Paper Citation Record · LEDGER

RWR-GAE: Random Walk Regularization for Graph Auto Encoders

As of 16 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:1908.04003.

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

pith.paper-citation-record.v1
1908.04003 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:58:43.681773Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:55:13.407662Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-15T17:55:13.529101Z

Reference resolution

20 of 20 outbound references displayed

verified exact1
verified fuzzy14
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ec0f5af-59c8-4b68-94b3-814b642aed79 · outbound

This paper cites Laplacian eigenmaps and spectral techniques for embedding and clustering.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Laplacian eigenmaps and spectral techniques for embedding and clustering

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.559057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 958bf52f-8d4f-4f48-af8a-23c5adfde48e · outbound

This paper cites node2vec: Scalable feature learning for networks.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders node2vec: Scalable feature learning for networks

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.533752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 82a51834-0eb0-4593-9b7f-801666a366f6 · outbound

This paper cites Variational Graph Auto-Encoders.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Variational Graph Auto-Encoders

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-14T13:58:43.522198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9601a0c-3405-441f-829f-3da0b9b7d181 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Efficient Estimation of Word Representations in Vector Space

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-14T13:58:43.526643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4ec0e02-206a-4591-bd55-9a124998f38b · outbound

This paper cites Automatic multimedia cross-modal correlation discovery.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Automatic multimedia cross-modal correlation discovery

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.337636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3a6772f1-2ece-466a-954a-9b17588357dd · outbound

This paper cites Deepwalk: Online learning of social repre- sentations.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Deepwalk: Online learning of social repre- sentations

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.324411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d0e5a6b1-cba0-442d-8193-560f9c8915a4 · outbound

This paper cites Line: Large- scale information network embedding.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Line: Large- scale information network embedding

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.130283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 32ee83f0-d50b-424e-a5ce-1b1092c245e9 · outbound

This paper cites Mgae: Marginal- ized graph autoencoder for graph clustering.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Mgae: Marginal- ized graph autoencoder for graph clustering

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:43.998781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d3e01038-6f88-4d24-830f-66841eeb8e21 · outbound

This paper cites Network representa- tion learning with rich text information.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Network representa- tion learning with rich text information

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:43.903833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 08a9f49b-7e89-4225-9cba-12f5ec786315 · outbound

This paper cites Collective classiﬁcation in network data.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Collective classiﬁcation in network data

Reference 2000

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.158066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a46896ed-cfd6-48d3-a066-d8e6f0123c04 · outbound

This paper cites A survey on network embedding.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders A survey on network embedding

Reference 2002

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.546682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a8145e87-e815-4cac-9998-35622be87439 · outbound

This paper cites Adversarially Regularized Graph Autoencoder for Graph Embedding.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Adversarially Regularized Graph Autoencoder for Graph Embedding

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-14T13:58:43.535172Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d7d51ec-1fe3-4e4b-9d8e-99fa09d0e6d8 · outbound

This paper cites Leveraging social media networks for classiﬁcation.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Leveraging social media networks for classiﬁcation

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.145006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation abe9e34b-7674-4916-9203-a5660d0f9376 · outbound

This paper cites Auto-Encoding Variational Bayes.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Auto-Encoding Variational Bayes

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-14T13:58:43.512023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62858408-0316-4ee4-b707-35d4a82a991c · outbound

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

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Semi-Supervised Classification with Graph Convolutional Networks

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-14T13:58:43.517204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79ac5bfc-1c14-4832-82df-ca8c26a62ee1 · outbound

This paper cites Nonlinear dimensionality reduction by locally lin- ear embedding.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Nonlinear dimensionality reduction by locally lin- ear embedding

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.272916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a22e461d-6083-4fb7-9daf-240808352ea7 · outbound

This paper cites Acceler- ating t-sne using tree-based algorithms.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Acceler- ating t-sne using tree-based algorithms

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.116309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2907231c-15d9-4ab9-8321-a940ebed3779 · outbound

This paper cites It’s who you know: graph mining using recursive structural features.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders It’s who you know: graph mining using recursive structural features

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:44.442979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4a892cd2-9f5b-4ce3-a1b4-35bac7f097ed · outbound

This paper cites Robust multi-view spectral clustering via low-rank and sparse decomposition.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Robust multi-view spectral clustering via low-rank and sparse decomposition

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:58:43.919155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3b9c8dd8-0019-4451-86c4-1744d56fa524 · outbound

This paper cites Capturing Edge Attributes via Network Embedding.

RWR-GAE: Random Walk Regularization for Graph Auto Encoders Capturing Edge Attributes via Network Embedding

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-14T13:58:43.787380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation a71d655b-dd5c-41af-9844-ff3e8034dd5b · inbound

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing cites this paper.

Aggregation-aware MLP: An Unsupervised Approach for Graph Message-passing RWR-GAE: Random Walk Regularization for Graph Auto Encoders

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:55:13.535236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.