An Efficient and Scalable Graph Condensation with Structure-Preserving | Paper Citation Record

Observation 1c22d5fc-d61d-49dc-a502-e36c3ca1096b · outbound

This paper cites Influence Maximization in Real-World Closed Social Networks.

An Efficient and Scalable Graph Condensation with Structure-Preserving Influence Maximization in Real-World Closed Social Networks

Reference 1

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Observation a57b13a5-61e8-4e71-95aa-95fce6c73226 · outbound

This paper cites Do transformers really perform badly for graph representation?.

An Efficient and Scalable Graph Condensation with Structure-Preserving Do transformers really perform badly for graph representation?

Reference 2

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This paper cites Graph neural networks for social recommendation,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Graph neural networks for social recommendation,

Reference 3

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This paper cites Graph Neural Networks: Methods, Applications, and Opportunities.

An Efficient and Scalable Graph Condensation with Structure-Preserving Graph Neural Networks: Methods, Applications, and Opportunities

Reference 4

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Observation 6bb11a07-09e3-4394-a74d-453ed89c120c · outbound

This paper cites Nas-bench-graph: Benchmarking graph neural architecture search,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Nas-bench-graph: Benchmarking graph neural architecture search,

Reference 5

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This paper cites Remember the past: Distilling datasets into addressable memories for neural networks,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Remember the past: Distilling datasets into addressable memories for neural networks,

Reference 6

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Observation bfab1cc5-5d40-49b1-8ba3-d63b99118618 · outbound

This paper cites Dataset Distillation.

An Efficient and Scalable Graph Condensation with Structure-Preserving Dataset Distillation

Reference 7

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Observation 054e238d-55e2-46c1-a155-8413f5b69ca7 · outbound

This paper cites Does graph distillation see like vision dataset counterpart?.

An Efficient and Scalable Graph Condensation with Structure-Preserving Does graph distillation see like vision dataset counterpart?

Reference 8

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Observation 945f4abf-5cb8-4a31-a0a6-72acf92a468e · outbound

This paper cites Dataset condensation with differentiable siamese augmentation,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Dataset condensation with differentiable siamese augmentation,

Reference 9

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Observation 45b227ce-96f9-4d92-ab29-05c67bcc56ce · outbound

This paper cites Dataset Condensation with Gradient Matching.

An Efficient and Scalable Graph Condensation with Structure-Preserving Dataset Condensation with Gradient Matching

Reference 10

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Observation a47fc338-7bac-41ab-b001-28dd2e8a504b · outbound

This paper cites Dataset condensation with distribution matching,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Dataset condensation with distribution matching,

Reference 11

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Observation 007bddd1-274f-4f1e-8ec1-49a80073a317 · outbound

This paper cites Dataset Meta-Learning from Kernel Ridge-Regression.

An Efficient and Scalable Graph Condensation with Structure-Preserving Dataset Meta-Learning from Kernel Ridge-Regression

Reference 12

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Observation 463d7212-a212-4767-a091-99ab79e86f4c · outbound

This paper cites Cafe: Learning to condense dataset by aligning features,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Cafe: Learning to condense dataset by aligning features,

Reference 13

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Observation eec7291c-fcb9-43bd-b4cd-efed569429d8 · outbound

This paper cites Graph Condensation for Graph Neural Networks.

An Efficient and Scalable Graph Condensation with Structure-Preserving Graph Condensation for Graph Neural Networks

Reference 14

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Observation f27b3ba0-bdf6-49e5-9717-b7acfc85cef5 · outbound

This paper cites Condensing graphs via one-step gradient matching,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Condensing graphs via one-step gradient matching,

Reference 15

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Observation 91880982-342b-4264-9f5f-ca6936e8aa4d · outbound

This paper cites Graph Condensation via Receptive Field Distribution Matching.

An Efficient and Scalable Graph Condensation with Structure-Preserving Graph Condensation via Receptive Field Distribution Matching

Reference 16

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Observation 73029250-b6ef-4e06-93d3-61d296b0b7de · outbound

This paper cites Structure-free graph condensation: From large-scale graphs to condensed graph-free data,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Structure-free graph condensation: From large-scale graphs to condensed graph-free data,

Reference 17

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Observation ef05df7a-b627-4744-955e-1fb92c5c1dbc · outbound

This paper cites Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching.

