Paper Citation Record · LEDGER

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods

As of 18 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2505.16466.

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

pith.paper-citation-record.v1
2505.16466 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:04:11.263323Z

measured 19 of 19 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

19 of 19 outbound references displayed

verified exact1
verified fuzzy17
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4eb09e35-f0e9-4f91-a44d-7ceb40d12952 · outbound

This paper cites Trustworthiness-aware knowledge graph representation for recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Trustworthiness-aware knowledge graph representation for recommendation,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.870598Z

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 4c69bb58-fbb4-44a2-96eb-936729120d63 · outbound

This paper cites Skipnode: On alleviating performance degradation for deep graph convolutional networks,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Skipnode: On alleviating performance degradation for deep graph convolutional networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.784789Z

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 8b966263-2eb3-4dd4-9e8a-a8d79d9df50a · outbound

This paper cites Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:04:10.378595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a6f76c8-1f00-40bd-a6b7-6611b5a59a6f · outbound

This paper cites Hcof: Hybrid collaborative filtering using social and semantic suggestions for friend recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Hcof: Hybrid collaborative filtering using social and semantic suggestions for friend recommendation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.718659Z

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 54326ed7-39ec-46ca-92c9-cccf3e8b4237 · outbound

This paper cites Adaptive denoising graph con- trastive learning with memory graph attention for recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Adaptive denoising graph con- trastive learning with memory graph attention for recommendation,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.698179Z

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 3841fef4-71e9-40c9-92b5-01e20d7273d9 · outbound

This paper cites Lightgcn: Simplifying and powering graph convolution network for recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Lightgcn: Simplifying and powering graph convolution network for recommendation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.670033Z

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 f0448ad8-8f93-43cc-9888-32fc7e40141b · outbound

This paper cites Kgcl: A knowledge-enhanced graph contrastive learning framework for session-based recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Kgcl: A knowledge-enhanced graph contrastive learning framework for session-based recommendation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.606720Z

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 3d144ff6-0a0a-4b82-8c75-5f6b8811a9e3 · outbound

This paper cites Kgat: Knowledge graph at- tention network for recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Kgat: Knowledge graph at- tention network for recommendation,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.531883Z

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 454c49dd-8139-4327-90ea-11eeda5b9c4f · outbound

This paper cites Mvin: Learning multiview items for recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Mvin: Learning multiview items for recommendation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.450313Z

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 d43651c6-3819-4d57-a0ce-660395918949 · outbound

This paper cites Graph-augmented co-attention model for socio-sequential recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Graph-augmented co-attention model for socio-sequential recommendation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.371008Z

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 7b0fb6ea-7b4c-402e-8089-7c8f79f46114 · outbound

This paper cites Self-supervised global graph neural networks with enhance-attention for session-based recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Self-supervised global graph neural networks with enhance-attention for session-based recommendation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.289001Z

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 cba7e0d1-1b9e-4089-b7fd-571efc979fc0 · outbound

This paper cites Robust graph recommendation via noise- aware adversarial perturbation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Robust graph recommendation via noise- aware adversarial perturbation,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.154507Z

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 11e185c6-1e22-4318-bd47-9c5949a3af14 · outbound

This paper cites E-commerce search via content collaborative graph neural network,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods E-commerce search via content collaborative graph neural network,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:12.106108Z

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 d31ca036-f32e-42de-89b3-cebf2cf601e4 · outbound

This paper cites Youtube video recommendation using user-based collab- orative filtering and graph neural networks approaches to improve users per- sonalized experience.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Youtube video recommendation using user-based collab- orative filtering and graph neural networks approaches to improve users per- sonalized experience

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:11.916548Z

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 56d114f9-456e-4009-9561-c2227b195a30 · outbound

This paper cites Dual variational graph recon- struction learning for social recommendation,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Dual variational graph recon- struction learning for social recommendation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:11.794546Z

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 696380a0-c7e7-4a50-a57a-08c58c4b99a0 · outbound

This paper cites Be confident! towards trustworthy graph neural networks via confidence calibration,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Be confident! towards trustworthy graph neural networks via confidence calibration,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:11.763471Z

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 f9272eae-15ea-40e2-a4e5-1a2f2a81c4e7 · outbound

This paper cites Nodemixup: Tackling under- reaching for graph neural networks,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Nodemixup: Tackling under- reaching for graph neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:11.672831Z

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 dc5b3b53-14c4-410d-a8f4-b49b7cb12efa · outbound

This paper cites AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:04:11.341976Z

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 962eaffc-bd8c-44d1-9c60-dfc11c643508 · outbound

This paper cites Truthsr: trustworthy sequential recommender systems via user-generated multimodal content,.

Conf-GNNRec: Quantifying and Calibrating the Prediction Confidence for GNN-based Recommendation Methods Truthsr: trustworthy sequential recommender systems via user-generated multimodal content,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:04:11.520299Z

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

No inbound Pith citation observations are available.