Papers Graph Sampling
“Graph Sampling” 태그가 달린 논문 101편 · 필터 해제
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training
Graph neural networks (GNNs) are machine learning models specialized for graph data and widely used in many applications. To train GNNs on large graphs that exceed CPU memory, several systems store data on disk and condu…
CPUGraph SamplingA Sampling-based Framework for Hypothesis Testing on Large Attributed Graphs
Hypothesis testing is a statistical method used to draw conclusions about populations from sample data, typically represented in tables. With the prevalence of graph representations in real-life applications, hypothesis …
Graph SamplingAccelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning
Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1,000 scientific papers into an ontological knowledge graph. Through an in-depth structural analysis, we have calculated node d…
Graph SamplingKnowledge GraphsPhilosophyscientific discoveryGLISP: A Scalable GNN Learning System by Exploiting Inherent Structural Properties of Graphs
As a powerful tool for modeling graph data, Graph Neural Networks (GNNs) have received increasing attention in both academia and industry. Nevertheless, it is notoriously difficult to deploy GNNs on industrial scale grap…
Graph Learninggraph partitioningGraph SamplingA Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs
Large-scale graph machine learning is challenging as the complexity of learning models scales with the graph size. Subsampling the graph is a viable alternative, but sampling on graphs is nontrivial as graphs are non-Euc…
Graph SamplingClassification of developmental and brain disorders via graph convolutional aggregation
While graph convolution based methods have become the de-facto standard for graph representation learning, their applications to disease prediction tasks remain quite limited, particularly in the classification of neurod…
Disease PredictionGraph Representation LearningGraph SamplingRepresentation LearningInterpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
Data-driven surrogate modeling has surged in capability in recent years with the emergence of graph neural networks (GNNs), which can operate directly on mesh-based representations of data. The goal of this work is to in…
Graph Neural NetworkGraph SamplingCooperative Minibatching in Graph Neural Networks
Training large scale Graph Neural Networks (GNNs) requires significant computational resources, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch traini…
GPUGraph SamplingCausal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
In the context of inferring a Bayesian network structure (directed acyclic graph, DAG for short), we devise a non-reversible continuous time Markov chain, the ``Causal Zig-Zag sampler'', that targets a probability distri…
Causal DiscoveryGraph SamplingGRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks
Graph neural networks (GNNs) learn to represent nodes by aggregating information from their neighbors. As GNNs increase in depth, their receptive field grows exponentially, leading to high memory costs. Several existing …
Graph SamplingNode ClassificationGraph Neural Network for Stress Predictions in Stiffened Panels Under Uniform Loading
Machine learning (ML) and deep learning (DL) techniques have gained significant attention as reduced order models (ROMs) to computationally expensive structural analysis methods, such as finite element analysis (FEA). Gr…
Graph EmbeddingGraph Neural NetworkGraph SamplingGDM: Dual Mixup for Graph Classification with Limited Supervision
Graph Neural Networks (GNNs) require a large number of labeled graph samples to obtain good performance on the graph classification task. The performance of GNNs degrades significantly as the number of labeled graph samp…
DiversityGraph ClassificationGraph SamplingImpression-Informed Multi-Behavior Recommender System: A Hierarchical Graph Attention Approach
While recommender systems have significantly benefited from implicit feedback, they have often missed the nuances of multi-behavior interactions between users and items. Historically, these systems either amalgamated all…
Graph AttentionGraph Neural NetworkGraph SamplingRecommendation Systems+1STGIN: Spatial-Temporal Graph Interaction Network for Large-scale POI Recommendation
In Location-Based Services, Point-Of-Interest(POI) recommendation plays a crucial role in both user experience and business opportunities. Graph neural networks have been proven effective in providing personalized POI re…
graph constructionGraph SamplingA Topology-aware Analysis of Graph Collaborative Filtering
The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph CF). By representing user-item data as …
Collaborative FilteringGraph SamplingRecommendation SystemsClass-level Structural Relation Modelling and Smoothing for Visual Representation Learning
Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models. However, these models …
Graph SamplingRelationRepresentation LearningGraph Sampling-based Meta-Learning for Molecular Property Prediction
Molecular property is usually observed with a limited number of samples, and researchers have considered property prediction as a few-shot problem. One important fact that has been ignored by prior works is that each mol…
Graph SamplingMeta-LearningMolecular Property PredictionProperty PredictionAccelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses
Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Although GNNs have been shown to be effect…
CPUGPUGraph SamplingGraph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
Graphs are omnipresent and GNNs are a powerful family of neural networks for learning over graphs. Despite their popularity, scaling GNNs either by deepening or widening suffers from prevalent issues of unhealthy gradien…
graph partitioningGraph SamplingQuiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Systems for serving inference requests on graph neural networks (GNN) must combine low latency with high throughout, but they face irregular computation due to skew in the number of sampled graph nodes and aggregated GNN…
CPUGPUGraph Sampling