ReInc: Scaling Training of Dynamic Graph Neural Networks | Paper Citation Record

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This paper cites https://dot.ca.gov/programs/ traffic-operations/mpr/pems-source.

ReInc: Scaling Training of Dynamic Graph Neural Networks https://dot.ca.gov/programs/ traffic-operations/mpr/pems-source

Reference 1

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This paper cites Tesseract: distributed, general graph pattern mining on evolving graphs.

ReInc: Scaling Training of Dynamic Graph Neural Networks Tesseract: distributed, general graph pattern mining on evolving graphs

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This paper cites Structural temporal graph neural networks for anomaly detection in dynamic graphs.

ReInc: Scaling Training of Dynamic Graph Neural Networks Structural temporal graph neural networks for anomaly detection in dynamic graphs

Reference 3

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This paper cites Chakaravarthy, Shivmaran S.

ReInc: Scaling Training of Dynamic Graph Neural Networks Chakaravarthy, Shivmaran S

Reference 4

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This paper cites Gc-lstm: Graph convolution embedded lstm for dynamic link pre- diction, 2021.

ReInc: Scaling Training of Dynamic Graph Neural Networks Gc-lstm: Graph convolution embedded lstm for dynamic link pre- diction, 2021

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This paper cites Powerlyra: Differentiated graph computation and parti- tioning on skewed graphs.

ReInc: Scaling Training of Dynamic Graph Neural Networks Powerlyra: Differentiated graph computation and parti- tioning on skewed graphs

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This paper cites Improving WWW proxies per- formance with greedy-dual-size-frequency caching pol- icy.

ReInc: Scaling Training of Dynamic Graph Neural Networks Improving WWW proxies per- formance with greedy-dual-size-frequency caching pol- icy

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This paper cites One trillion edges: Graph processing at facebook-scale.

ReInc: Scaling Training of Dynamic Graph Neural Networks One trillion edges: Graph processing at facebook-scale

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This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

ReInc: Scaling Training of Dynamic Graph Neural Networks Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 9

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This paper cites Convolutional neural networks on graphs with fast localized spectral filtering.

ReInc: Scaling Training of Dynamic Graph Neural Networks Convolutional neural networks on graphs with fast localized spectral filtering

Reference 10

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ReInc: Scaling Training of Dynamic Graph Neural Networks Unresolved cited work

Reference 11

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This paper cites P3: Dis- tributed deep graph learning at scale.

ReInc: Scaling Training of Dynamic Graph Neural Networks P3: Dis- tributed deep graph learning at scale

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This paper cites Automating incremental graph processing with flexible memoization.

ReInc: Scaling Training of Dynamic Graph Neural Networks Automating incremental graph processing with flexible memoization

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This paper cites Powergraph: Distributed graph-parallel computation on natural graphs.

ReInc: Scaling Training of Dynamic Graph Neural Networks Powergraph: Distributed graph-parallel computation on natural graphs

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This paper cites Dynagraph: Dynamic graph neural networks at scale.

ReInc: Scaling Training of Dynamic Graph Neural Networks Dynagraph: Dynamic graph neural networks at scale

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This paper cites Attention based spatial-temporal graph convolutional networks for traffic flow forecast- ing.

ReInc: Scaling Training of Dynamic Graph Neural Networks Attention based spatial-temporal graph convolutional networks for traffic flow forecast- ing

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This paper cites In- ductive representation learning on large graphs.

ReInc: Scaling Training of Dynamic Graph Neural Networks In- ductive representation learning on large graphs

Reference 17

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This paper cites Representation Learning on Graphs: Methods and Applications.

ReInc: Scaling Training of Dynamic Graph Neural Networks Representation Learning on Graphs: Methods and Applications

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This paper cites Long Short- Term Memory.

ReInc: Scaling Training of Dynamic Graph Neural Networks Long Short- Term Memory

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

ReInc: Scaling Training of Dynamic Graph Neural Networks Open Graph Benchmark: Datasets for Machine Learning on Graphs

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This paper cites T-gcn: A sampling based streaming graph neural network system with hybrid ar- chitecture.

