Papers Graph Sampling
“Graph Sampling” 태그가 달린 논문 101편 · 필터 해제
SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs
We describe SynGraphy, a method for visually summarising the structure of large network datasets that works by drawing smaller graphs generated to have similar structural properties to the input graphs. Visualising compl…
Graph MiningGraph SamplingMD-GCN: A Multi-Scale Temporal Dual Graph Convolution Network for Traffic Flow Prediction
The spatial–temporal prediction of traffic flow is very important for traffic management and planning. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlat…
Graph SamplingManagementPredictionvalidRethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
The success of graph neural networks (GNNs) provokes the question about explainability: ``Which fraction of the input graph is the most determinant of the prediction?'' Particularly, parametric explainers prevail in exis…
Graph SamplingConditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidl…
Drug DiscoveryGraph GenerationGraph SamplingMolecular Graph GenerationDynamic Sensor Placement Based on Sampling Theory for Graph Signals
In this paper, we consider a sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select K sensor positions from N candidates where K < N. Most existing methods ass…
Dictionary LearningGraph SamplingCharacterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glass
Graph neural networks (GNNs) have received great attention due to their success in various graph-related learning tasks. Several GNN frameworks have then been developed for fast and easy implementation of GNN models. Des…
Graph Neural NetworkGraph SamplingEfficient Directed Graph Sampling via Gershgorin Disc Alignment
Graph sampling is the problem of choosing a node subset via sampling matrix $\mathbf{H} \in \{0,1\}^{K \times N}$ to collect samples $\mathbf{y} = \mathbf{H} \mathbf{x} \in \mathbb{R}^K$, $K < N$, so that the target sign…
Graph SamplingGraph sampling for node embedding
Node embedding is a central topic in graph representation learning. Computational efficiency and scalability can be challenging to any method that requires full-graph operations. We propose sampling approaches to node em…
Computational EfficiencyGraph Representation LearningGraph SamplingRepresentation LearningJoint Network Topology Inference via a Shared Graphon Model
We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph model. We adopt a graphon as our random g…
Graphon EstimationGraph SamplingSampling for network function learning
Given a valued graph, where both the nodes and the edges of the graph are associated with one or several values, any network function for a given node must be defined in terms of that node and its connected nodes in the …
Graph SamplingEfficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment
A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. However, existing fast graph sampling scheme…
Graph SamplingNode Copying: A Random Graph Model for Effective Graph Sampling
There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes.…
Graph SamplingNode ClassificationFine-tuning Partition-aware Item Similarities for Efficient and Scalable Recommendation
Collaborative filtering (CF) is widely searched in recommendation with various types of solutions. Recent success of Graph Convolution Networks (GCN) in CF demonstrates the effectiveness of modeling high-order relationsh…
Collaborative FilteringData Augmentationgraph partitioningGraph Sampling+1TF-GNN: Graph Neural Networks in TensorFlow
TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in TensorFlow. It is designed from the bottom up to support the kinds of rich heterogeneous graph data that occurs in today's information ecosystems…
Graph LearningGraph SamplingNeuro-Symbolic Procedural Planning with Commonsense Prompting
Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps. Although procedural planning is a basic skill set for humans in daily life, it remains a challenge …
Graph SamplingNeighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks
In the modern age of social media and networks, graph representations of real-world phenomena have become an incredibly useful source to mine insights. Often, we are interested in understanding how entities in a graph ar…
ClassificationEdge ClassificationGraph LearningGraph Neural Network+4HyFactor: Hydrogen-count labelled graph-based defactorization Autoencoder
Graph-based architectures are becoming increasingly popular as a tool for structure generation. Here, we introduce a novel open-source architecture HyFactor which is inspired by previously reported DEFactor architecture …
BenchmarkingGraph LearningGraph SamplingMolecular Graph GenerationLearning Large-scale Network Embedding from Representative Subgraph
We study the problem of large-scale network embedding, which aims to learn low-dimensional latent representations for network mining applications. Recent research in the field of network embedding has led to significant …
Graph SamplingNetwork EmbeddingLearning Dynamic Preference Structure Embedding From Temporal Networks
The dynamics of temporal networks lie in the continuous interactions between nodes, which exhibit the dynamic node preferences with time elapsing. The challenges of mining temporal networks are thus two-fold: the dynamic…
Graph SamplingFedGraph: Federated Graph Learning with Intelligent Sampling
Federated learning has attracted much research attention due to its privacy protection in distributed machine learning. However, existing work of federated learning mainly focuses on Convolutional Neural Network (CNN), w…
Deep Reinforcement LearningFederated LearningGraph LearningGraph Sampling