An Efficient and Scalable Graph Condensation with Structure-Preserving Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching

Reference 18

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Observation 3deca5b5-abbb-4bc1-aea7-d880b1356ea9 · outbound

This paper cites Fast graph condensation with structure-based neural tangent kernel,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Fast graph condensation with structure-based neural tangent kernel,

Reference 19

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Observation cde9facd-e59e-42b4-96c6-b09301909dd0 · outbound

This paper cites Kernel ridge regression-based graph dataset distillation,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Kernel ridge regression-based graph dataset distillation,

Reference 20

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Observation 62f060f0-e67d-4cfc-9e97-1e2e21c38de5 · outbound

This paper cites Simple graph condensation,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Simple graph condensation,

Reference 21

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Observation e0956ea3-9fe5-41aa-93f5-319df695bbf7 · outbound

This paper cites Rethinking and accelerating graph condensation: A training-free approach with class partition,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Rethinking and accelerating graph condensation: A training-free approach with class partition,

Reference 22

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Observation 055a944b-5d37-4509-b1f5-299786c3cccb · outbound

This paper cites Graph spectral image smoothing using the heat kernel,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Graph spectral image smoothing using the heat kernel,

Reference 23

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Observation 281cd290-f43d-4045-b065-b54f7c1d2f2c · outbound

This paper cites Adaptive diffusion in graph neural networks,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Adaptive diffusion in graph neural networks,

Reference 24

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Observation 1994d2fe-17e4-46be-9db9-ba7e72d5aa97 · outbound

This paper cites Distributed implementation of heat kernel smoothing for graph signal denoising,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Distributed implementation of heat kernel smoothing for graph signal denoising,

Reference 25

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Observation 7da4250e-f51a-4223-beb6-3cbf2a5eec75 · outbound

This paper cites Fast approximate truncated svd,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Fast approximate truncated svd,

Reference 26

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Observation 92cd366d-00bb-4ecf-b1f9-eaae17217331 · outbound

This paper cites Random features for large-scale kernel machines,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Random features for large-scale kernel machines,

Reference 27

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Observation 28b33671-1a95-43d9-8a9a-6a8f2e7ca69c · outbound

This paper cites Disentangled condensation for large-scale graphs,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Disentangled condensation for large-scale graphs,

Reference 28

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This paper cites Herding dynamical weights to learn,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Herding dynamical weights to learn,

Reference 29

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Observation 587cb993-6a78-4ac2-a2bf-6fece46de5d4 · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

An Efficient and Scalable Graph Condensation with Structure-Preserving Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 30

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Observation 176b614a-db98-4a38-bdf6-44963e64de02 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

An Efficient and Scalable Graph Condensation with Structure-Preserving Semi-Supervised Classification with Graph Convolutional Networks

Reference 31

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Observation 7f3396a9-257a-49b9-aa8b-e033be473db8 · outbound

This paper cites Simplifying graph convolutional networks,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Simplifying graph convolutional networks,

Reference 32

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Observation 9dbd4938-0e46-436e-8bf8-8f84c1be69b4 · outbound

This paper cites Graph attention networks,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Graph attention networks,

Reference 33

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Observation 49e3cad8-a469-4085-87d1-166f7f3c403c · outbound

This paper cites Inductive representation learning on large graphs,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Inductive representation learning on large graphs,

Reference 34

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Observation 889ec963-7ce0-48b8-9381-e1c59f8c63d8 · outbound

This paper cites Predict then Propagate: Graph Neural Networks meet Personalized PageRank.

An Efficient and Scalable Graph Condensation with Structure-Preserving Predict then Propagate: Graph Neural Networks meet Personalized PageRank

Reference 35

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Observation 475bc4b7-46e5-447c-9c48-3c8d3b0de949 · outbound

This paper cites Graph tensor convolutional network,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Graph tensor convolutional network,

Reference 36

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This paper cites Advanced high-order graph convolutional networks with assorted time-frequency transforms,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Advanced high-order graph convolutional networks with assorted time-frequency transforms,

Reference 37

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This paper cites A node-collaboration-informed graph convolutional network for highly accurate representation to undirected weighted graph,.

An Efficient and Scalable Graph Condensation with Structure-Preserving A node-collaboration-informed graph convolutional network for highly accurate representation to undirected weighted graph,

Reference 38

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This paper cites Gt-a 2 t: Graph tensor alliance attention network,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Gt-a 2 t: Graph tensor alliance attention network,

Reference 39

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Observation 55c22c90-35f7-4b64-8818-885a8becc3e4 · outbound

This paper cites A kalman-filter-incorporated latent factor analysis model for temporally dynamic sparse data,.

An Efficient and Scalable Graph Condensation with Structure-Preserving A kalman-filter-incorporated latent factor analysis model for temporally dynamic sparse data,

Reference 40

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This paper cites An adaptive divergence-based non-negative latent factor model,.