ReInc: Scaling Training of Dynamic Graph Neural Networks T-gcn: A sampling based streaming graph neural network system with hybrid ar- chitecture

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This paper cites Lsgcn: Long short-term traffic prediction with graph convolutional networks.

ReInc: Scaling Training of Dynamic Graph Neural Networks Lsgcn: Long short-term traffic prediction with graph convolutional networks

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This paper cites ASAP: Fast, approximate graph pattern mining at scale.

ReInc: Scaling Training of Dynamic Graph Neural Networks ASAP: Fast, approximate graph pattern mining at scale

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This paper cites Gonzalez, and Ion Stoica.

ReInc: Scaling Training of Dynamic Graph Neural Networks Gonzalez, and Ion Stoica

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Reference 26

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ReInc: Scaling Training of Dynamic Graph Neural Networks Improving the accuracy, scalability, and performance of graph neural networks with roc

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ReInc: Scaling Training of Dynamic Graph Neural Networks Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networks

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This paper cites A fast and high qual- ity multilevel scheme for partitioning irregular graphs.

ReInc: Scaling Training of Dynamic Graph Neural Networks A fast and high qual- ity multilevel scheme for partitioning irregular graphs

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This paper cites A fast and high qual- ity multilevel scheme for partitioning irregular graphs.

ReInc: Scaling Training of Dynamic Graph Neural Networks A fast and high qual- ity multilevel scheme for partitioning irregular graphs

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ReInc: Scaling Training of Dynamic Graph Neural Networks Representation learning for dynamic graphs: A survey, 2020

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ReInc: Scaling Training of Dynamic Graph Neural Networks Zipg: A memory-efficient graph store for interactive queries

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ReInc: Scaling Training of Dynamic Graph Neural Networks Unresolved cited work

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ReInc: Scaling Training of Dynamic Graph Neural Networks GRIP: A Graph Neural Network Accelerator Architecture

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ReInc: Scaling Training of Dynamic Graph Neural Networks Kipf and Max Welling

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ReInc: Scaling Training of Dynamic Graph Neural Networks Howie Huang

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ReInc: Scaling Training of Dynamic Graph Neural Networks Professor forcing: A new algorithm for training recurrent networks

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This paper cites Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution.

ReInc: Scaling Training of Dynamic Graph Neural Networks Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution

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ReInc: Scaling Training of Dynamic Graph Neural Networks Cache-based gnn system for dynamic graphs

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ReInc: Scaling Training of Dynamic Graph Neural Networks Dif- fusion convolutional recurrent neural network: Data- driven traffic forecasting

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ReInc: Scaling Training of Dynamic Graph Neural Networks Pagraph: Scaling gnn training on large graphs via computation-aware caching

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ReInc: Scaling Training of Dynamic Graph Neural Networks Rensi, Wen Torng, and Russ B

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ReInc: Scaling Training of Dynamic Graph Neural Networks Kilmer, and Haim Avron

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ReInc: Scaling Training of Dynamic Graph Neural Networks Dynamic graph convolutional networks

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ReInc: Scaling Training of Dynamic Graph Neural Networks Graphbolt: Dependency-driven synchronous processing of stream- ing graphs

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ReInc: Scaling Training of Dynamic Graph Neural Networks Marius: Learning massive graph embeddings on a single ma- chine

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This paper cites Pinner- sage: Multi-modal user embedding framework for rec- ommendations at pinterest.

ReInc: Scaling Training of Dynamic Graph Neural Networks Pinner- sage: Multi-modal user embedding framework for rec- ommendations at pinterest

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ReInc: Scaling Training of Dynamic Graph Neural Networks Transfer graph neural networks for pandemic forecasting

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ReInc: Scaling Training of Dynamic Graph Neural Networks Schardl, and Charles E

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This paper cites Estimating node impor- tance in knowledge graphs using graph neural networks.

ReInc: Scaling Training of Dynamic Graph Neural Networks Estimating node impor- tance in knowledge graphs using graph neural networks

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ReInc: Scaling Training of Dynamic Graph Neural Networks Community dis- covery in dynamic networks: A survey

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This paper cites PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neu- ral Machine Learning Models.

