Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Graph Diffusion Network for Drug-Gene Prediction
Predicting drug-gene associations is crucial for drug development and disease treatment. While graph neural networks (GNN) have shown effectiveness in this task, they face challenges with data sparsity and efficient cont…
Contrastive LearningGraph LearningPredictionSimple Path Structural Encoding for Graph Transformers
Graph transformers extend global self-attention to graph-structured data, achieving notable success in graph learning. Recently, random walk structural encoding (RWSE) has been found to further enhance their predictive p…
Graph LearningLLM4GNAS: A Large Language Model Based Toolkit for Graph Neural Architecture Search
Graph Neural Architecture Search (GNAS) facilitates the automatic design of Graph Neural Networks (GNNs) tailored to specific downstream graph learning tasks. However, existing GNAS approaches often require manual adapta…
Feature EngineeringGraph LearningHyperparameter OptimizationLanguage Modeling+3Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems
Deep neural networks have enabled researchers to create powerful generalized frameworks, such as transformers, that can be used to solve well-studied problems in various application domains, such as text and image. Howev…
Community DetectionGraph LearningGraph-To-Graph TranslationKnowledge Graph Completion+2Deep Semantic Graph Learning via LLM based Node Enhancement
Graph learning has attracted significant attention due to its widespread real-world applications. Current mainstream approaches rely on text node features and obtain initial node embeddings through shallow embedding lear…
Graph LearningNode ClassificationPrompt-Driven Continual Graph Learning
Continual Graph Learning (CGL), which aims to accommodate new tasks over evolving graph data without forgetting prior knowledge, is garnering significant research interest. Mainstream solutions adopt the memory replay-ba…
Continual LearningGraph LearningGraph Neural NetworkRobust Graph Learning Against Adversarial Evasion Attacks via Prior-Free Diffusion-Based Structure Purification
Adversarial evasion attacks pose significant threats to graph learning, with lines of studies that have improved the robustness of Graph Neural Networks (GNNs). However, existing works rely on priors about clean graphs o…
DenoisingGraph LearningHetSSNet: Spatial-Spectral Heterogeneous Graph Learning Network for Panchromatic and Multispectral Images Fusion
Remote sensing pansharpening aims to reconstruct spatial-spectral properties during the fusion of panchromatic (PAN) images and low-resolution multi-spectral (LR-MS) images, finally generating the high-resolution multi-s…
Graph LearningPansharpeningMedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification
Medical time series has been playing a vital role in real-world healthcare systems as valuable information in monitoring health conditions of patients. Accurate classification for medical time series, e.g., Electrocardio…
Electrocardiography (ECG)Graph LearningTime SeriesTime Series ClassificationNo Metric to Rule Them All: Toward Principled Evaluations of Graph-Learning Datasets
Benchmark datasets have proved pivotal to the success of graph learning, and good benchmark datasets are crucial to guide the development of the field. Recent research has highlighted problems with graph-learning dataset…
AllBenchmarkingGraph LearningA Metric for the Balance of Information in Graph Learning
Graph learning on molecules makes use of information from both the molecular structure and the features attached to that structure. Much work has been conducted on biasing either towards structure or features, with the a…
Graph LearningGraph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data
For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification applications provide alerts on potential exposures, a fully automated system…
Graph LearningBeyond 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+2Gradual Domain Adaptation for Graph Learning
Existing literature lacks a graph domain adaptation technique for handling large distribution shifts, primarily due to the difficulty in simulating an evolving path from source to target graph. To make a breakthrough, we…
Domain AdaptationGRAPH DOMAIN ADAPTATIONGraph LearningScaDyG:A New Paradigm for Large-scale Dynamic Graph Learning
Dynamic graphs (DGs), which capture time-evolving relationships between graph entities, have widespread real-world applications. To efficiently encode DGs for downstream tasks, most dynamic graph neural networks follow t…
Graph LearningGraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design
The growing importance of textual and relational systems has driven interest in enhancing large language models (LLMs) for graph-structured data, particularly Text-Attributed Graphs (TAGs), where samples are represented …
Graph LearningGraph Neural NetworkIn-Context LearningPrompt EngineeringRandom Walk Guided Hyperbolic Graph Distillation
Graph distillation (GD) is an effective approach to extract useful information from large-scale network structures. However, existing methods, which operate in Euclidean space to generate condensed graphs, struggle to ca…
Continual LearningGraph LearningLink PredictionNode 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 LearningToward Model-centric Heterogeneous Federated Graph Learning: A Knowledge-driven Approach
Federated graph learning (FGL) has emerged as a promising paradigm for collaborative machine learning, enabling multiple parties to jointly train models while preserving the privacy of raw graph data. However, existing F…
DiversityGraph LearningKnowledge DistillationIntegrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Model
Temporal Knowledge Graph Forecasting (TKGF) aims to predict future events based on the observed events in history. Recently, Large Language Models (LLMs) have exhibited remarkable capabilities, generating significant res…
cross-modal alignmentGraph EmbeddingGraph LearningKnowledge Graphs