An Efficient and Scalable Graph Condensation with Structure-Preserving An adaptive divergence-based non-negative latent factor model,

Reference 41

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This paper cites A multilayered-and-randomized latent factor model for high-dimensional and sparse matrices,.

An Efficient and Scalable Graph Condensation with Structure-Preserving A multilayered-and-randomized latent factor model for high-dimensional and sparse matrices,

Reference 42

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This paper cites A generalized and fast-converging non-negative latent factor model for predicting user preferences in recommender systems,.

An Efficient and Scalable Graph Condensation with Structure-Preserving A generalized and fast-converging non-negative latent factor model for predicting user preferences in recommender systems,

Reference 43

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This paper cites Temporal web service qos prediction via kalman filter-incorporated latent factor analysis.

An Efficient and Scalable Graph Condensation with Structure-Preserving Temporal web service qos prediction via kalman filter-incorporated latent factor analysis

Reference 44

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This paper cites Non-gradient hash factor learning for high-dimensional and incomplete data representation learning,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Non-gradient hash factor learning for high-dimensional and incomplete data representation learning,

Reference 45

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This paper cites Learning accurate representation to nonstandard tensors via a mode-aware tucker network,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Learning accurate representation to nonstandard tensors via a mode-aware tucker network,

Reference 46

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Observation 79387177-a509-435e-a0d3-29db4ad55eba · outbound

This paper cites Tensor low-rank orthogonal compression for convolutional neural networks,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Tensor low-rank orthogonal compression for convolutional neural networks,

Reference 47

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Observation 848cd8c2-50f9-434e-9e97-51b8e883356c · outbound

This paper cites Discovering spatiotemporal–individual coupled features from nonstandard tensors—a novel dynamic graph mixer approach,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Discovering spatiotemporal–individual coupled features from nonstandard tensors—a novel dynamic graph mixer approach,

Reference 48

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This paper cites Scg: A novel spatiotemporal coupling graph convolutional network-incorporated approach for dynamic qos estimation,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Scg: A novel spatiotemporal coupling graph convolutional network-incorporated approach for dynamic qos estimation,

Reference 49

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Observation 7427f2a3-d59e-4381-8833-0b87e7ddb89f · outbound

This paper cites A convolution bias-incorporated nonnegative latent factorization of tensors model for accurate representation learning to dynamic directed graphs,.

An Efficient and Scalable Graph Condensation with Structure-Preserving A convolution bias-incorporated nonnegative latent factorization of tensors model for accurate representation learning to dynamic directed graphs,

Reference 50

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Observation 74f9f97f-5c34-4b6b-96d5-b5d60b34b216 · outbound

This paper cites Knowledge-driven multiple instance learning with hierarchical cluster-incorporated aware filtering for larynx pathological grading,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Knowledge-driven multiple instance learning with hierarchical cluster-incorporated aware filtering for larynx pathological grading,

Reference 51

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This paper cites Fmvpci: a multiview fusion neural network for identifying protein complex via fuzzy clustering,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Fmvpci: a multiview fusion neural network for identifying protein complex via fuzzy clustering,

Reference 52

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This paper cites Graph-based prediction of mirna-drug associations with multisource information and metapath enhancement matrices,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Graph-based prediction of mirna-drug associations with multisource information and metapath enhancement matrices,

Reference 53

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This paper cites Fuzzy mixture-of-experts aggregation for organoid identification with multiscale state space features,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Fuzzy mixture-of-experts aggregation for organoid identification with multiscale state space features,

Reference 54

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Observation 5b4a5d6d-b314-4b3e-ac8a-8090185a48a2 · outbound

This paper cites Ncsac: Effective neural community search via attribute-augmented conductance,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Ncsac: Effective neural community search via attribute-augmented conductance,

Reference 55

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This paper cites A proximal-admm-incorporated nonnegative latent-factorization-of-tensors model for representing dynamic cryptocurrency transaction network,.

An Efficient and Scalable Graph Condensation with Structure-Preserving A proximal-admm-incorporated nonnegative latent-factorization-of-tensors model for representing dynamic cryptocurrency transaction network,

Reference 56

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This paper cites Multi-scale collaborative distillation graph neural networks for session-based recommendation,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Multi-scale collaborative distillation graph neural networks for session-based recommendation,

Reference 57

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Observation 68995da5-989f-4284-8923-a6d21d69b48e · outbound

This paper cites A generalized nesterov’s accelerated gradient-incorporated non-negative latent- factorization-of-tensors model for efficient representation to dynamic qos data,.

