Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection | Paper Citation Record

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This paper cites Graph based anomaly detection and description: a survey.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graph based anomaly detection and description: a survey

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This paper cites Enhancing one-class support vector machines for unsupervised anomaly detection.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Enhancing one-class support vector machines for unsupervised anomaly detection

Reference 2

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This paper cites Theoretical numerical analysis , volume 39.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Theoretical numerical analysis , volume 39

Reference 3

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This paper cites Emergence of scaling in random networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Emergence of scaling in random networks

Reference 4

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This paper cites Outliers in statistical data , volume 3.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Outliers in statistical data , volume 3

Reference 5

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This paper cites Spectrally-normalized margin bounds for neural networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Spectrally-normalized margin bounds for neural networks

Reference 6

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This paper cites Outlier……….

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Outlier………

Reference 7

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This paper cites Shortest-path kernels on graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Shortest-path kernels on graphs

Reference 8

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This paper cites Lof: identifying density-based local outliers.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Lof: identifying density-based local outliers

Reference 9

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This paper cites Lg-fgad: An effective federated graph anomaly detection framework.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Lg-fgad: An effective federated graph anomaly detection framework

Reference 10

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This paper cites Hyperbolic graph convolutional neural networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Hyperbolic graph convolutional neural networks

Reference 11

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This paper cites Sampling techniques.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Sampling techniques

Reference 12

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This paper cites Deep anomaly detection on attributed networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Deep anomaly detection on attributed networks

Reference 13

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Observation 8f047bd4-b546-4b4f-87da-15ae9a85d71b · outbound

This paper cites Uniform central limit theorems , volume 142.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Uniform central limit theorems , volume 142

Reference 14

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Observation adca886e-171f-4d97-b06e-f721b63f505b · outbound

This paper cites Graph Mixture Density Networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graph Mixture Density Networks

Reference 15

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This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Fast Graph Representation Learning with PyTorch Geometric

Reference 16

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This paper cites Neural message passing for quantum chemistry.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Neural message passing for quantum chemistry

Reference 17

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This paper cites a tsch, Alexander J Smola, and Bernhard Sch \.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection a tsch, Alexander J Smola, and Bernhard Sch \

Reference 18

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This paper cites node2vec: Scalable feature learning for networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection node2vec: Scalable feature learning for networks

Reference 19

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This paper cites Spectro-Riemannian Graph Neural Networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Spectro-Riemannian Graph Neural Networks

Reference 20

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This paper cites Graphmore: Mitigating topological heterogeneity via mixture of riemannian experts.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graphmore: Mitigating topological heterogeneity via mixture of riemannian experts

Reference 21

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This paper cites Exploring network structure, dynamics, and function using networkx.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Exploring network structure, dynamics, and function using networkx

Reference 22

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Observation c64bdb73-90f4-4b29-9a71-1276b0fea575 · outbound

This paper cites Inductive representation learning on large graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Inductive representation learning on large graphs

Reference 23

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This paper cites Graph representation learning.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graph representation learning

Reference 24

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This paper cites Stochastic blockmodels: First steps.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Stochastic blockmodels: First steps

Reference 25

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This paper cites Anemone: Graph anomaly detection with multi-scale contrastive learning.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Anemone: Graph anomaly detection with multi-scale contrastive learning

Reference 26

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Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Unresolved cited work

Reference 27

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Observation c88b2ef8-cebc-4217-96c9-5f9b09425778 · outbound

This paper cites Methods of reducing sample size in monte carlo computations.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Methods of reducing sample size in monte carlo computations

Reference 28

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This paper cites Advances and open problems in federated learning.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Advances and open problems in federated learning

Reference 29

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Observation 5fa6a844-55bd-4a8d-9430-d909beaabbf0 · outbound

This paper cites Marginalized kernels between labeled graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Marginalized kernels between labeled graphs

Reference 30

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This paper cites Robust kernel density estimation.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Robust kernel density estimation

Reference 31

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This paper cites Rethinking reconstruction-based graph-level anomaly detection: limitations and a simple remedy.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Rethinking reconstruction-based graph-level anomaly detection: limitations and a simple remedy

