Paper Citation Record · LEDGER

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective

As of 18 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2502.01936.

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

pith.paper-citation-record.v1
2502.01936 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:00:12.178454Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T17:24:45.043933Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

verified exact0
verified fuzzy22
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 107af564-0ce1-4c13-8f58-3f2f0fc1f9a4 · outbound

This paper cites Understanding and improving graph injection at- tack by promoting unnoticeability.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Understanding and improving graph injection at- tack by promoting unnoticeability

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.647387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 888b543d-3ac9-4deb-bbe5-1f1d39ef8f87 · outbound

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

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Open graph benchmark: Datasets for machine learning on graphs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-09T14:00:12.102111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2dc3df71-0c18-4b57-a864-e7449613f1c9 · outbound

This paper cites Revisiting graph adversarial attack and defense from a data distribution perspective.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Revisiting graph adversarial attack and defense from a data distribution perspective

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.568561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 33236eb9-5589-4a75-ba3a-0d5bbb28551d · outbound

This paper cites Simple black-box ad- versarial attacks on deep neural networks.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Simple black-box ad- versarial attacks on deep neural networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.559284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b6e172f0-7039-4090-935f-caa82fae4684 · outbound

This paper cites Collective classification in network data.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Collective classification in network data

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T14:00:12.138824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f4074c7-3962-453f-a851-2b49aeb05f6d · outbound

This paper cites Single node injection attack against graph neural networks.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Single node injection attack against graph neural networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.534524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation eb9d4791-27e2-4839-b296-c9270f957ad8 · outbound

This paper cites Graph attention networks.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Graph attention networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-09T14:00:12.146752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9003b6cc-7dc8-4e04-b4fe-831112a1f46e · outbound

This paper cites BRUSLEATTACK: A QUERY- EFFICIENT SCORE- BASED BLACK-BOX SPARSE ADVERSARIAL ATTACK.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective BRUSLEATTACK: A QUERY- EFFICIENT SCORE- BASED BLACK-BOX SPARSE ADVERSARIAL ATTACK

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.519226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 77daff65-a2b4-4fe7-b033-c2f111ca9cc1 · outbound

This paper cites Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Cluster Attack: Query-based Adversarial Attacks on Graphs with Graph-Dependent Priors

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T14:00:12.155802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95a7e58e-75e5-46fd-8403-061225a74526 · outbound

This paper cites Topol- ogy attack and defense for graph neural networks: An op- timization perspective.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Topol- ogy attack and defense for graph neural networks: An op- timization perspective

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.499746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dea2249c-a015-4e43-8d67-ad131a7b6236 · outbound

This paper cites Revisiting semi-supervised learning with graph embeddings.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Revisiting semi-supervised learning with graph embeddings

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.490851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0528dfd3-9359-4e40-8b7a-3595d54596cd · outbound

This paper cites Mini- mum topology attacks for graph neural networks.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Mini- mum topology attacks for graph neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.458344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 11d5fb0e-a17d-401d-8084-cb7777e7a348 · outbound

This paper cites Graph robustness benchmark: Benchmarking the adver- sarial robustness of graph machine learning.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Graph robustness benchmark: Benchmarking the adver- sarial robustness of graph machine learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.347917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 076402df-a48e-4b53-9cff-5939cdc532f6 · outbound

This paper cites Tdgia: Effective injection attacks on graph neural networks.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Tdgia: Effective injection attacks on graph neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.257680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0141f10e-84dd-4970-9da2-de07e7765bbd · outbound

This paper cites Adversarial attacks on neural net- works for graph data.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Adversarial attacks on neural net- works for graph data

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.225372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6dfa92f7-1249-43c9-8cee-5f98eb8a10a6 · outbound

This paper cites Adversarial attacks on graph neural networks via meta learning.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Adversarial attacks on graph neural networks via meta learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.215362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 73d6769a-9a19-416b-b91e-e2d8f5484665 · outbound

This paper cites Adversarial attacks on graph neural networks via node injections: A hierar- chical reinforcement learning approach.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Adversarial attacks on graph neural networks via node injections: A hierar- chical reinforcement learning approach

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.544340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7c37ab52-1a17-4a18-930d-fe28c7ef79bd · outbound

This paper cites Gnnguard: Defending graph neural networks against ad- versarial attacks.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Gnnguard: Defending graph neural networks against ad- versarial attacks

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.481479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 22445b2a-eedc-49ea-a44f-403cc8f8184b · outbound

This paper cites Query-efficient and scalable black-box adversarial attacks on discrete sequential data via bayesian optimization.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Query-efficient and scalable black-box adversarial attacks on discrete sequential data via bayesian optimization

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.577799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 40904e50-a59a-4f72-a803-e909fdddf6cd · outbound

This paper cites Combining neural networks with personalized pagerank for classification on graphs.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Combining neural networks with personalized pagerank for classification on graphs

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.620258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 50cbccf7-81ad-440c-86d4-22b33d13cbc6 · outbound

This paper cites Adversarial attack on graph structured data.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Adversarial attack on graph structured data

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.629264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ac6feda7-1b2c-495e-8ca5-0273ec48cbdf · outbound

This paper cites Could graph neural networks learn better molecular representa- tion for drug discovery? a comparison study of descriptor- based and graph-based models.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Could graph neural networks learn better molecular representa- tion for drug discovery? a comparison study of descriptor- based and graph-based models

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.605754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation caf93477-0c97-4d2a-be36-2011cc4b6fb8 · outbound

This paper cites Let graph be the go board: gradient-free node injection attack for graph neural networks via reinforce- ment learning.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Let graph be the go board: gradient-free node injection attack for graph neural networks via reinforce- ment learning

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.596440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 263e4840-2766-45a1-b6ac-6485dbf154dc · outbound

This paper cites Sparse and imperceivable adversarial attacks.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Sparse and imperceivable adversarial attacks

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.638096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4db5a650-c06e-4943-87eb-63b2d37f61d9 · outbound

This paper cites Kipf and Max Welling.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Kipf and Max Welling

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.586802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2060eb35-78af-4210-92cf-8bfebd862771 · outbound

This paper cites Scalable attack on graph data by injecting vicious nodes.Data Min- ing and Knowledge Discovery, 34:1363–1389,.

Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective Scalable attack on graph data by injecting vicious nodes.Data Min- ing and Knowledge Discovery, 34:1363–1389,

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:00:12.509241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation f9e9935d-502b-4109-aed6-134502fbcf2d · inbound

PEANUT: Perturbations by Eigenvector Alignment for Attacking Graph Neural Networks Under Topology-Driven Message Passing cites this paper.

PEANUT: Perturbations by Eigenvector Alignment for Attacking Graph Neural Networks Under Topology-Driven Message Passing Query-Based and Unnoticeable Graph Injection Attack from Neighborhood Perspective

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T17:24:45.043933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.