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Paper Citation Record · LEDGER

Enhancing Graph Representation Learning with Localized Topological Features

As of 21 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2501.09178.

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

pith.paper-citation-record.v1
2501.09178 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:16:36.724406Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

  • verified exact2
  • verified fuzzy6
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f3a4462e-fb6b-4363-ace8-0fc87689202d · outbound

This paper cites Link Prediction without Graph Neural Networks.

Enhancing Graph Representation Learning with Localized Topological Features Link Prediction without Graph Neural Networks

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-10T20:16:36.685222Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:16:36.685222Z digest=sha256:6b5b9a19e931acf8367985c6e84f0a621163c8741ab952d7b8e1c26a8a2eb8f3

Observation d4d691c6-7de9-4556-8857-dc192b137bb8 · outbound

This paper cites Chien-Chun Ni, Yu-Yao Lin, Jie Gao, and Xianfeng Gu.

Enhancing Graph Representation Learning with Localized Topological Features Chien-Chun Ni, Yu-Yao Lin, Jie Gao, and Xianfeng Gu

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:16:36.876533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T20:16:36.701092Z digest=sha256:88fa6e898d39eaf494e32bf3662bb2516d275bc8f74751ec4787a60daebf9932

Observation c27a3d34-b1f2-4608-bafe-fa561d4a9e82 · outbound

This paper cites A limit theorem for the $1$st Betti number of layer-$1$ subgraphs in random graphs.

Enhancing Graph Representation Learning with Localized Topological Features A limit theorem for the $1$st Betti number of layer-$1$ subgraphs in random graphs

Reference 13

Resolution
metadata mismatch
local_arxiv, observed 2026-08-10T20:16:36.787590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T20:16:36.719630Z digest=sha256:a7f058012c74ebc1b6dc2a268607f19fbe3a67ded140d00012e2bc50b07ffcc2

Observation cbd6f048-dd22-4b0d-9395-49e4eeb6c2ba · outbound

This paper cites Topogan: A topology-aware generative adversarial network.

Enhancing Graph Representation Learning with Localized Topological Features Topogan: A topology-aware generative adversarial network

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:16:36.863976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T20:16:36.724406Z digest=sha256:086c8934c963014d62d8770086730006bcabb3fc46acc9368907adc910509623

Observation 0b37162a-7720-4c96-94f3-af5b1b9cc386 · outbound

This paper cites doi: 10.1016/S0195-6698(80)80030-8.

Enhancing Graph Representation Learning with Localized Topological Features doi: 10.1016/S0195-6698(80)80030-8

Reference 1980

Resolution
unresolved
no resolver link, observed 2026-08-10T20:16:36.661362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:16:36.661362Z digest=sha256:6d1f7cbd33f0f59cd6f8926768af888f12dd8b7e4d16b0512e52b5a66219dc0b

Observation 8777bdb6-9e66-4f55-9dcd-eb7f2cb0750e · outbound

This paper cites Jin-Yi Cai, Martin F¨ urer, and Neil Immerman.

Enhancing Graph Representation Learning with Localized Topological Features Jin-Yi Cai, Martin F¨ urer, and Neil Immerman

Reference 1982

Resolution
verified exact
doi, observed 2026-08-10T20:16:36.762183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T20:16:36.666792Z digest=sha256:f292c3fb30477df28b789b9435ba862c563fa4a1853a7e865940cb46cb60834b

Observation a42d4c80-462d-4f76-a71a-8f2b342d782a · outbound

This paper cites Pitfalls of Graph Neural Network Evaluation.

Enhancing Graph Representation Learning with Localized Topological Features Pitfalls of Graph Neural Network Evaluation

Reference 2008

Resolution
unresolved
no resolver link, observed 2026-08-10T20:16:36.705300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:16:36.705300Z digest=sha256:833d5cbd3ce443c1e6b03b19b98d59b178b2aa1222fade85435fe85c37ab4853

Observation 326d34dc-6623-49c9-9fb8-945c087104a8 · outbound

This paper cites Persistence weighted gaussian kernel for topological data analysis.

Enhancing Graph Representation Learning with Localized Topological Features Persistence weighted gaussian kernel for topological data analysis

Reference 2010

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:16:36.905795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T20:16:36.690156Z digest=sha256:7e4d52939b0e7a68b5eae7cac66471d33d6c975a6836340d5b398399d87048ac

Observation 140f6ab5-8c7f-46c5-8337-8c2340b4133d · outbound

This paper cites Ripsnet: a general architecture for fast and robust estimation of the persistent homology of point clouds.

Enhancing Graph Representation Learning with Localized Topological Features Ripsnet: a general architecture for fast and robust estimation of the persistent homology of point clouds

Reference 2013

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:16:36.932499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T20:16:36.676367Z digest=sha256:828eafff02fe1effc46bf97248daf76cb8b95a8aa3d72f5edb127e75e59efec2

Observation d77f2004-7333-4d0a-8860-2847cb0a4d86 · outbound

This paper cites Graph u-nets.

Enhancing Graph Representation Learning with Localized Topological Features Graph u-nets

Reference 2016

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:16:36.918879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T20:16:36.681012Z digest=sha256:621b80e8db42ea2707d8717034c6184b60bcc79cd7d80b73183f0768770c73ea

Observation 1173672a-69ea-4d6d-9083-dd547744bd7e · outbound

This paper cites Wasserstein Stability for Persistence Diagrams.

Enhancing Graph Representation Learning with Localized Topological Features Wasserstein Stability for Persistence Diagrams

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-10T20:16:36.709934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:16:36.709934Z digest=sha256:4e67167d8b1dc82daab8d8b0ff0040e22fd97312d477725232040d443e641295

Observation db292172-4480-440b-a9ea-e90b00650f65 · outbound

This paper cites The density of expected persistence diagrams and its kernel based estimation.

Enhancing Graph Representation Learning with Localized Topological Features The density of expected persistence diagrams and its kernel based estimation

Reference 2019

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:16:36.851131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T20:16:36.671781Z digest=sha256:961927f3b62b97ad75b524525d9d72d9fbc341db6a8da0bd2ba47875eef5b09a

Observation 19b0fd9a-0aad-4942-b77e-7ca79689697e · outbound

This paper cites Notes on an Elementary Proof for the Stability of Persistence Diagrams.

Enhancing Graph Representation Learning with Localized Topological Features Notes on an Elementary Proof for the Stability of Persistence Diagrams

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-10T20:16:36.714876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:16:36.714876Z digest=sha256:b83cb4974270f752d5048256e2fc6bec5652bf146726b2bf8ed9570d15db5a2b

Observation b318fcbf-39ca-4c0a-b30b-bdc5505addcc · outbound

This paper cites Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe.

Enhancing Graph Representation Learning with Localized Topological Features Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-10T20:16:36.891721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-10T20:16:36.694737Z digest=sha256:362b26b46ac11206ee33a8906dda203bba35093ad86a3610044aff311c8b588e

Pith citing papers

No inbound Pith citation observations are available.