Reference 32

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Observation 4f352613-32d3-46dc-a43d-73a9bb2de382 · outbound

This paper cites Variational Graph Auto-Encoders.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Variational Graph Auto-Encoders

Reference 33

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This paper cites Semi-supervised classification with graph convolutional networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Semi-supervised classification with graph convolutional networks

Reference 34

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Observation 5c4b3362-e7e5-4368-8760-9354f9287269 · outbound

This paper cites Explainable classification of brain networks via contrast subgraphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Explainable classification of brain networks via contrast subgraphs

Reference 35

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Observation d50f4ddf-3c9d-4f93-a1c9-1e64be2c5414 · outbound

This paper cites Graphde: A generative framework for debiased learning and out-of-distribution detection on graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graphde: A generative framework for debiased learning and out-of-distribution detection on graphs

Reference 36

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Observation a50a7e28-3a22-4f86-a093-a46ab6ee6c91 · outbound

This paper cites Cvtgad: Simplified transformer with cross-view attention for unsupervised graph-level anomaly detection.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Cvtgad: Simplified transformer with cross-view attention for unsupervised graph-level anomaly detection

Reference 37

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Observation 39dd4274-2f40-4a6d-943d-1ea3532392d8 · outbound

This paper cites Isolation forest.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Isolation forest

Reference 38

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Observation 61f769ac-8eee-4a6c-a71c-366a49381f5f · outbound

This paper cites Graph normalizing flows.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graph normalizing flows

Reference 39

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Observation d31e6f7d-d107-433b-a40d-b2631150d675 · outbound

This paper cites Energy-based models for atomic-resolution protein conformations.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Energy-based models for atomic-resolution protein conformations

Reference 40

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Observation baed7e2c-454e-4bed-8c64-7363d0ba11c7 · outbound

This paper cites Good-d: On unsupervised graph out-of-distribution detection.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Good-d: On unsupervised graph out-of-distribution detection

Reference 41

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Observation 807f9a84-6e6c-47c9-a4b5-0d17e33ffd7d · outbound

This paper cites Towards self-interpretable graph-level anomaly detection.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Towards self-interpretable graph-level anomaly detection

Reference 42

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Observation 91be50fc-f139-434f-896c-a6ebb1a0d6fb · outbound

This paper cites Deep graph level anomaly detection with contrastive learning.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Deep graph level anomaly detection with contrastive learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.418583Z

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Observation a0d16d81-1493-4348-bc85-1bed0ca4a324 · outbound

This paper cites A comprehensive survey on graph anomaly detection with deep learning.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection A comprehensive survey on graph anomaly detection with deep learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.273553Z

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Observation 1ba51061-a93f-45b0-b2ad-2c8f8cc48378 · outbound

This paper cites Deep graph-level anomaly detection by glocal knowledge distillation.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Deep graph-level anomaly detection by glocal knowledge distillation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.182345Z

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Observation 1945b8b7-8819-4c39-a3b1-995dc7ade57c · outbound

This paper cites TUDataset: A collection of benchmark datasets for learning with graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection TUDataset: A collection of benchmark datasets for learning with graphs

Reference 46

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This paper cites Biological network analysis with deep learning.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Biological network analysis with deep learning

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:24.056134Z

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Observation df5105a5-e3d8-4134-bdd6-6b45dd3aec19 · outbound

This paper cites Nachman and D.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Nachman and D

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.923129Z

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Observation fb654ed8-a173-4903-b0b9-9195172f4314 · outbound

This paper cites Propagation kernels: efficient graph kernels from propagated information.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Propagation kernels: efficient graph kernels from propagated information

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.818958Z

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Observation 395d1da8-716b-4ecd-a91f-54da841fbbe5 · outbound

This paper cites Deep learning for anomaly detection: A review.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Deep learning for anomaly detection: A review

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.687749Z

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Observation dbd48e77-6426-4340-893e-e3aaa2825f1e · outbound

This paper cites On estimation of a probability density function and mode.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection On estimation of a probability density function and mode

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.580439Z

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Observation 6e23a20b-5e62-4bc4-b53e-b155bc996f0c · outbound

This paper cites Deepwalk: Online learning of social representations.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Deepwalk: Online learning of social representations

