Pith. sign in

Paper Citation Record · LEDGER

Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2111.04314.

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

pith.paper-citation-record.v1
2111.04314 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:47:09.158971Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:36:08.776287Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5d03f8bf-8624-492b-8ec4-0d2e38d5c09e · inbound

Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis cites this paper.

Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T22:47:09.158971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:47:09.158971Z digest=sha256:dbdfbedd65ebd8fb3e30ddfc8388a5fdf7eb665158cd1c1d5211af937c05aead

Observation f842e216-c0f1-4717-bd44-332ea3e18d56 · inbound

Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation cites this paper.

Adversarial Graph Neural Network Benchmarks: Towards Practical and Fair Evaluation Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning

Reference 71

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:36:08.778632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-09T15:58:09.348623Z digest=sha256:fa6330fd2b61392624620f35b4a94e38164a9cb5c4018f7e5b5509c066a8c0de