ReInc: Scaling Training of Dynamic Graph Neural Networks PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neu- ral Machine Learning Models

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ReInc: Scaling Training of Dynamic Graph Neural Networks Dysat: Deep neural representation learn- ing on dynamic graphs via self-attention networks

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ReInc: Scaling Training of Dynamic Graph Neural Networks Structured sequence modeling with graph convolutional recurrent networks, 2016

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ReInc: Scaling Training of Dynamic Graph Neural Networks Accelerating dynamic graph analytics on gpus

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Observation a2eb9c22-da43-4056-8eda-26f8782cdea3 · outbound

This paper cites Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey.

ReInc: Scaling Training of Dynamic Graph Neural Networks Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey

Reference 57

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Observation 9e1821ef-6a09-49ae-84e5-70a9124be85a · outbound

This paper cites Session-based social recommendation via dynamic graph attention networks.

ReInc: Scaling Training of Dynamic Graph Neural Networks Session-based social recommendation via dynamic graph attention networks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.366189Z

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

Observation a9b51bf9-2fb6-4b76-bc3d-4755e61f0809 · outbound

This paper cites Session-based social recommendation via dynamic graph attention networks.

ReInc: Scaling Training of Dynamic Graph Neural Networks Session-based social recommendation via dynamic graph attention networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.355473Z

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 f898c9a1-a394-4296-9159-8fbb37b3dd25 · outbound

This paper cites Stokes, Kevin Yang, Kyle Swanson, Wen- gong Jin, Andres Cubillos-Ruiz, Nina M.

ReInc: Scaling Training of Dynamic Graph Neural Networks Stokes, Kevin Yang, Kyle Swanson, Wen- gong Jin, Andres Cubillos-Ruiz, Nina M

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.345339Z

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

Observation 2372a62b-bdbf-48c1-a1f9-79ae2f9b3b18 · outbound

This paper cites Dorylus: Affordable, scalable, and accurate GNN train- ing with distributed CPU servers and serverless threads.

ReInc: Scaling Training of Dynamic Graph Neural Networks Dorylus: Affordable, scalable, and accurate GNN train- ing with distributed CPU servers and serverless threads

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.334238Z

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 ddc4125a-1d97-4616-9177-f3a7e15727f7 · outbound

This paper cites Graph attention networks.

ReInc: Scaling Training of Dynamic Graph Neural Networks Graph attention networks

Reference 62

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Observation 260f9dbb-49f9-47ab-97d4-aace981ab9e9 · outbound

This paper cites Pipad: Pipelined and parallel dynamic gnn training on gpus.

ReInc: Scaling Training of Dynamic Graph Neural Networks Pipad: Pipelined and parallel dynamic gnn training on gpus

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.318025Z

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 9117482f-fb50-45c0-8801-a60ab7f1c825 · outbound

This paper cites Flex- graph: A flexible and efficient distributed framework for gnn training.

ReInc: Scaling Training of Dynamic Graph Neural Networks Flex- graph: A flexible and efficient distributed framework for gnn training

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.307462Z

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 df00e24b-dcff-4165-b764-f57dff31f921 · outbound

This paper cites Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.

ReInc: Scaling Training of Dynamic Graph Neural Networks Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-10T14:28:16.957484Z

Source-reported events for the cited work

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Observation 54643522-5a49-458a-b127-230d8c464d6d · outbound

This paper cites an unresolved cited work.

ReInc: Scaling Training of Dynamic Graph Neural Networks Unresolved cited work

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T14:28:16.950096Z

Source-reported events for the cited work

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Observation 9d2a1916-81ba-4088-b8fb-392520c209c9 · outbound

This paper cites Williams and David Zipser.

ReInc: Scaling Training of Dynamic Graph Neural Networks Williams and David Zipser

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.285775Z

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 7b370c4e-84e7-47ed-9cd3-f26e967dc0a1 · outbound

This paper cites Fast and Accu- rate Optimizer for Query Processing over Knowledge Graphs, page 503–517.

