Paper Citation Record · LEDGER

CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2506.17709.

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

pith.paper-citation-record.v1
2506.17709 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:23:13.439026Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:09:14.388380Z

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 5a5009c7-f14d-4d84-9613-fc717f72f050 · inbound

A Survey on Model Extraction Attacks and Defenses for Large Language Models cites this paper.

A Survey on Model Extraction Attacks and Defenses for Large Language Models CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T22:23:13.439026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7da29426-68ae-4450-92f5-f9a3783f36cc · inbound

DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning cites this paper.

DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T19:25:28.196466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39db13fd-f616-434e-b7ee-f04926944dc0 · inbound

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives cites this paper.

A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Reference 217

Resolution: unresolved; no resolver link, observed 2026-08-05T18:12:37.973991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bfcbb38-ced0-470b-8645-bbb2a888207b · inbound

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses cites this paper.

Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-05T15:39:55.634206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0506d90-6770-4f46-a0f0-b352cc18da63 · inbound

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? cites this paper.

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-08-04T01:57:34.338767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fd8474ff-6a06-494e-96ac-ee4ed265d4b0 · inbound

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? cites this paper.

GraphIP-Bench: How Hard Is It to Steal a Graph Neural Network, and Can We Stop It? CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-08-04T01:57:34.338767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9e88e215-a447-4eb0-a124-577c0ccbad58 · inbound

Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks? cites this paper.

Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks? CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-08-04T01:57:34.338767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fae325ff-54dd-4231-8bc1-8c407913781e · inbound

AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network cites this paper.

AGDN: Learning to Solve Traveling Salesman Problem with Anisotropic Graph Diffusion Network CEGA: A Cost-Effective Approach for Graph-Based Model Extraction and Acquisition

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-08-04T01:57:34.338767Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.