Paper Citation Record · LEDGER

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective

As of 19 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2505.05785.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.05785 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:01:54.249641Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

40 of 40 outbound references displayed

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External citation measurements

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Outbound references

Observation d46bc9f8-489b-470c-bbe2-85404b1899fd · outbound

This paper cites Invariant Risk Minimization.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Invariant Risk Minimization

Reference 1

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This paper cites Probability and measure theory.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Probability and measure theory

Reference 2

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This paper cites Graph neural networks in network neuroscience.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Graph neural networks in network neuroscience

Reference 3

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This paper cites Gccad: Graph contrastive learning for anomaly detection.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Gccad: Graph contrastive learning for anomaly detection

Reference 4

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This paper cites Learning causally invariant representations for out-of- distribution generalization on graphs.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Learning causally invariant representations for out-of- distribution generalization on graphs

Reference 5

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This paper cites Investigating out-of-distribution generalization of gnns: An architecture perspective.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Investigating out-of-distribution generalization of gnns: An architecture perspective

Reference 6

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Observation ddd13ec4-b3e3-45fa-8c39-e35fc33e7b5d · outbound

This paper cites Inductive representation learning on large graphs.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Inductive representation learning on large graphs

Reference 7

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Observation 220dfe7e-b8d2-4166-8970-0973635b84e3 · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Open graph benchmark: Datasets for machine learning on graphs

Reference 8

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

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Semi-supervised classification with graph convolutional networks

Reference 9

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This paper cites Klicpera, A.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Klicpera, A

Reference 10

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This paper cites Out-of-distribution generalization with maximal invariant predictor.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Out-of-distribution generalization with maximal invariant predictor

Reference 11

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Observation fc96d285-37ba-42bb-a443-6283b7f907e3 · outbound

This paper cites Estimating mutual information.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Estimating mutual information

Reference 12

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This paper cites Rethinking node-wise propagation for large-scale graph learning.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Rethinking node-wise propagation for large-scale graph learning

Reference 13

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This paper cites Revisiting graph contrastive learning from the perspective of graph spectrum.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Revisiting graph contrastive learning from the perspective of graph spectrum

Reference 14

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This paper cites Estimation of mutual information using kernel density estimators.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Estimation of mutual information using kernel density estimators

Reference 15

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This paper cites Geom-gcn: Geo- metric graph convolutional networks.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Geom-gcn: Geo- metric graph convolutional networks

Reference 16

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This paper cites Multi-scale attributed node embedding.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Multi-scale attributed node embedding

Reference 17

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Observation d1095c1d-91c2-43ed-93f6-279beb3e3eee · outbound

This paper cites Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models

Reference 18

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This paper cites Density estimation for statistics and data analysis.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Density estimation for statistics and data analysis

Reference 19

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Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Graph-based semi-supervised learning: A comprehensive review

Reference 20

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Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective The mu- tual information: detecting and evaluating dependencies between variables

Reference 21

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This paper cites Dirw: Path-aware digraph learning for heterophily.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Dirw: Path-aware digraph learning for heterophily

Reference 22

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This paper cites Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification

Reference 23

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This paper cites Rethinking graph neural networks for anomaly detection.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Rethinking graph neural networks for anomaly detection

Reference 24

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Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Graph attention networks

Reference 25

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This paper cites Recommending related products using graph neural networks in directed graphs.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Recommending related products using graph neural networks in directed graphs

Reference 26

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This paper cites Generative Risk Minimization for Out-of-Distribution Generalization on Graphs.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Generative Risk Minimization for Out-of-Distribution Generalization on Graphs

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This paper cites Graph out-of-distribution generalization via causal intervention.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Graph out-of-distribution generalization via causal intervention

Reference 28

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This paper cites Handling Distribution Shifts on Graphs: An Invariance Perspective.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Handling Distribution Shifts on Graphs: An Invariance Perspective

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This paper cites A comprehensive survey on graph neural networks.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective A comprehensive survey on graph neural networks

Reference 30

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This paper cites Learning invariant representations of graph neural networks via cluster generalization.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Learning invariant representations of graph neural networks via cluster generalization

Reference 31

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This paper cites PathMLP: Smooth Path Towards High-order Homophily.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective PathMLP: Smooth Path Towards High-order Homophily

Reference 32

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This paper cites How powerful are graph neural networks? 2019.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective How powerful are graph neural networks? 2019

Reference 33

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This paper cites Cohen, and Ruslan Salakhutdinov.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Cohen, and Ruslan Salakhutdinov

Reference 34

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Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Graph attention multi-layer perceptron

Reference 35

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This paper cites Hierarchical Graph Pooling with Structure Learning.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Hierarchical Graph Pooling with Structure Learning

Reference 36

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Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Ugrec: modeling directed and undirected relations for recommendation

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:01:54.472767Z

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This paper cites Graph neural networks: Taxonomy, advances, and trends.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Graph neural networks: Taxonomy, advances, and trends

Reference 38

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This paper cites Shift-robust gnns: Overcoming the limitations of localized graph training data.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Shift-robust gnns: Overcoming the limitations of localized graph training data

Reference 39

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This paper cites Mario: Model agnostic recipe for improving ood generalization of graph contrastive learning.

Rethinking Graph Out-Of-Distribution Generalization: A Learnable Random Walk Perspective Mario: Model agnostic recipe for improving ood generalization of graph contrastive learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:01:54.436953Z

Source-reported events for the cited work

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Pith citing papers

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