ReInc: Scaling Training of Dynamic Graph Neural Networks Fast and Accu- rate Optimizer for Query Processing over Knowledge Graphs, page 503–517

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.274473Z

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 1cf98f37-937a-4fa2-b2c8-c5a1ac9f8174 · outbound

This paper cites GNNAdvisor: An adaptive and efficient runtime system for GNN ac- celeration on GPUs.

ReInc: Scaling Training of Dynamic Graph Neural Networks GNNAdvisor: An adaptive and efficient runtime system for GNN ac- celeration on GPUs

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.296411Z

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 ec882770-0584-4a61-82a7-653b5bd2d5b4 · outbound

This paper cites Gnnlab: a factored system for sample-based gnn training over gpus.

ReInc: Scaling Training of Dynamic Graph Neural Networks Gnnlab: a factored system for sample-based gnn training over gpus

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.263239Z

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 2151fcd2-c2e6-497c-b5e3-01ee5233eb6e · outbound

This paper cites Hamilton, and Jure Leskovec.

ReInc: Scaling Training of Dynamic Graph Neural Networks Hamilton, and Jure Leskovec

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.252380Z

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 41c6f802-fa54-42b4-bc68-abf3b466d163 · outbound

This paper cites How Powerful are Graph Neural Networks?.

ReInc: Scaling Training of Dynamic Graph Neural Networks How Powerful are Graph Neural Networks?

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-10T14:28:16.972732Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6368cfd1-6b35-4564-a77c-7ff34974d856 · outbound

This paper cites Agl: A scalable system for industrial-purpose graph machine learning.

ReInc: Scaling Training of Dynamic Graph Neural Networks Agl: A scalable system for industrial-purpose graph machine learning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.242233Z

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 105a1301-4a9d-4807-b55d-dbc1e30a2e15 · outbound

This paper cites Under- standing GNN computational graph: A coordinated com- putation, io, and memory perspective.

ReInc: Scaling Training of Dynamic Graph Neural Networks Under- standing GNN computational graph: A coordinated com- putation, io, and memory perspective

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.231700Z

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 daeb0853-9549-497f-a61c-ae8a30d90607 · outbound

This paper cites Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

ReInc: Scaling Training of Dynamic Graph Neural Networks Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-10T14:28:16.985022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5c123c2-7f32-4714-a02c-d51112d7de38 · outbound

This paper cites Dynamic graph neural networks for sequential recommendation.

ReInc: Scaling Training of Dynamic Graph Neural Networks Dynamic graph neural networks for sequential recommendation

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.220566Z

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 a866d3a7-350c-4534-b731-93ccda23fceb · outbound

This paper cites Exploring the hidden dimension in graph processing.

ReInc: Scaling Training of Dynamic Graph Neural Networks Exploring the hidden dimension in graph processing

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.209880Z

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 317ce46e-a84c-4f8e-b1a8-2641ff57a484 · outbound

This paper cites GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs.

ReInc: Scaling Training of Dynamic Graph Neural Networks GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-10T14:28:16.995842Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bc6a20d-7c70-4d26-acda-cc6c5ebac665 · outbound

This paper cites T-gcn: A tempo- ral graph convolutional network for traffic prediction.

ReInc: Scaling Training of Dynamic Graph Neural Networks T-gcn: A tempo- ral graph convolutional network for traffic prediction

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.188233Z

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 fbf83907-a8c4-41a5-91be-c8a489dcdc7b · outbound

This paper cites Tgl: A general framework for temporal gnn training on billion-scale graphs.

ReInc: Scaling Training of Dynamic Graph Neural Networks Tgl: A general framework for temporal gnn training on billion-scale graphs

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.178243Z

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 c5495be4-1773-4ee8-8339-3b1d1e7f12af · outbound

This paper cites Egraph: Efficient concurrent gpu-based dynamic graph processing.

ReInc: Scaling Training of Dynamic Graph Neural Networks Egraph: Efficient concurrent gpu-based dynamic graph processing

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:28:17.198934Z

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 7caf4bcc-72a6-4e03-861d-246909e8e7eb · outbound

This paper cites an unresolved cited work.

ReInc: Scaling Training of Dynamic Graph Neural Networks Unresolved cited work

Reference 549

Resolution: unresolved; no resolver link, observed 2026-08-10T14:28:16.888279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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