Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs
Graph neural networks (GNNs) are widely used for learning node embeddings in graphs, typically adopting a message-passing scheme. This approach, however, leads to the neighbor explosion problem, with exponentially growin…
Graph LearningGraph SamplingMultimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection
RGB-Thermal Video Object Detection (RGBT VOD) can address the limitation of traditional RGB-based VOD in challenging lighting conditions, making it more practical and effective in many applications. However, similar to m…
Graph LearningGraph Representation Learningobject-detectionObject Detection+2GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector Quantization
Graph Transformers (GTs), which simultaneously integrate message-passing and self-attention mechanisms, have achieved promising empirical results in some graph prediction tasks. Although these approaches show the potenti…
Graph LearningGraph Representation LearningNode ClassificationQuantization+1Trajectory Encoding Temporal Graph Networks
Temporal Graph Networks (TGNs) have demonstrated significant success in dynamic graph tasks such as link prediction and node classification. Both tasks comprise transductive settings, where the model predicts links among…
Graph LearningLink PredictionNode ClassificationEmber: A Compiler for Efficient Embedding Operations on Decoupled Access-Execute Architectures
Irregular embedding lookups are a critical bottleneck in recommender models, sparse large language models, and graph learning models. In this paper, we first demonstrate that, by offloading these lookups to specialized a…
Graph LearningTowards Unbiased Federated Graph Learning: Label and Topology Perspectives
Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data. Among its approaches, subgraph-FL has become the dominant paradigm, with most work focuse…
FairnessGraph LearningGraph MiningNode Classification+1Federated Prototype Graph Learning
In recent years, Federated Graph Learning (FGL) has gained significant attention for its distributed training capabilities in graph-based machine intelligence applications, mitigating data silos while offering a new pers…
Contrastive LearningGraph LearningNetTAG: A Multimodal RTL-and-Layout-Aligned Netlist Foundation Model via Text-Attributed Graph
Circuit representation learning has shown promise in advancing Electronic Design Automation (EDA) by capturing structural and functional circuit properties for various tasks. Existing pre-trained solutions rely on graph …
Graph LearningRepresentation LearningBetween Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning
Dynamic graph learning is essential for applications involving temporal networks and requires effective modeling of temporal relationships. Seminal attention-based models like TGAT and DyGFormer rely on sinusoidal time e…
Graph LearningGraph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
Large language models (LLMs) struggle with the factual error during inference due to the lack of sufficient training data and the most updated knowledge, leading to the hallucination problem. Retrieval-Augmented Generati…
Graph LearningHallucinationNavigateRAG+2Scalable Hypergraph Structure Learning with Diverse Smoothness Priors
In graph signal processing, learning the weighted connections between nodes from a set of sample signals is a fundamental task when the underlying relationships are not known a priori. This task is typically addressed by…
Graph LearningToward General and Robust LLM-enhanced Text-attributed Graph Learning
Recent advancements in Large Language Models (LLMs) and the proliferation of Text-Attributed Graphs (TAGs) across various domains have positioned LLM-enhanced TAG learning as a critical research area. By utilizing rich g…
Graph LearningTAGText AugmentationAlleviating Performance Disparity in Adversarial Spatiotemporal Graph Learning Under Zero-Inflated Distribution
Spatiotemporal Graph Learning (SGL) under Zero-Inflated Distribution (ZID) is crucial for urban risk management tasks, including crime prediction and traffic accident profiling. However, SGL models are vulnerable to adve…
Crime PredictionGraph LearningAdvances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review
Financial institutions are required by regulation to report suspicious financial transactions related to money laundering. Therefore, they need to constantly monitor vast amounts of incoming and outgoing transactions. A …
Continual LearningGraph LearningGraph Neural NetworkLGIN: Defining an Approximately Powerful Hyperbolic GNN
While graph neural networks (GNNs) operating in hyperbolic spaces have shown promise for modeling hierarchical and complex relational data, a critical limitation often overlooked is their potentially limited discriminati…
Graph LearningGraph Neural NetworkGraph Representation LearningRepresentation LearningDynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation
Learning from longitudinal electronic health records is limited if it does not capture the temporal trajectories of the patient's state in a clinical setting. Graph models allow us to capture the hidden dependencies of t…
graph constructionGraph LearningTime SeriesA Semantic-Enhanced Heterogeneous Graph Learning Method for Flexible Objects Recognition
Flexible objects recognition remains a significant challenge due to its inherently diverse shapes and sizes, translucent attributes, and subtle inter-class differences. Graph-based models, such as graph convolution netwo…
Feature CorrelationGraph GenerationGraph LearningAdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model
The skip-gram model (SGM), which employs a neural network to generate node vectors, serves as the basis for numerous popular graph embedding techniques. However, since the training datasets contain sensitive linkage info…
Graph EmbeddingGraph LearningGraph-to-Vision: Multi-graph Understanding and Reasoning using Vision-Language Models
Graph Neural Networks (GNNs), as the dominant paradigm for graph-structured learning, have long faced dual challenges of exponentially escalating computational complexity and inadequate cross-scenario generalization capa…
Graph LearningKnowledge GraphsRelational ReasoningRethinking Graph Structure Learning in the Era of LLMs
Recently, the emergence of LLMs has prompted researchers to integrate language descriptions into graphs, aiming to enhance model encoding capabilities from a data-centric perspective. This graph representation is called …
Graph LearningGraph structure learningIn-Context LearningTAG