Paper Citation Record · LEDGER

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2608.07733.

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

pith.paper-citation-record.v1
2608.07733 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T00:24:54.011353Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

38 of 38 outbound references displayed

verified exact1
verified fuzzy20
unresolved16
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malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fa69458b-a323-424a-8d4f-92745acfd8e5 · outbound

This paper cites Semi-supervised classification with graph convolu- tional networks,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Semi-supervised classification with graph convolu- tional networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.171431Z

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Observation 3989b1d5-7c64-468b-ba4d-179f50f6e656 · outbound

This paper cites Graph attention networks,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Graph attention networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.156158Z

Source-reported events for the cited work

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Observation 556e63bd-3fa9-4328-a3d8-c02dc5e4a86d · outbound

This paper cites Inductive representation learning on large graphs,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Inductive representation learning on large graphs,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.143321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 62e8d386-af5b-4b64-ae0b-2aea32cf5c4c · outbound

This paper cites How powerful are graph neural networks?.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs How powerful are graph neural networks?

Reference 4

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Source-reported events for the cited work

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Observation c5ec75bd-f688-41d5-8383-0a332bb55718 · outbound

This paper cites OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T00:24:53.826395Z

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Observation 8df7e1ad-19ae-4e83-aa94-80a6383dc36c · outbound

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LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Unresolved cited work

Reference 6

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Source-reported events for the cited work

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Observation 892c6adc-e417-40ed-a93c-1573c6893a44 · outbound

This paper cites Fast graph representation learning with PyTorch Geo- metric,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Fast graph representation learning with PyTorch Geo- metric,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.106911Z

Source-reported events for the cited work

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Observation dce15e97-4a04-476b-a40e-c9ee40c39d65 · outbound

This paper cites Deep graph library: A graph- centric, highly-performant package for graph neural networks,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Deep graph library: A graph- centric, highly-performant package for graph neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.091349Z

Source-reported events for the cited work

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Observation f8335691-9dc3-45ac-853e-24b4e95a1e9c · outbound

This paper cites Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Cluster-gcn: An efficient algorithm for training deep and large graph convolutional networks,

Reference 9

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Source-reported events for the cited work

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Observation 05e236fe-e522-4cb2-92eb-6c911a1450b8 · outbound

This paper cites Smartsage: Training large-scale graph neural networks using in-storage processing architectures,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Smartsage: Training large-scale graph neural networks using in-storage processing architectures,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.077299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e501fdc0-2e80-483f-b4d5-5065e60f52e2 · outbound

This paper cites Open Graph Benchmark: Datasets for Machine Learning on Graphs.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Open Graph Benchmark: Datasets for Machine Learning on Graphs

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T00:24:53.863601Z

Source-reported events for the cited work

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Observation 3d2a8cec-89ce-4cfc-84a3-a8cbade05d53 · outbound

This paper cites Improving the speed of neural networks on cpus,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Improving the speed of neural networks on cpus,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.050791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 50cc591e-8fd8-47d6-8691-11d1aab7543f · outbound

This paper cites Binarized neural networks,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Binarized neural networks,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.036984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23a8bced-b74f-44d0-9b6f-01b6de9081b5 · outbound

This paper cites Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.020519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0839df26-e776-44f2-9c58-5f21fa34018c · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 15

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Source-reported events for the cited work

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Observation 9356a336-1274-4015-97f5-f61a248c8c29 · outbound

This paper cites Tango: re-thinking quantization for graph neural network training on gpus,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Tango: re-thinking quantization for graph neural network training on gpus,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T00:24:53.891442Z

Source-reported events for the cited work

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Observation 6a30d57b-38ad-45de-9a56-08ef6362cbca · outbound

This paper cites Approximation- and quantization- aware training for graph neural networks,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Approximation- and quantization- aware training for graph neural networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:55.003594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c2370137-500e-4843-87b8-d05ab9d9d05f · outbound

This paper cites Low-bit quantization for deep graph neural networks with smoothness-aware message propagation,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Low-bit quantization for deep graph neural networks with smoothness-aware message propagation,

Reference 18

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Source-reported events for the cited work

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Observation 8d53fdf9-fa2e-43ca-8915-a1801fb440d6 · outbound

This paper cites NVIDIA BLUEFIELD-2 DPU Data Center Infrastructure on a Chip,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs NVIDIA BLUEFIELD-2 DPU Data Center Infrastructure on a Chip,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:54.987247Z

Source-reported events for the cited work

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Observation c659db6f-daca-4a1c-bbb2-164531b5737a · outbound

