Paper Citation Record · LEDGER

Inferring the Graph Structure of Images for Graph Neural Networks

As of 5 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 1 inbound Pith citation observation for arXiv:2509.04677.

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

pith.paper-citation-record.v1
2509.04677 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T06:01:08.931447Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T06:01:08.874253Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T06:01:08.959205Z

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy14
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2bce1fa6-d1b8-4539-afcc-eb63f2a1dce5 · outbound

This paper cites Inferring the Graph Structure of Images for Graph Neural Networks.

Inferring the Graph Structure of Images for Graph Neural Networks Inferring the Graph Structure of Images for Graph Neural Networks

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T06:01:08.965623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9fc03acf-2976-4ef8-af1b-d2e52086512a · outbound

This paper cites We first produce a row and column correlation graph using the method in [5, 6].

Inferring the Graph Structure of Images for Graph Neural Networks We first produce a row and column correlation graph using the method in [5, 6]

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.129887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 760c4f51-db45-4005-85f0-1eceba00d42a · outbound

This paper cites Both datasets have 70,000 images with 60,000 im- ages for training and 10,000 for testing.

Inferring the Graph Structure of Images for Graph Neural Networks Both datasets have 70,000 images with 60,000 im- ages for training and 10,000 for testing

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.109908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation e5da0ad2-ceca-4824-beea-71ac00fc8fbe · outbound

This paper cites an unresolved cited work.

Inferring the Graph Structure of Images for Graph Neural Networks Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-05T06:01:09.100064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d480316e-b04b-4a8e-8c60-2ea5f95024b0 · outbound

This paper cites We achieve this by inferring the underlying graph for im- ages using the correlation method in [5, 6].

Inferring the Graph Structure of Images for Graph Neural Networks We achieve this by inferring the underlying graph for im- ages using the correlation method in [5, 6]

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.090379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation deead37f-9b20-4b19-a391-f518402a0dfd · outbound

This paper cites an unresolved cited work.

Inferring the Graph Structure of Images for Graph Neural Networks Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-05T06:01:09.120242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 686c802f-e02d-4f0c-b462-92c529620d1d · outbound

This paper cites Semi-supervised classi- fication with graph convolutional networks,.

Inferring the Graph Structure of Images for Graph Neural Networks Semi-supervised classi- fication with graph convolutional networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.080774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9e3d06d3-a0f1-426c-b058-4c59732c9d30 · outbound

This paper cites Topology adaptive graph convolutional networks,.

Inferring the Graph Structure of Images for Graph Neural Networks Topology adaptive graph convolutional networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.071213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 240d8c0c-13f8-4724-a140-f42d4106b792 · outbound

This paper cites Graph attention networks,.

Inferring the Graph Structure of Images for Graph Neural Networks Graph attention networks,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.061188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3c5461fa-65cb-475c-9473-5e3a4a8aef88 · outbound

This paper cites Can classic gnns be strong baselines for graph-level tasks? simple architectures meet excellence,.

Inferring the Graph Structure of Images for Graph Neural Networks Can classic gnns be strong baselines for graph-level tasks? simple architectures meet excellence,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.051642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 956b1cf0-5de1-4388-b02b-b985eada402f · outbound

This paper cites Learning the causal structure of networked dynam- ical systems under latent nodes and structured noise,.

Inferring the Graph Structure of Images for Graph Neural Networks Learning the causal structure of networked dynam- ical systems under latent nodes and structured noise,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.041670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ef55a910-2c90-4a33-8773-891772784d37 · outbound

This paper cites Inferring the graph of networked dynamical sys- tems under partial observability and spatially colored noise,.

Inferring the Graph Structure of Images for Graph Neural Networks Inferring the graph of networked dynamical sys- tems under partial observability and spatially colored noise,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.031696Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 176d46a3-5e0a-4e5c-ad0c-835149260990 · outbound

This paper cites Graph convolutional networks for image classification: Comparing approaches for building graphs from images,.

Inferring the Graph Structure of Images for Graph Neural Networks Graph convolutional networks for image classification: Comparing approaches for building graphs from images,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.020950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bc9bbf99-5067-4254-8aed-360184238656 · outbound

This paper cites Slic superpixels,.

Inferring the Graph Structure of Images for Graph Neural Networks Slic superpixels,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:09.009880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 5b323619-d3c3-4fc8-96d2-1616bee83bda · outbound

This paper cites Discrete signal processing on graphs,.

Inferring the Graph Structure of Images for Graph Neural Networks Discrete signal processing on graphs,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:08.999314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a577d13d-3e68-466a-8c26-3e32494b31b5 · outbound

This paper cites Graph signal processing: Overview, challenges, and applications,.

Inferring the Graph Structure of Images for Graph Neural Networks Graph signal processing: Overview, challenges, and applications,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:08.988072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6f509ea7-b924-452e-b9f2-9b36dc30ffcd · outbound

This paper cites Graph signal processing: The 2d companion model,.

Inferring the Graph Structure of Images for Graph Neural Networks Graph signal processing: The 2d companion model,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T06:01:08.976781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

Observation 2bce1fa6-d1b8-4539-afcc-eb63f2a1dce5 · inbound