Paper Citation Record · LEDGER

Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1909.03496.

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

pith.paper-citation-record.v1
1909.03496 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:26:32.168028Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T09:21:24.874228Z

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 b172cd1f-b723-4234-82f6-b52d55f617fa · inbound

Toward Neurosymbolic Program Comprehension cites this paper.

Toward Neurosymbolic Program Comprehension Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-09T14:26:32.168028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bc9d358-d3ad-4345-b729-e2e01132dd3d · inbound

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation cites this paper.

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-05T16:10:53.528396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fab5bd8a-9804-4e35-8439-0d267af91aed · inbound

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection cites this paper.

From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T17:13:51.285401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f00da7eb-0615-42ad-9b0e-05b5872e360b · inbound

RepoDoc: A Knowledge Graph-Based Framework to Automatic Documentation Generation and Incremental Updates cites this paper.

RepoDoc: A Knowledge Graph-Based Framework to Automatic Documentation Generation and Incremental Updates Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:21:24.876221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3faaf239-eae5-4ac7-8801-f8443eb24608 · inbound

Lightweight Vulnerability Detection from Code Metrics and Token Features cites this paper.

Lightweight Vulnerability Detection from Code Metrics and Token Features Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:36:06.642488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 038e00ac-be60-429a-9346-c8f39a3d063e · inbound

LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection cites this paper.

LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T03:06:24.801039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.