Papers Graph Sampling
“Graph Sampling” 태그가 달린 논문 101편 · 필터 해제
On Random Walk Based Graph Sampling
Random walk based graph sampling has been recognized as a fundamental technique to collect uniform node samples from a large graph. In this paper, we first present a comprehensive analysis of the drawbacks of three widel…
Graph SamplingNeighborhood Matching Network for Entity Alignment
Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. This paper presents Neighborhood Matching Network (NMN), a novel entity alignment framework for tackling the structural …
Entity AlignmentGraph SamplingKnowledge GraphsSIGN: Scalable Inception Graph Neural Networks
Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media. The popularity of graph neural networks has sp…
Graph Representation LearningGraph SamplingNode ClassificationNode Property Prediction+1Heterogeneous Graph Transformer
Recent years have witnessed the emerging success of graph neural networks (GNNs) for modeling structured data. However, most GNNs are designed for homogeneous graphs, in which all nodes and edges belong to the same types…
Graph SamplingHeterogeneous Node ClassificationNode Property PredictionFolded Graph Signals: Sensing with Unlimited Dynamic Range
Self-reset analog-to-digital converters (ADCs) are used to sample high dynamic range signals resulting in modulo-operation based folded signal samples. We consider the case where each vertex of a graph (e.g., sensors in …
Graph SamplingTime SeriesTime Series AnalysisGraphBGS: Background Subtraction via Recovery of Graph Signals
Background subtraction is a fundamental pre-processing task in computer vision. This task becomes challenging in real scenarios due to variations in the background for both static and moving camera sequences. Several dee…
Change Detectiongraph constructionGraph SamplingInstance Segmentation+1Graph Sampling for Matrix Completion Using Recurrent Gershgorin Disc Shift
Matrix completion algorithms fill missing entries in a large matrix given a subset of observed samples. However, how to best pre-select informative matrix entries given a sampling budget is largely unaddressed. In this p…
Graph SamplingMatrix CompletionGraphSAINT: Graph Sampling Based Inductive Learning Method
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "n…
Graph AttentionGraph EmbeddingGraph Representation LearningGraph Sampling+3Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling
A serious challenge when finding influential actors in real-world social networks is the lack of knowledge about the structure of the underlying network. Current state-of-the-art methods rely on hand-crafted sampling alg…
Graph SamplingReinforcement LearningReconstruction-Cognizant Graph Sampling using Gershgorin Disc Alignment
Graph sampling with noise is a fundamental problem in graph signal processing (GSP). Previous works assume an unbiased least square (LS) signal reconstruction scheme and select samples greedily via expensive extreme eige…
Graph SamplingAccurate, Efficient and Scalable Graph Embedding
The Graph Convolutional Network (GCN) model and its variants are powerful graph embedding tools for facilitating classification and clustering on graphs. However, a major challenge is to reduce the complexity of layered …
ClusteringGraph EmbeddingGraph LearningGraph Sampling+1Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Empirical risk minimization is the main tool for prediction problems, but its extension to relational data remains unsolved. We solve this problem using recent ideas from graph sampling theory to (i) define an empirical …
Graph SamplingNode ClassificationCounting Motifs with Graph Sampling
Applied researchers often construct a network from a random sample of nodes in order to infer properties of the parent network. Two of the most widely used sampling schemes are subgraph sampling, where we sample each ver…
Graph SamplingAdaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly time-varying) subset of vertices. We recast two classical adaptive algorithm…
Graph SamplingUnsupervised Learning of Morphology with Graph Sampling
We introduce a language-independent, graph-based probabilistic model of morphology, which uses transformation rules operating on whole words instead of the traditional morphological segmentation. The morphological analys…
Graph SamplingMorphological AnalysisSegmentationA Sampling Theory Perspective of Graph-based Semi-supervised Learning
Graph-based methods have been quite successful in solving unsupervised and semi-supervised learning problems, as they provide a means to capture the underlying geometry of the dataset. It is often desirable for the const…
Graph SamplingEvaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction
Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work…
Dimensionality ReductionFunctional ConnectivityGeneral ClassificationGraph SamplingGraph sampling with determinantal processes
We present a new random sampling strategy for k-bandlimited signals defined on graphs, based on determinantal point processes (DPP). For small graphs, ie, in cases where the spectrum of the graph is accessible, we exhibi…
Graph SamplingPoint ProcessesAdaptive Least Mean Squares Estimation of Graph Signals
The aim of this paper is to propose a least mean squares (LMS) strategy for adaptive estimation of signals defined over graphs. Assuming the graph signal to be band-limited, over a known bandwidth, the method enables rec…
Graph SamplingEvaluating Visual Properties via Robust HodgeRank
Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the problem of how to estimate such visual properties from a ranking perspe…
Graph SamplingOutlier Detection