Papers Graph Sampling
“Graph Sampling” 태그가 달린 논문 101편 · 필터 해제
Gophormer: Ego-Graph Transformer for Node Classification
Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area, where graph neural network (GNN) has bee…
ClassificationData AugmentationGraph MiningGraph Neural Network+2Fast Graph Sampling for Short Video Summarization using Gershgorin Disc Alignment
We study the problem of efficiently summarizing a short video into several keyframes, leveraging recent progress in fast graph sampling. Specifically, we first construct a similarity path graph (SPG) $\mathcal{G}$, repre…
Graph SamplingVideo SummarizationScaffold-Induced Molecular Graph (SIMG): Effective Graph Sampling Methods for High-Throughput Computational Drug Discovery
Scaffold based drug discovery (SBDD) is a technique for drug discovery which pins chemical scaffolds as the framework of design. Scaffolds, or molecular frameworks, organize the design of compounds into local neighborhoo…
Drug DiscoveryGraph SamplingAre Negative Samples Necessary in Entity Alignment? An Approach with High Performance, Scalability and Robustness
Entity alignment (EA) aims to find the equivalent entities in different KGs, which is a crucial step in integrating multiple KGs. However, most existing EA methods have poor scalability and are unable to cope with large-…
Entity AlignmentGraph SamplingTraining Graph Neural Networks with 1000 Layers
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges. However, memory complexity has become a major obstacle when traini…
GPUGraph SamplingNode Property PredictionBayesian structure learning and sampling of Bayesian networks with the R package BiDAG
The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample g…
Graph SamplingGraph Sampling Based Deep Metric Learning for Generalizable Person Re-Identification
Recent studies show that, both explicit deep feature matching as well as large-scale and diverse training data can significantly improve the generalization of person re-identification. However, the efficiency of learning…
Generalizable Person Re-identificationGraph SamplingMetric LearningPerson Re-Identification+2Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment
3D point cloud (PC) -- a collection of discrete geometric samples of a physical object's surface -- is typically large in size, which entails expensive subsequent operations like viewpoint image rendering and object reco…
Graph SamplingObject RecognitionSuper-ResolutionPre-Training on Dynamic Graph Neural Networks
The pre-training on the graph neural network model can learn the general features of large-scale networks or networks of the same type by self-supervised methods, which allows the model to work even when node labels are …
Graph GenerationGraph Neural NetworkGraph SamplingLink PredictionGIST: Distributed Training for Large-Scale Graph Convolutional Networks
The graph convolutional network (GCN) is a go-to solution for machine learning on graphs, but its training is notoriously difficult to scale both in terms of graph size and the number of model parameters. Although some w…
BIG-bench Machine LearningGPUGraph SamplingEfficient Graph Deep Learning in TensorFlow with tf_geometric
We introduce tf_geometric, an efficient and friendly library for graph deep learning, which is compatible with both TensorFlow 1.x and 2.x. tf_geometric provides kernel libraries for building Graph Neural Networks (GNNs)…
Deep LearningGeneral ClassificationGraph ClassificationGraph Sampling+4Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances
For the traveling salesman problem (TSP), the existing supervised learning based algorithms suffer seriously from the lack of generalization ability. To overcome this drawback, this paper tries to train (in supervised ma…
Graph SamplingReinforcement Learning (RL)Traveling Salesman ProblemLearning Parameters for Balanced Index Influence Maximization
Influence maximization is the task of finding the smallest set of nodes whose activation in a social network can trigger an activation cascade that reaches the targeted network coverage, where threshold rules determine t…
BIG-bench Machine LearningGraph SamplingEfficient Neural Architecture Search for End-to-end Speech Recognition via Straight-Through Gradients
Neural Architecture Search (NAS), the process of automating architecture engineering, is an appealing next step to advancing end-to-end Automatic Speech Recognition (ASR), replacing expert-designed networks with learned,…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Graph SamplingNeural Architecture Search+2Text Graph Transformer for Document Classification
Text classification is a fundamental problem in natural language processing. Recent studies applied graph neural network (GNN) techniques to capture global word co-occurrence in a corpus. However, previous works are not …
ClassificationDocument ClassificationGeneral ClassificationGraph Neural Network+3Scalable Graph Neural Networks via Bidirectional Propagation
Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most existing methods use "graph sampling" or…
Graph SamplingAccurate, Efficient and Scalable Training of Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning models to generate node embeddings on graphs. When applying deep GNNs on large graphs, it is still challenging to perform training in an efficient and scalable way.…
Graph SamplingAccelerating Graph Sampling for Graph Machine Learning using GPUs
Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, sample the graph to produce mini-batches …
BIG-bench Machine LearningGPUGraph SamplingRepresentation LearningLittle Ball of Fur: A Python Library for Graph Sampling
Sampling graphs is an important task in data mining. In this paper, we describe Little Ball of Fur a Python library that includes more than twenty graph sampling algorithms. Our goal is to make node, edge, and exploratio…
Graph ClassificationGraph EmbeddingGraph SamplingNode ClassificationReducing Large Internet Topologies for Faster Simulations
In this paper, we develop methods to “sample” a small realistic graph from a large real network. Despite recent activity, the modeling and generation of realistic graphs is still not a resolved issue. All previous work h…
Graph Sampling