing are often subject to stringent privacy protection regulations, resulting in insuffic…
Federated LearningGraph LearningPrivacy PreservingRepresentation LearningLearning Kronecker-Structured Graphs from Smooth Signals
Graph learning, or network inference, is a prominent problem in graph signal processing (GSP). GSP generalizes the Fourier transform to non-Euclidean domains, and graph learning is pivotal to applying GSP when these doma…
Graph LearningClustering of Incomplete Data via a Bipartite Graph Structure
There are various approaches to graph learning for data clustering, incorporating different spectral and structural constraints through diverse graph structures. Some methods rely on bipartite graph models, where nodes a…
ClusteringGraph LearningLearn to Think: Bootstrapping LLM Reasoning Capability Through Graph Learning
Large Language Models (LLMs) have achieved remarkable success across various domains. However, they still face significant challenges, including high computational costs for training and limitations in solving complex re…
Graph LearningGraph Neural NetworkRepresentation LearningA Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction
The comorbidities of hypertension impose a heavy burden on patients and society. Early identification is necessary to prompt intervention, but it remains a challenging task. This study aims to address this challenge by c…
Graph LearningGraph Representation LearningRepresentation LearningSoft causal learning for generalized molecule property prediction: An environment perspective
Learning on molecule graphs has become an increasingly important topic in AI for science, which takes full advantage of AI to facilitate scientific discovery. Existing solutions on modeling molecules utilize Graph Neural…
Graph LearningProperty Predictionscientific discoveryPartial Label Clustering
Partial label learning (PLL) is a significant weakly supervised learning framework, where each training example corresponds to a set of candidate labels and only one label is the ground-truth label. For the first time, t…
ClusteringConstrained ClusteringGraph LearningPartial Label Learning+1Rethinking Federated Graph Learning: A Data Condensation Perspective
Federated graph learning is a widely recognized technique that promotes collaborative training of graph neural networks (GNNs) by multi-client graphs.However, existing approaches heavily rely on the communication of mode…
Graph LearningMulti-Scale Graph Learning for Anti-Sparse Downscaling
Water temperature can vary substantially even across short distances within the same sub-watershed. Accurate prediction of stream water temperature at fine spatial resolutions (i.e., fine scales, $\leq$ 1 km) enables pre…
Graph LearningMulti-Task LearningInterpretable graph-based models on multimodal biomedical data integration: A technical review and benchmarking
Integrating heterogeneous biomedical data including imaging, omics, and clinical records supports accurate diagnosis and personalised care. Graph-based models fuse such non-Euclidean data by capturing spatial and relatio…
BenchmarkingData Integrationgraph constructionGraph Learning+1Toward Data-centric Directed Graph Learning: An Entropy-driven Approach
The directed graph (digraph), as a generalization of undirected graphs, exhibits superior representation capability in modeling complex topology systems and has garnered considerable attention in recent years. Despite th…
Graph LearningKnowledge DistillationScalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling
Large Language Models (LLMs) have demonstrated strong capabilities in various natural language processing tasks; however, their application to graph-related problems remains limited, primarily due to scalability constrai…
Graph LearningLink PredictionNode ClassificationFedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner while preserving data privacy. However, FGL methods usually require that the graph data owned …
Federated LearningGraph LearningNode ClassificationGraphATC: advancing multilevel and multi-label anatomical therapeutic chemical classification via atom-level graph learning
The accurate categorization of compounds within the anatomical therapeutic chemical (ATC) system is fundamental for drug development and fundamental research. Although this area has garnered significant research focus fo…
Drug ATC ClassificationGraph ClassificationGraph LearningMolecular Property Prediction+2ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion
Graph Neural Networks (GNNs) have demonstrated impressive performance across diverse graph-based tasks by leveraging message passing to capture complex node relationships. However, when applied to large-scale real-world …
Computational EfficiencyGraph LearningEdge-boosted graph learning for functional brain connectivity analysis
Predicting disease states from functional brain connectivity is critical for the early diagnosis of severe neurodegenerative diseases such as Alzheimer's Disease and Parkinson's Disease. Existing studies commonly employ …
Functional ConnectivityGraph LearningFedC4: Graph Condensation Meets Client-Client Collaboration for Efficient and Private Federated Graph Learning
Federated Graph Learning (FGL) is an emerging distributed learning paradigm that enables collaborative model training over decentralized graph-structured data while preserving local privacy. Existing FGL methods can be c…
Graph LearningEG-Gaussian: Epipolar Geometry and Graph Network Enhanced 3D Gaussian Splatting
In this paper, we explore an open research problem concerning the reconstruction of 3D scenes from images. Recent methods have adopt 3D Gaussian Splatting (3DGS) to produce 3D scenes due to its efficient training process…
3DGS3D Scene ReconstructionGraph LearningPlain Transformers Can be Powerful Graph Learners
Transformers have attained outstanding performance across various modalities, owing to their simple but powerful scaled-dot-product (SDP) attention mechanisms. Researchers have attempted to migrate Transformers to graph …
Graph Learning