An Efficient and Scalable Graph Condensation with Structure-Preserving A generalized nesterov’s accelerated gradient-incorporated non-negative latent- factorization-of-tensors model for efficient representation to dynamic qos data,

Reference 58

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Observation f5c25301-d165-42db-83a4-0b38e3fa8b90 · outbound

This paper cites Symmetry and graph bi-regularized non-negative matrix factorization for precise community detection,.

An Efficient and Scalable Graph Condensation with Structure-Preserving Symmetry and graph bi-regularized non-negative matrix factorization for precise community detection,

Reference 59

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An Efficient and Scalable Graph Condensation with Structure-Preserving Mmlf: Multi-metric latent feature analysis for high-dimensional and incomplete data,

Reference 60

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An Efficient and Scalable Graph Condensation with Structure-Preserving Learning error refinement in stochastic gradient descent-based latent factor analysis via diversified pid controllers,

Reference 61

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An Efficient and Scalable Graph Condensation with Structure-Preserving A fuzzy pid-incorporated stochastic gradient descent algorithm for fast and accurate latent factor analysis,

Reference 62

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An Efficient and Scalable Graph Condensation with Structure-Preserving Adaptively-accelerated parallel stochastic gradient descent for high-dimensional and incomplete data representation learning,

Reference 63

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An Efficient and Scalable Graph Condensation with Structure-Preserving Sgd-dyg: Self-reliant global dependency apprehending on dynamic graphs,

Reference 64

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An Efficient and Scalable Graph Condensation with Structure-Preserving Auto-encoding neural tucker factorization,

Reference 65

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An Efficient and Scalable Graph Condensation with Structure-Preserving Multimetric autoencoder for representing high-dimensional and incomplete data,

Reference 66

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An Efficient and Scalable Graph Condensation with Structure-Preserving A novel tensor causal convolution network model for highly-accurate representation to spatio-temporal data,

Reference 67

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An Efficient and Scalable Graph Condensation with Structure-Preserving Adaptive divergence-based non-negative latent factor analysis of high-dimensional and incomplete matrices from industrial applications,

Reference 68

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An Efficient and Scalable Graph Condensation with Structure-Preserving An adaptively bias-extended non-negative latent factorization of tensors model for accurately representing the dynamic qos data,

Reference 69

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An Efficient and Scalable Graph Condensation with Structure-Preserving A robust coevolutionary neural-based optimization algorithm for constrained nonconvex optimization,

Reference 70

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Reference 71

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An Efficient and Scalable Graph Condensation with Structure-Preserving A novel tensor decomposition-based efficient detector for low-altitude aerial objects with knowledge distillation scheme,

Reference 72

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An Efficient and Scalable Graph Condensation with Structure-Preserving A fast nonnegative autoencoder-based approach to latent feature analysis on high-dimensional and incomplete data,

Reference 73

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An Efficient and Scalable Graph Condensation with Structure-Preserving A sampling-neighborhood-regularized latent factorization of tensor for dynamic qos estimation,

Reference 74

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An Efficient and Scalable Graph Condensation with Structure-Preserving Attention-mechanism-based neural latent-factorization-of-tensors model,

Reference 75

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An Efficient and Scalable Graph Condensation with Structure-Preserving Graph linear convolution pooling for learning in incomplete high-dimensional data,

Reference 76

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An Efficient and Scalable Graph Condensation with Structure-Preserving An outlier-resilient autoencoder for representing high-dimensional and incomplete data,

Reference 77

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An Efficient and Scalable Graph Condensation with Structure-Preserving Robust low-rank latent feature analysis for spatiotemporal signal recovery,

Reference 78

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An Efficient and Scalable Graph Condensation with Structure-Preserving Neural tucker factorization,

Reference 79

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An Efficient and Scalable Graph Condensation with Structure-Preserving Link-based attributed graph clustering via approximate generative bayesian learning,

Reference 80

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An Efficient and Scalable Graph Condensation with Structure-Preserving Neural nonnegative latent factorization of tensors model with acceleration and unconstraint,

Reference 81

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Reference 82

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An Efficient and Scalable Graph Condensation with Structure-Preserving Local search-based anytime algorithms for continuous distributed constraint optimization problems,

Reference 83

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An Efficient and Scalable Graph Condensation with Structure-Preserving Local search-based anytime algorithms for continuous distributed constraint optimization problems,

Reference 84

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An Efficient and Scalable Graph Condensation with Structure-Preserving Iterative role negotiation via the bilevel gra++ with decision tolerance,

Reference 85

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