Reference 52

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Observation 32f4b180-f196-425a-80ae-1e06ebcf3d65 · outbound

This paper cites Deep Graph Anomaly Detection: A Survey and New Perspectives.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Deep Graph Anomaly Detection: A Survey and New Perspectives

Reference 53

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This paper cites Raising the bar in graph-level anomaly detection.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Raising the bar in graph-level anomaly detection

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.446803Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2d453d35-d9c0-4006-ba6e-06d9c6250232 · outbound

This paper cites Rong, Tingyang Xu, Junzhou Huang, Wen bing Huang, Hong Cheng, Yao Ma, Yiqi Wang, Tyler Derr, Lingfei Wu, and Tengfei Ma.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Rong, Tingyang Xu, Junzhou Huang, Wen bing Huang, Hong Cheng, Yao Ma, Yiqi Wang, Tyler Derr, Lingfei Wu, and Tengfei Ma

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.309099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c9e82db7-193c-4deb-9814-dece8ae19fac · outbound

This paper cites Temporal Graph Networks for Deep Learning on Dynamic Graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Temporal Graph Networks for Deep Learning on Dynamic Graphs

Reference 56

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Observation 5e0b6a27-b9df-4e34-975e-60e82b86aca9 · outbound

This paper cites Estimating the support of a high-dimensional distribution.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Estimating the support of a high-dimensional distribution

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:15.914674Z

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Observation aa17c847-ed93-4a52-a48f-deac73532efa · outbound

This paper cites Optimizing ood detection in molecular graphs: A novel approach with diffusion models.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Optimizing ood detection in molecular graphs: A novel approach with diffusion models

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:23.138854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 76b35df0-08d8-4938-a93f-84078c5a26fb · outbound

This paper cites Weisfeiler-lehman graph kernels.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Weisfeiler-lehman graph kernels

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.968773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c61620c-7758-4be8-ba92-ed20bc1c8729 · outbound

This paper cites Grakel: A graph kernel library in python, 2020.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Grakel: A graph kernel library in python, 2020

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.792460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0bd03052-f08d-4814-8bf6-d88b929770d4 · outbound

This paper cites Uniform: Towards unified framework for anomaly detection on graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Uniform: Towards unified framework for anomaly detection on graphs

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.628519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e9adfb16-c9ea-40a9-bd3d-2141b5bb3493 · outbound

This paper cites Spectral sparsification of graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Spectral sparsification of graphs

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.437413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4a48d66e-6dae-4267-87ca-ba41480d77d7 · outbound

This paper cites Mmd graph kernel: Effective metric learning for graphs via maximum mean discrepancy.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Mmd graph kernel: Effective metric learning for graphs via maximum mean discrepancy

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.264989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0c3eb348-637f-4289-939c-ee1b56831953 · outbound

This paper cites InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:16.547704Z

Source-reported events for the cited work

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Observation ccf2db7b-d974-4069-9519-dca70d90a751 · outbound

This paper cites Graph convolutional networks for computational drug development and discovery.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graph convolutional networks for computational drug development and discovery

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:22.078359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1e8171cb-9088-4700-a91f-f5acb2288fcb · outbound

This paper cites Learning graph representation via graph entropy maximization.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Learning graph representation via graph entropy maximization

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:21.930571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5617ba19-25c9-464c-b9c2-43c8b90726f2 · outbound

This paper cites Introduction to Nonparametric Estimation.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Introduction to Nonparametric Estimation

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:21.775900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a2faa84-12ed-4e32-bcf9-8e82ef0d7a66 · outbound

This paper cites Visualizing data using t-sne.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Visualizing data using t-sne

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:16.920367Z

Source-reported events for the cited work

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Observation ac789525-ae32-4510-b2c1-1e8270f8d424 · outbound

This paper cites Deep graph infomax.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Deep graph infomax

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:21.599471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation becbb571-c2d3-4caa-b3e4-cf6b76cc14e8 · outbound

This paper cites Graph kernels.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graph kernels

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:21.403968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6060dbd8-3335-4935-ad62-b71bd279f2db · outbound

This paper cites Learning low-dimensional latent graph structures: A density estimation approach.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Learning low-dimensional latent graph structures: A density estimation approach

