How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? | Paper Citation Record

Observation d5e36c6e-0bb7-447e-be99-248f30fc3395 · outbound

This paper cites An introduction to probabilistic graphical models, 2003.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? An introduction to probabilistic graphical models, 2003

Reference 1

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Observation 09d0c10e-b599-4e97-9de3-dde9c995ac1f · outbound

This paper cites Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt

Reference 2

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This paper cites Wang and George Y.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Wang and George Y

Reference 3

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This paper cites Estimation and prediction for stochastic blockmodels for graphs with latent block structure.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Estimation and prediction for stochastic blockmodels for graphs with latent block structure

Reference 4

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This paper cites Community detection and stochastic block models: recent developments.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Community detection and stochastic block models: recent developments

Reference 5

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This paper cites The graph neural network model.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? The graph neural network model

Reference 6

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This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Convolutional neural networks on graphs with fast localized spectral filtering

Reference 7

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This paper cites Bronstein.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Bronstein

Reference 8

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This paper cites Message passing all the way up, 2022.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Message passing all the way up, 2022

Reference 9

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This paper cites Revisiting heterophily for graph neural networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Revisiting heterophily for graph neural networks

Reference 10

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This paper cites Bronstein.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Bronstein

Reference 11

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This paper cites Understanding heterophily for graph neural networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Understanding heterophily for graph neural networks

Reference 12

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This paper cites Beyond homophily in graph neural networks: current limitations and effective designs.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Beyond homophily in graph neural networks: current limitations and effective designs

Reference 13

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This paper cites Finding global homophily in graph neural networks when meeting heterophily.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Finding global homophily in graph neural networks when meeting heterophily

Reference 14

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This paper cites The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges

Reference 15

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This paper cites On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks

Reference 16

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This paper cites Insights from network science can advance deep graph learning.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Insights from network science can advance deep graph learning

Reference 17

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This paper cites Stochastic Blockmodels meet Graph Neural Networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Stochastic Blockmodels meet Graph Neural Networks

Reference 18

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This paper cites The deep latent position block model for the block clustering and latent representation of networks, 2024.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? The deep latent position block model for the block clustering and latent representation of networks, 2024

Reference 19

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This paper cites Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks

Reference 20

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This paper cites Inference in probabilistic graphical models by graph neural networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Inference in probabilistic graphical models by graph neural networks

Reference 21

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This paper cites GNNs getting comfy: Community and feature similarity guided rewiring.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? GNNs getting comfy: Community and feature similarity guided rewiring

Reference 22

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This paper cites Revisiting graph neural networks: All we have is low-pass filters, 2019.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Revisiting graph neural networks: All we have is low-pass filters, 2019

Reference 23

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This paper cites On the bottleneck of graph neural networks and its practical implications.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? On the bottleneck of graph neural networks and its practical implications

Reference 24

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This paper cites Graph clustering with graph neural networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Graph clustering with graph neural networks

Reference 25

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This paper cites Duranthon and Lenka Zdeborov’a.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Duranthon and Lenka Zdeborov’a

Reference 26

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This paper cites The ground truth about metadata and community detection in networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? The ground truth about metadata and community detection in networks

Reference 27

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This paper cites Structure and inference in annotated networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Structure and inference in annotated networks

Reference 28

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This paper cites Community detection with node attributes in multilayer networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Community detection with node attributes in multilayer networks

Reference 29

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How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Structure and inference in hypergraphs with node attributes

Reference 30

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This paper cites Power, Daniel B.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Power, Daniel B

Reference 31

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This paper cites Efficient monte carlo and greedy heuristic for the inference of stochastic block models.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Efficient monte carlo and greedy heuristic for the inference of stochastic block models

Reference 32

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This paper cites Pairre: Knowledge graph embeddings via paired relation vectors.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Pairre: Knowledge graph embeddings via paired relation vectors

Reference 33

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This paper cites Automated concatenation of embeddings for structured prediction.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Automated concatenation of embeddings for structured prediction

Reference 34

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This paper cites MacQueen.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? MacQueen

Reference 35

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How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Unresolved cited work

Reference 36

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This paper cites Graph Attention Networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Graph Attention Networks

Reference 37

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This paper cites Kipf and Max Welling.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Kipf and Max Welling

Reference 38

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This paper cites Beyond homophily in graph neural networks: Current limitations and effective designs.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Beyond homophily in graph neural networks: Current limitations and effective designs

Reference 39

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This paper cites Konstantin Rusch, Michael M.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Konstantin Rusch, Michael M

Reference 40

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This paper cites Demystifying oversmoothing in attention-based graph neural networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Demystifying oversmoothing in attention-based graph neural networks

Reference 41

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This paper cites Graph neural networks exponentially lose expressive power for node classification.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Graph neural networks exponentially lose expressive power for node classification

Reference 42

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This paper cites A Note on Over-Smoothing for Graph Neural Networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? A Note on Over-Smoothing for Graph Neural Networks

Reference 43

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This paper cites Cohen, and Ruslan Salakhutdinov.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Cohen, and Ruslan Salakhutdinov

Reference 44

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This paper cites Predicting multicellular function through multi-layer tissue networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Predicting multicellular function through multi-layer tissue networks

Reference 45

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This paper cites Long range graph benchmark.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Long range graph benchmark

Reference 46

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This paper cites Vishwanathan.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Vishwanathan

Reference 47

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This paper cites Adamic and Natalie Glance.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Adamic and Natalie Glance

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.355434Z

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This paper cites Benson, Jure Leskovec, and David F.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Benson, Jure Leskovec, and David F

