Papers Graph Sampling
“Graph Sampling” 태그가 달린 논문 101편 · 필터 해제
Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference
Neural network inference typically operates on raw input data, increasing the risk of exposure during preprocessing and inference. Moreover, neural architectures lack efficient built-in mechanisms for directly authentica…
Graph SamplingNeural Network SecurityPrivacy PreservingSimple yet Effective Graph Distillation via Clustering
Despite plentiful successes achieved by graph representation learning in various domains, the training of graph neural networks (GNNs) still remains tenaciously challenging due to the tremendous computational overhead ne…
ClusteringGraph Representation LearningGraph SamplingNode ClassificationBeyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target d…
Distributed OptimizationGraph SamplingThe Limits of Graph Samplers for Training Inductive Recommender Systems: Extended results
Inductive Recommender Systems are capable of recommending for new users and with new items thus avoiding the need to retrain after new data reaches the system. However, these methods are still trained on all the data ava…
Graph SamplingRecommendation SystemsGraph 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 SamplingDistributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
Graph neural networks (GNNs) have delivered remarkable results in various fields. However, the rapid increase in the scale of graph data has introduced significant performance bottlenecks for GNN inference. Both computat…
Graph LearningGraph Neural NetworkGraph SamplingHierarchical graph sampling based minibatch learning with chain preservation and variance reduction
Graph sampling based Graph Convolutional Networks (GCNs) decouple the sampling from the forward and backward propagation during minibatch training, which exhibit good scalability in terms of layer depth and graph size. W…
Graph SamplingNode ClassificationGraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better
The success of Large Language Models (LLMs) in various domains has led researchers to apply them to graph-related problems by converting graph data into natural language text. However, unlike graph data, natural language…
Graph Question AnsweringGraph SamplingNode ClassificationQuestion Answering+1BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch
This paper introduces Localized Bipartite Match Graph Attention Q-Learning (BMG-Q), a novel Multi-Agent Reinforcement Learning (MARL) algorithm framework tailored for ride-pooling order dispatch. BMG-Q advances ride-pool…
Graph AttentionGraph SamplingMulti-agent Reinforcement LearningQ-LearningPhysics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks
This work introduces a novel graph neural networks (GNNs)-based method to predict stream water temperature and reduce model bias across locations of different income and education levels. Traditional physics-based models…
Graph SamplingTowards joint graph learning and sampling set selection from data
We explore the problem of sampling graph signals in scenarios where the graph structure is not predefined and must be inferred from data. In this scenario, existing approaches rely on a two-step process, where a graph is…
Graph LearningGraph SamplingGraph Sampling for Scalable and Expressive Graph Neural Networks on Homophilic Graphs
Graph Neural Networks (GNNs) excel in many graph machine learning tasks but face challenges when scaling to large networks. GNN transferability allows training on smaller graphs and applying the model to larger ones, but…
Graph SamplingGCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis
Sentiment analysis and emotion recognition in videos are challenging tasks, given the diversity and complexity of the information conveyed in different modalities. Developing a highly competent framework that effectively…
Emotion RecognitionGraph SamplingSentiment AnalysisFederated Graph Learning with Adaptive Importance-based Sampling
For privacy-preserving graph learning tasks involving distributed graph datasets, federated learning (FL)-based GCN (FedGCN) training is required. A key challenge for FedGCN is scaling to large-scale graphs, which typica…
Federated LearningGraph LearningGraph SamplingPrivacy PreservingRetrofitting Temporal Graph Neural Networks with Transformer
Temporal graph neural networks (TGNNs) outperform regular GNNs by incorporating time information into graph-based operations. However, TGNNs adopt specialized models (e.g., TGN, TGAT, and APAN ) and require tailored trai…
Graph AttentionGraph SamplingLanguage ModelingLanguage ModellingBearing Fault Diagnosis using Graph Sampling and Aggregation Network
Bearing fault diagnosis technology has a wide range of practical applications in industrial production, energy and other fields. Timely and accurate detection of bearing faults plays an important role in preventing catas…
Fault DetectionFault DiagnosisGraph SamplingUnveiling Privacy Vulnerabilities: Investigating the Role of Structure in Graph Data
The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studies have concentrated on privacy leakage …
AttributeGraph SamplingInference AttackPrivacy PreservingEdge Sampling of Graphs: Graph Signal Processing Approach With Edge Smoothness
Finding important edges in a graph is a crucial problem for various research fields, such as network epidemics, signal processing, machine learning, and sensor networks. In this paper, we tackle the problem based on samp…
Graph SamplingLight-weight End-to-End Graph Interest Network for CTR Prediction in E-commerce Search
Click-through-rate (CTR) prediction has an essential impact on improving user experience and revenue in e-commerce search. With the development of deep learning, graph-based methods are well exploited to utilize graph st…
Click-Through Rate PredictionGraph EmbeddingGraph LearningGraph Sampling+1AGS-GNN: Attribute-guided Sampling for Graph Neural Networks
We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and hete…
AttributeGraph SamplingNode Classification