Paper Citation Record · LEDGER

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation

As of 14 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2507.07414.

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

pith.paper-citation-record.v1
2507.07414 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:50:08.593287Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

21 of 21 outbound references displayed

verified exact3
verified fuzzy1
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1effeabc-bcc4-48f5-a112-abc363e01001 · outbound

This paper cites Review of lightweight deep convolutional neural networks.Archives of Computational Methods in Engineering, 31(4):1915–1937,.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation Review of lightweight deep convolutional neural networks.Archives of Computational Methods in Engineering, 31(4):1915–1937,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:50:09.172634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fe655db0-fadc-4d95-9321-311103a430ac · outbound

This paper cites Generating Long Sequences with Sparse Transformers.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation Generating Long Sequences with Sparse Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.526215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdc13029-92b9-4b87-8f34-9afaa11fb040 · outbound

This paper cites doi:https://doi.org/10.1016/j.asoc.2024.112631.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation doi:https://doi.org/10.1016/j.asoc.2024.112631

Reference 11

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:50:08.946588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9b6b155d-f7e7-45c4-a692-731e9142d26d · outbound

This paper cites doi:10.1162/tacl_a_00461.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation doi:10.1162/tacl_a_00461

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.552027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d713eb94-69a7-433e-99ca-2ea94e0bbda2 · outbound

This paper cites doi:10.1162/tacl_a_00448.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation doi:10.1162/tacl_a_00448

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.556831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6d11aee-27f3-403e-b068-f0768f179c0a · outbound

This paper cites An Introduction to Convolutional Neural Networks.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation An Introduction to Convolutional Neural Networks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.562920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b050311a-c3ea-46b2-8dee-c11567c460e6 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.593287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ea97d15-db01-4e13-a495-b7f546918062 · outbound

This paper cites What graph neural networks cannot learn: depth vs width.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation What graph neural networks cannot learn: depth vs width

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.542762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63871731-3f66-4970-8d8f-891762aeeeb4 · outbound

This paper cites A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.579271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a0a5544-e40a-4d5b-9cb4-c87dcd5837c2 · outbound

This paper cites Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation

Reference 2013

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:50:08.708685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fec9ca18-2d2c-4d00-87de-1cdd1d1bb07a · outbound

This paper cites Distilling the Knowledge in a Neural Network.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation Distilling the Knowledge in a Neural Network

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.491448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45e22be1-2f7a-4fa1-a9fd-a8690cf2a039 · outbound

This paper cites URL https: //ojs.aaai.org/index.php/AAAI/article/view/10362.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation URL https: //ojs.aaai.org/index.php/AAAI/article/view/10362

Reference 2016

Resolution: verified exact; doi, observed 2026-08-06T18:50:08.650455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6d574286-7043-450a-888a-ade0f6c1f3c7 · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901,

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.506044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 383418a3-405d-47cc-8bdb-8bb76601dafd · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.482781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d15c35b-019e-4ed9-bdfa-6b90cc2dc2d7 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.536449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93b399b1-f9ef-46cf-a52b-12aa1f22a381 · outbound

This paper cites FastBERT: a Self-distilling BERT with Adaptive Inference Time.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation FastBERT: a Self-distilling BERT with Adaptive Inference Time

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.510313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dba012b-3e03-4a9f-97e4-aa3c7543d91f · outbound

This paper cites doi:https://doi.org/10.1016/j.ymssp.2020.107398.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation doi:https://doi.org/10.1016/j.ymssp.2020.107398

Reference 2021

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:50:08.814200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 058e6369-1db3-4c0c-8ae7-0fa3aaaac638 · outbound

This paper cites The Cost of Training NLP Models: A Concise Overview.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation The Cost of Training NLP Models: A Concise Overview

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.475355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18b5baa5-e16c-4c60-bcc9-f276e6b5d9ac · outbound

This paper cites doi:https://doi.org/10.1016/j.neucom.2023.126808.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation doi:https://doi.org/10.1016/j.neucom.2023.126808

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T18:50:08.498974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b27d6ebf-6167-4649-a7d4-6c15125fa4ad · outbound

This paper cites Rubén Romero, Pedro Celard, José Manuel Sorribes-Fdez, A Seara Vieira, Eva Lorenzo Iglesias, and L Borrajo.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation Rubén Romero, Pedro Celard, José Manuel Sorribes-Fdez, A Seara Vieira, Eva Lorenzo Iglesias, and L Borrajo

Reference 2024

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T18:50:09.047105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 72b5a1e4-a685-4e60-9285-9041480928d9 · outbound

This paper cites Andrei Paleyes, Raoul-Gabriel Urma, and Neil D Lawrence.

GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text Representation Andrei Paleyes, Raoul-Gabriel Urma, and Neil D Lawrence

Reference 2025

Resolution: verified exact; doi, observed 2026-08-06T18:50:08.668176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

No inbound Pith citation observations are available.