Paper Citation Record · LEDGER

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs

As of 22 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2411.16127.

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

pith.paper-citation-record.v1
2411.16127 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:37:37.587899Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T22:52:59.081816Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:16:01.138485Z

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy14
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4411b5c7-0a51-490d-b879-9f4eb29cf012 · outbound

This paper cites Attention is all you need.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Attention is all you need

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.473101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2dd2b69-8937-469d-8fd2-b794f7a57932 · outbound

This paper cites Graph attention networks.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Graph attention networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.477577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e530618f-e48e-4f96-a281-091384177706 · outbound

This paper cites How Attentive are Graph Attention Networks?.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs How Attentive are Graph Attention Networks?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.482448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2ca54ef-7b8d-4b4b-9fc0-295543dd67e3 · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs A Generalization of Transformer Networks to Graphs

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.486846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42fe1a6b-688f-46ba-b00b-6700dd67b283 · outbound

This paper cites Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems, 34:28877–28888, 2021.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems, 34:28877–28888, 2021

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.925668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5d179db4-03c3-4c42-9bcf-a8a3c6240eb7 · outbound

This paper cites Recipe for a general, powerful, scalable graph transformer.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Recipe for a general, powerful, scalable graph transformer

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.913704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation addeb81e-865b-44d0-8ddd-7e91b5a52bea · outbound

This paper cites Attention-based Graph Neural Network for Semi-supervised Learning.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Attention-based Graph Neural Network for Semi-supervised Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.501244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c32b262e-339b-4467-96d2-298db7cb087c · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Semi-Supervised Classification with Graph Convolutional Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.505423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 647cc6d4-93a4-4ff8-92b1-0f513c5de19a · outbound

This paper cites How Powerful are Graph Neural Networks?.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs How Powerful are Graph Neural Networks?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.509600Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94591dbd-f286-42a8-9390-1eb513221c27 · outbound

This paper cites Link prediction based on graph neural networks.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Link prediction based on graph neural networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.513789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58837d9f-9d39-4154-9c0d-dfce1a9f5fbe · outbound

This paper cites Neural bellman-ford networks: A general graph neural network framework for link prediction.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Neural bellman-ford networks: A general graph neural network framework for link prediction

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.517389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef7c833c-8326-4c0c-b557-14fc7f10de76 · outbound

This paper cites A Fair Comparison of Graph Neural Networks for Graph Classification.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs A Fair Comparison of Graph Neural Networks for Graph Classification

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.521340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3088a84-760d-4c36-a10c-1a50f2bcfe51 · outbound

This paper cites InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.525747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bb86616-9626-4ae4-b588-1b35757dce90 · outbound

This paper cites Deep graph library: Towards efficient and scalable deep learning on graphs.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Deep graph library: Towards efficient and scalable deep learning on graphs

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.885544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation aa208e5d-89ae-4eab-b14b-58852f1633c8 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Fast Graph Representation Learning with PyTorch Geometric

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.533268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5515057b-5f25-4647-9d34-96d121e1187b · outbound

This paper cites Neural message passing for quantum chemistry.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Neural message passing for quantum chemistry

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.537152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4171d66-fb79-4e21-be87-28093046482b · outbound

This paper cites Seastar: vertex-centric programming for graph neural networks.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Seastar: vertex-centric programming for graph neural networks

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.864306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation eaaea74e-26b7-42d1-84a8-4cd8730ce540 · outbound

This paper cites Understanding gnn computational graph: A coordinated computation, io, and memory perspective.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Understanding gnn computational graph: A coordinated computation, io, and memory perspective

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.850887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation cc04967d-1646-49ff-970f-ff1ba851155f · outbound

This paper cites Tlpgnn: A lightweight two-level parallelism paradigm for graph neural network computation on gpu.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Tlpgnn: A lightweight two-level parallelism paradigm for graph neural network computation on gpu

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.838329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7aa34703-6eaa-45b1-8c0d-5087eee17270 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Pytorch: An imperative style, high-performance deep learning library

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.825061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8cb83550-dbde-47fa-b180-67e7896a11a5 · outbound

This paper cites Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in Neural Information Processing Systems, 35:16344–16359, 2022.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Flashattention: Fast and memory-efficient exact attention with io-awareness.Advances in Neural Information Processing Systems, 35:16344–16359, 2022

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.555725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a53d581-2266-4163-bd2a-71ab0b666581 · outbound

This paper cites Fusedmm: A unified sddmm- spmm kernel for graph embedding and graph neural networks.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Fusedmm: A unified sddmm- spmm kernel for graph embedding and graph neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.805243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5854c556-9ab3-4bec-b861-8a284a84e0e8 · outbound

This paper cites Graphiler: Optimizing graph neural networks with message passing data flow graph.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Graphiler: Optimizing graph neural networks with message passing data flow graph

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.792306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 886782a8-186e-4365-94d4-cfd5846c88e4 · outbound

This paper cites Featgraph: A flexible and efficient backend for graph neural network systems.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Featgraph: A flexible and efficient backend for graph neural network systems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.780688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation db46f5bc-c77d-4e93-ba3e-ee11f1763e79 · outbound

This paper cites Sparsetir: Composable abstractions for sparse compilation in deep learning.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Sparsetir: Composable abstractions for sparse compilation in deep learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.769328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 403deb5a-32be-4b8b-867f-c67643769c19 · outbound

This paper cites Exploiting online locality and reduction parallelism for sampled dense matrix multiplication on gpus.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Exploiting online locality and reduction parallelism for sampled dense matrix multiplication on gpus

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.757292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0bb250aa-fea5-4394-ae35-ce7c4b8afe4a · outbound

This paper cites Rapids cugraph, 2024.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Rapids cugraph, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.744306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d64824cd-d8b5-4038-8ad2-40db77298c95 · outbound

This paper cites Benchmarking graph neural networks.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Benchmarking graph neural networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T13:37:37.583717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e68939d-f988-4b5b-bb7d-dddb891f2ba0 · outbound

This paper cites Long range graph benchmark.

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs Long range graph benchmark

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:37:37.723489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

Observation 284776bb-06a3-452f-b213-9c0037792e6d · inbound

On Efficient Scaling of GNNs via IO-Aware Layers Implementations cites this paper.

On Efficient Scaling of GNNs via IO-Aware Layers Implementations DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:16:01.140293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.