Paper Citation Record · LEDGER

Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2101.08543.

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

pith.paper-citation-record.v1
2101.08543 v2

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 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:12:02.193337Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:49:41.997557Z

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 7a42a73f-7b23-4e6b-809f-ffa0246fb6d0 · inbound

Retrieval-Augmented Generation with Graphs (GraphRAG) cites this paper.

Retrieval-Augmented Generation with Graphs (GraphRAG) Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

Reference 171

Resolution: verified exact; arxiv_id, observed 2026-05-18T04:33:39.210325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7670c84c-79af-4022-bc97-85e25bfa6c94 · inbound

Robust Anomaly Detection with Graph Neural Networks using Controllability cites this paper.

Robust Anomaly Detection with Graph Neural Networks using Controllability Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:19:28.372634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9669e296-250e-4954-887a-5aba78a46b0a · inbound

Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach cites this paper.

Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T23:14:01.549707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9c553e15-565e-4901-9d13-147ee3d7d7b0 · inbound

A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction cites this paper.

A Zero-shot Generalized Graph Anomaly Detection Framework via Node Reconstruction Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:17:57.308463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d9f2b7fb-ffcb-468f-b276-991a9405c41a · inbound

Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation cites this paper.

Enhancing LLMs for Graph Tasks via Graph-aware LoRA Generation Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

Reference 183

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:49:41.999861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation a5668b7c-9aaa-4385-adf8-f31a5d550d5e · inbound

ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes cites this paper.

ProTAGAD: A Foundation Model for TAG Anomaly Detection with Decoupled Topological and Textual Prototypes Boost then Convolve: Gradient Boosting Meets Graph Neural Networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T19:12:02.193337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.