This paper cites Nvidia doca,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Nvidia doca,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:54.974677Z

Source-reported events for the cited work

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Observation cb04c939-dd72-447f-96d8-6411c976ac2c · outbound

This paper cites Stochastic Training of Graph Convolutional Networks with Variance Reduction.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Stochastic Training of Graph Convolutional Networks with Variance Reduction

Reference 21

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Source-reported events for the cited work

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Observation c3b73a80-605d-45b5-8bad-a50908dd14cf · outbound

This paper cites Graph convolutional neural networks for web-scale recommender systems,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Graph convolutional neural networks for web-scale recommender systems,

Reference 22

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Source-reported events for the cited work

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Observation 84c9952b-c217-4aa2-bbbd-dba23b200727 · outbound

This paper cites FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

Reference 23

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Source-reported events for the cited work

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Observation 9a79443f-6170-498b-8e91-bd16f069d40d · outbound

This paper cites Adaptive sampling towards fast graph representation learning,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Adaptive sampling towards fast graph representation learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:54.960692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9b295b69-27cd-426b-86e3-80829367c621 · outbound

This paper cites Layer-dependent importance sampling for training deep and large graph convolutional networks,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Layer-dependent importance sampling for training deep and large graph convolutional networks,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3a539935-9515-4825-9f19-69cb56d735ba · outbound

This paper cites GraphSAINT: Graph Sampling Based Inductive Learning Method.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs GraphSAINT: Graph Sampling Based Inductive Learning Method

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T00:24:53.939265Z

Source-reported events for the cited work

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Observation 4d130afa-a95d-47c7-8c34-ce599228777a · outbound

This paper cites Ripple walk training: A subgraph-based training framework for large and deep graph neural network,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Ripple walk training: A subgraph-based training framework for large and deep graph neural network,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:54.908914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 63ba4998-2154-4250-8b3e-7dfbfbfd3fd0 · outbound

This paper cites Fast random walk with restart and its applications,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Fast random walk with restart and its applications,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:54.895008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8ed73300-b240-4d52-80ad-bf80dd3b7a1b · outbound

This paper cites Simplifying graph convolutional networks,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Simplifying graph convolutional networks,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:54.881382Z

Source-reported events for the cited work

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Observation b26359df-50ff-4c8b-9c2b-3e32bee65edd · outbound

This paper cites Farview: Disaggregated memory with operator off-loading for database engines,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Farview: Disaggregated memory with operator off-loading for database engines,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:54.869128Z

Source-reported events for the cited work

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Observation e230325d-7d7c-4f8b-b03a-a8ea299e86cf · outbound

This paper cites DGCL: An efficient communication library for distributed GNN training,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs DGCL: An efficient communication library for distributed GNN training,

Reference 31

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Source-reported events for the cited work

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Observation b85561b9-101d-4de3-870d-6a8a380659b5 · outbound

This paper cites Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T00:24:53.980967Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d882eab4-e000-483b-be94-26f7a8d69ca1 · outbound

This paper cites GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T00:24:53.986049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b21d0f39-6b38-496f-8b38-3e534f8c7dbf · outbound

This paper cites Efficient neighbor- sampling-based gnn training on cpu-fpga heterogeneous platform,.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Efficient neighbor- sampling-based gnn training on cpu-fpga heterogeneous platform,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:54.857184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b9dab79f-2ac5-4d5c-8ff4-38558dd1e4b9 · outbound

This paper cites Hardware Acceleration of Sampling Algorithms in Sample and Aggregate Graph Neural Networks.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Hardware Acceleration of Sampling Algorithms in Sample and Aggregate Graph Neural Networks

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e7f151db-9bc8-4136-885d-642ae91a96d4 · outbound

This paper cites HitGNN: High-throughput GNN Training Framework on CPU+Multi-FPGA Heterogeneous Platform.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs HitGNN: High-throughput GNN Training Framework on CPU+Multi-FPGA Heterogeneous Platform

Reference 36

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Source-reported events for the cited work

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Observation d58db694-8022-40ad-a2b9-495b9256e83c · outbound

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

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-11T00:24:53.882035Z

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Unavailable: canonical work link unavailable.

Observation f6a7a33c-2c9a-4494-8a09-7b26a699829c · outbound

This paper cites Available: https://api.semanticscholar.org/CorpusID:195886159.

LGNNIC: Acceleration of Large-Scale GNN Training using SmartNICs Available: https://api.semanticscholar.org/CorpusID:195886159

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:24:54.925590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.