Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
Causal Disentanglement Learning for Accurate Anomaly Detection in Multivariate Time Series
Disentangling complex causal relationships is important for accurate detection of anomalies. In multivariate time series analysis, dynamic interactions among data variables over time complicate the interpretation of caus…
Graph Representation LearningTime Series AnalysisAnomaly DetectionQuestion-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation
Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multi…
Graph Representation LearningGraph Neural NetworkGraph LearningStructured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans
With the growing volume of CT examinations, there is an increasing demand for automated tools such as organ segmentation, abnormality detection, and report generation to support radiologists in managing their clinical wo…
Graph Representation LearningMulti-Label ClassificationWhat Do Temporal Graph Learning Models Learn?
Learning on temporal graphs has become a central topic in graph representation learning, with numerous benchmarks indicating the strong performance of state-of-the-art models. However, recent work has raised concerns abo…
Graph Representation LearningGraph LearningHeSRN: Representation Learning On Heterogeneous Graphs via Slot-Aware Retentive Network
Graph Transformers have recently achieved remarkable progress in graph representation learning by capturing long-range dependencies through self-attention. However, their quadratic computational complexity and inability …
Graph Representation LearningNode ClassificationTAWRMAC: A Novel Dynamic Graph Representation Learning Method
Dynamic graph representation learning has become essential for analyzing evolving networks in domains such as social network analysis, recommendation systems, and traffic analysis. However, existing continuous-time metho…
Graph Representation LearningDynamic Link PredictionRecommendation SystemsNode ClassificationRelational Database Distillation: From Structured Tables to Condensed Graph Data
Relational databases (RDBs) underpin the majority of global data management systems, where information is structured into multiple interdependent tables. To effectively use the knowledge within RDBs for predictive tasks,…
Graph Representation LearningGNN-enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics
Consumer electronics (CE) connected to the Internet of Things are susceptible to various attacks, including DDoS and web-based threats, which can compromise their functionality and facilitate remote hijacking. These vuln…
Graph Representation LearningAnomaly DetectionMolGA: Molecular Graph Adaptation with Pre-trained 2D Graph Encoder
Molecular graph representation learning is widely used in chemical and biomedical research. While pre-trained 2D graph encoders have demonstrated strong performance, they overlook the rich molecular domain knowledge asso…
Graph Representation LearningGenerative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection
Spoofing detection in financial trading is crucial, especially for identifying complex behaviors such as conspiracy spoofing. Traditional machine-learning approaches primarily focus on isolated node features, often overl…
Graph Representation LearningDiffusion-Assisted Distillation for Self-Supervised Graph Representation Learning with MLPs
For large-scale applications, there is growing interest in replacing Graph Neural Networks (GNNs) with lightweight Multi-Layer Perceptrons (MLPs) via knowledge distillation. However, distilling GNNs for self-supervised g…
Graph Representation LearningSelf-Supervised LearningKnowledge DistillationLEAP: Local ECT-Based Learnable Positional Encodings for Graphs
Graph neural networks (GNNs) largely rely on the message-passing paradigm, where nodes iteratively aggregate information from their neighbors. Yet, standard message passing neural networks (MPNNs) face well-documented th…
Graph Representation LearningLess is More: Towards Simple Graph Contrastive Learning
Graph Contrastive Learning (GCL) has shown strong promise for unsupervised graph representation learning, yet its effectiveness on heterophilic graphs, where connected nodes often belong to different classes, remains lim…
Graph Representation LearningContrastive LearningData AugmentationTRACE: Learning to Compute on Circuit Graphs
Learning to compute, the ability to model the functional behavior of a circuit graph, is a fundamental challenge for graph representation learning. Yet, the dominant paradigm is architecturally mismatched for this task. …
Graph Representation LearningPolynomial Contrastive Learning for Privacy-Preserving Representation Learning on Graphs
Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations on graph data without requiring manual labels. However, leading SSL methods like GRACE are fundamentally incompatible with pr…
Graph Representation LearningSelf-Supervised LearningContrastive LearningExploring the Global-to-Local Attention Scheme in Graph Transformers: An Empirical Study
Graph Transformers (GTs) show considerable potential in graph representation learning. The architecture of GTs typically integrates Graph Neural Networks (GNNs) with global attention mechanisms either in parallel or as a…
Graph Representation LearningSGNNBench: A Holistic Evaluation of Spiking Graph Neural Network on Large-scale Graph
Graph Neural Networks (GNNs) are exemplary deep models designed for graph data. Message passing mechanism enables GNNs to effectively capture graph topology and push the performance boundaries across various graph tasks.…
Graph Representation LearningGraph Neural NetworkGSTBench: A Benchmark Study on the Transferability of Graph Self-Supervised Learning
Self-supervised learning (SSL) has shown great promise in graph representation learning. However, most existing graph SSL methods are developed and evaluated under a single-dataset setting, leaving their cross-dataset tr…
Graph Representation LearningSelf-Supervised LearningGraph LearningParameter-Free Structural-Diversity Message Passing for Graph Neural Networks
Graph Neural Networks (GNNs) have shown remarkable performance in structured data modeling tasks such as node classification. However, mainstream approaches generally rely on a large number of trainable parameters and fi…
Graph Representation LearningGraph Neural NetworkNode ClassificationPredicting Drug-Drug Interactions Using Heterogeneous Graph Neural Networks: HGNN-DDI
Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the compl…
Graph Representation LearningGraph Neural Network