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.344626Z

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Observation a07b7555-13e3-45cc-af4d-05f3fc07a25d · outbound

This paper cites Geom-GCN: Geometric Graph Convolutional Networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Geom-GCN: Geometric Graph Convolutional Networks

Reference 50

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This paper cites Adam: A Method for Stochastic Optimization.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Adam: A Method for Stochastic Optimization

Reference 51

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Observation a8784a43-4769-4b7c-b9aa-3f204d84f989 · outbound

This paper cites The expressive power of graph neural networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? The expressive power of graph neural networks

Reference 52

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Observation 7d9c1a62-20b2-442e-874f-2ed686232905 · outbound

This paper cites A Fair Comparison of Graph Neural Networks for Graph Classification.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 53

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This paper cites Hamilton, Rex Ying, and Jure Leskovec.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Hamilton, Rex Ying, and Jure Leskovec

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.321671Z

Source-reported events for the cited work

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Observation 237890e8-72bd-43f1-a577-1d39677f547d · outbound

This paper cites How Powerful are Graph Neural Networks?.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? How Powerful are Graph Neural Networks?

Reference 55

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Source-reported events for the cited work

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This paper cites On positional and structural node features for graph neural networks on non-attributed graphs.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? On positional and structural node features for graph neural networks on non-attributed graphs

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.310225Z

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.

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This paper cites Tenorio, Madeline Navarro, Santiago Segarra, and Antonio G.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Tenorio, Madeline Navarro, Santiago Segarra, and Antonio G

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.297501Z

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 d9e047c9-29d5-456c-aca9-9da50756b89f · outbound

This paper cites Graph convolutional networks for graphs containing missing features.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Graph convolutional networks for graphs containing missing features

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.284685Z

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 fb2a673b-cbdf-4ed8-b9a5-33dea1d8cd09 · outbound

This paper cites Graph neural networks can recover the hidden features solely from the graph structure.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Graph neural networks can recover the hidden features solely from the graph structure

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.272142Z

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 4bfeef22-20d7-4930-b1c3-5fb507d112e9 · outbound

This paper cites Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.261165Z

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 654a9d97-af46-4886-b32a-3de25370fa5a · outbound

This paper cites Community detection in large hypergraphs.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Community detection in large hypergraphs

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.249993Z

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 cb493edf-1ad7-46a6-958c-403a16d4c5c4 · outbound

This paper cites Broad spectrum structure discovery in large-scale higher-order networks, 2025.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Broad spectrum structure discovery in large-scale higher-order networks, 2025

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.237911Z

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 047fcd38-4cb0-4288-abd9-3da3a0cd46a8 · outbound

This paper cites Link prediction under heterophily: A physics-inspired graph neural network approach.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Link prediction under heterophily: A physics-inspired graph neural network approach

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.225964Z

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 57825331-c390-4312-82de-80d0d304c810 · outbound

This paper cites Is Homophily a Necessity for Graph Neural Networks?.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Is Homophily a Necessity for Graph Neural Networks?

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-07T01:07:34.806548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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This paper cites Ordered gnn: Ordering message passing to deal with heterophily and over-smoothing, 02 2023.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Ordered gnn: Ordering message passing to deal with heterophily and over-smoothing, 02 2023

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.214114Z

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 c5a2ae96-3176-4c71-bd6f-bd94dad4db96 · outbound

This paper cites an unresolved cited work.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-07T01:07:35.202184Z

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 a802fd7d-a0e7-421c-b5dc-29c756c8a108 · outbound

This paper cites Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Interpretable deep learning: Interpretation, interpretability, trustworthiness, and beyond

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.190508Z

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 008d7420-ff5c-4f14-bc80-a562dfe8d3c1 · outbound

This paper cites Graphlime: Local interpretable model explanations for graph neural networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Graphlime: Local interpretable model explanations for graph neural networks

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.179075Z

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 2c28ba9e-6a7b-4602-a81e-5f6bb8868384 · outbound

This paper cites Learning deep representations for graph clustering.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Learning deep representations for graph clustering

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.167243Z

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 12aa1b1e-7ff1-43c1-aee5-bbc839f0c72b · outbound

This paper cites Attributed graph clustering: a deep attentional embedding approach.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Attributed graph clustering: a deep attentional embedding approach

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.154805Z

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 8960613c-f711-44f9-85d8-1d4423d7f738 · outbound

This paper cites Deep k-means clustering based on graph neural networks: Leveraging cohesion and separation in graph nodes.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Deep k-means clustering based on graph neural networks: Leveraging cohesion and separation in graph nodes

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.143265Z

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 e829f122-6941-41d8-b494-d1af234ddff3 · outbound

This paper cites Viualizing data using t-sne.Journal of Machine Learning Research, 9:2579–2605, 11 2008.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Viualizing data using t-sne.Journal of Machine Learning Research, 9:2579–2605, 11 2008

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.131063Z

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 254df6ab-1fe4-4b24-be16-a940153c1623 · outbound

This paper cites Flexible inference in heterogeneous and attributed multilayer networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? Flexible inference in heterogeneous and attributed multilayer networks

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.119240Z

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 119466d0-2647-4a71-8caf-9f2c105c6ca5 · outbound

This paper cites How powerful are spectral graph neural networks.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? How powerful are spectral graph neural networks

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T01:07:35.106829Z

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 58ae4db2-bd59-4591-8a2d-ac75872df819 · outbound

This paper cites On the Equivalence between Positional Node Embeddings and Structural Graph Representations.

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data? On the Equivalence between Positional Node Embeddings and Structural Graph Representations

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-07T01:07:34.844695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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