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:21.270022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2e0239eb-a1a0-42e5-a5ac-6011a3a9f05b · outbound

This paper cites Relational graph attention network for aspect-based sentiment analysis.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Relational graph attention network for aspect-based sentiment analysis

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:21.134087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eea02745-72d2-4315-8281-42ec6ac5f44b · outbound

This paper cites Graph Neural Networks for Molecules.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graph Neural Networks for Molecules

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:17.394143Z

Source-reported events for the cited work

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Observation 7dc66584-b041-4c22-a203-b071646f7e24 · outbound

This paper cites Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:17.544244Z

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Observation 4f1d0990-de12-4f85-884d-f645db38c8a4 · outbound

This paper cites Adaptive riemannian graph neural networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Adaptive riemannian graph neural networks

Reference 75

Resolution: verified exact; raw_fallback, observed 2026-08-07T13:43:19.279611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 63636465-1ec0-44f7-b750-21e5165bec12 · outbound

This paper cites Explainable graph representation learning via graph pattern analysis.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Explainable graph representation learning via graph pattern analysis

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:21.003999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54144b8b-118c-4606-88c4-19cde80a0d9d · outbound

This paper cites Deep graph library: Towards efficient and scalable deep learning on graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Deep graph library: Towards efficient and scalable deep learning on graphs

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:20.875113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c133f0ad-94fe-433e-8b93-128d373fd7d2 · outbound

This paper cites All of nonparametric statistics.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection All of nonparametric statistics

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:17.905286Z

Source-reported events for the cited work

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Observation c9e5b6dc-c5a7-405a-b7c7-83960983207e · outbound

This paper cites Collective dynamics of ‘small-world’networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Collective dynamics of ‘small-world’networks

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:17.969549Z

Source-reported events for the cited work

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Observation 60338174-ae2e-435a-9a25-3bb0a6cae023 · outbound

This paper cites Using the nystr \"o m method to speed up kernel machines.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Using the nystr \"o m method to speed up kernel machines

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:20.708451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cde99f5a-ccbc-4375-9a71-ba3cc170ceef · outbound

This paper cites A comprehensive survey on graph neural networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection A comprehensive survey on graph neural networks

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:18.136911Z

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Observation ed4f9939-f41e-479f-a3b9-96c4b020ea17 · outbound

This paper cites Rethinking explaining graph neural networks via non-parametric subgraph matching.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Rethinking explaining graph neural networks via non-parametric subgraph matching

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:20.523563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2253f0d3-9a75-4e2f-84e2-f8fb00941e4e · outbound

This paper cites Federated graph classification over non-iid graphs.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Federated graph classification over non-iid graphs

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:20.336220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e46a6946-da30-469c-a6f3-e4652744d79f · outbound

This paper cites How powerful are graph neural networks? In International Conference on Learning Representations , 2019.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection How powerful are graph neural networks? In International Conference on Learning Representations , 2019

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:18.404479Z

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Observation 7b809316-7457-4e26-8699-46e68dafa965 · outbound

This paper cites Infogcl: Information-aware graph contrastive learning.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Infogcl: Information-aware graph contrastive learning

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:18.502062Z

Source-reported events for the cited work

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Observation 540c8ed1-14d7-4718-a723-83bf91035bd9 · outbound

This paper cites Gnnexplainer: Generating explanations for graph neural networks.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Gnnexplainer: Generating explanations for graph neural networks

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:20.143722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 730c34bf-50bc-4eae-a617-189270c16645 · outbound

This paper cites Graph contrastive learning with augmentations.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Graph contrastive learning with augmentations

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:18.728200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd1dd82f-1eaa-4e19-83e0-f4e03bad41ec · outbound

This paper cites Dual-discriminative graph neural network for imbalanced graph-level anomaly detection.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Dual-discriminative graph neural network for imbalanced graph-level anomaly detection

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:19.971593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 47780d37-5187-4989-883e-948e4be36981 · outbound

This paper cites Using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection Using classification datasets to evaluate graph outlier detection: Peculiar observations and new insights

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:19.796434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dfe7b469-f73a-44d9-b680-fd2f29cd66c3 · outbound

This paper cites write newline.

Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection write newline

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:19.013242Z

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