Paper Citation Record · LEDGER

Morphling: Fast, Fused, and Flexible GNN Training at Scale

As of 11 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2512.01678.

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

pith.paper-citation-record.v1
2512.01678 v5

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T19:15:08.623648Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

35 of 35 outbound references displayed

verified exact2
verified fuzzy0
unresolved31
parse uncertain0
malformed identifier2
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9be70f86-7142-445f-80fa-7d6fc510cbf7 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Fast Graph Representation Learning with PyTorch Geometric

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:04.756885Z

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Observation ebc6ec58-9b4f-44e8-a6d7-75135c5e78a4 · outbound

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

Morphling: Fast, Fused, and Flexible GNN Training at Scale Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:04.776816Z

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Observation d5bbfb3b-b093-4012-8f5f-14c9f264d846 · outbound

This paper cites GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:04.848461Z

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Observation 1a0afb60-979e-4dbb-a31c-2d449f272c62 · outbound

This paper cites Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:04.964753Z

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Observation ed016cd2-ea1f-4be0-96f6-569b448ec7f3 · outbound

This paper cites AliGraph: A Comprehensive Graph Neural Network Platform,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale AliGraph: A Comprehensive Graph Neural Network Platform,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:05.055221Z

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Observation 5f53ca70-0d9c-4714-b5ab-84056eda8718 · outbound

This paper cites Euler: A System for Large-Scale Graph Analytics,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Euler: A System for Large-Scale Graph Analytics,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:05.149409Z

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Observation 47976b9d-bf38-4202-85ef-df64d2bc036c · outbound

This paper cites DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs.

Morphling: Fast, Fused, and Flexible GNN Training at Scale DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:05.266045Z

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Observation fc40afb5-8dd4-4e60-933e-eab213cf730a · outbound

This paper cites GraphIt: A High-Performance DSL for Graph An- alytics,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale GraphIt: A High-Performance DSL for Graph An- alytics,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:05.363291Z

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Observation 1ff0c322-51e4-4d68-8360-bd408290d90c · outbound

This paper cites Ligra: A Lightweight Graph Pro- cessing Framework for Shared Memory,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Ligra: A Lightweight Graph Pro- cessing Framework for Shared Memory,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:05.494919Z

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Observation 6b8640c9-7594-4805-a522-df0eb0b72299 · outbound

This paper cites The Galois System: Optimistic Parallelization of Irregular Programs,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale The Galois System: Optimistic Parallelization of Irregular Programs,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:05.574955Z

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Observation e1a36d18-2b20-4db7-a201-a5f11a26a288 · outbound

This paper cites Gemini: A Computation- Centric Distributed Graph Processing System,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Gemini: A Computation- Centric Distributed Graph Processing System,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:05.747664Z

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Observation 167a321e-9984-45fd-a86c-7402d2ef3434 · outbound

This paper cites StarPlat: A Versatile DSL for Graph Analytics,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale StarPlat: A Versatile DSL for Graph Analytics,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:05.879401Z

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Observation 7bf853c4-b2d5-4813-aa09-697cbcfe1be9 · outbound

This paper cites TensorFlow: A System for Large- Scale Machine Learning,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale TensorFlow: A System for Large- Scale Machine Learning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:05.963631Z

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Observation 89a853b4-7f26-436e-861b-d28bed45f949 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Morphling: Fast, Fused, and Flexible GNN Training at Scale PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:06.116185Z

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Observation 271a9ae3-d925-4615-9d26-36bfb69eaa86 · outbound

This paper cites ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale ScaleFreeCTR: MixCache-based Distributed Training System for CTR Models with Huge Embedding Table,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:06.244510Z

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Observation f37edecc-0583-4b9c-9a3b-1041a1598c25 · outbound

This paper cites Seastar: Vertex-Centric Programming for Graph Neural Networks,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Seastar: Vertex-Centric Programming for Graph Neural Networks,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:06.339434Z

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Observation 42d8f8ef-bc81-4a76-9d8e-8453deef04f3 · outbound

This paper cites FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:06.406482Z

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Observation 054892d5-d0c8-4b59-9e05-2b9294b4b2ec · outbound

This paper cites Halide: A Language and Compiler for Optimizing Parallelism, Locality, and Recomputation in Image Processing Pipelines,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Halide: A Language and Compiler for Optimizing Parallelism, Locality, and Recomputation in Image Processing Pipelines,

Reference 18

Resolution: verified exact; doi, observed 2026-08-03T19:18:33.580305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c0dc1461-03b6-4101-9e5e-bcd0b7231d15 · outbound

This paper cites TVM: An Automated End-to-End Optimizing Compiler for Deep Learning,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale TVM: An Automated End-to-End Optimizing Compiler for Deep Learning,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:06.708483Z

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Observation 5e6caedb-9a9c-41cf-b9b7-136b5387e3a0 · outbound

This paper cites The Tensor Algebra Compiler,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale The Tensor Algebra Compiler,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:06.791815Z

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Observation cd62053f-24eb-4bdc-a26d-52fd3603ec31 · outbound

This paper cites Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code,

Reference 21

Resolution: malformed identifier; no resolver link, observed 2026-08-03T19:15:06.927847Z

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Observation b89c95cb-f981-493f-b2cd-e3228895abc3 · outbound

This paper cites Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:07.037519Z

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Observation ede1972b-3ffe-479a-98a1-e76f9556b373 · outbound

This paper cites Kokkos: Enabling manycore performance portability through polymorphic memory access patterns,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Kokkos: Enabling manycore performance portability through polymorphic memory access patterns,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:07.157913Z

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Observation c75f21bf-6f6b-462c-9257-4f1365d3df55 · outbound

This paper cites RAJA: Portable Performance for Large-Scale Scientific Applications,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale RAJA: Portable Performance for Large-Scale Scientific Applications,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:07.315209Z

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Observation a92d92ee-379b-4222-8dfe-fd88413aaebe · outbound

This paper cites SYCL: Single-source C++ accelerator programming,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale SYCL: Single-source C++ accelerator programming,

Reference 25

Resolution: verified exact; doi, observed 2026-08-03T19:18:33.327837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a1b7173b-8a08-429a-b9da-53ecc52f1a08 · outbound

This paper cites Intel® oneAPI Programming Guide,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Intel® oneAPI Programming Guide,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:07.555231Z

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Observation 69529883-cbbe-4a03-a90a-0ba1f0c33dc6 · outbound

This paper cites OpenCL: A parallel programming standard for heterogeneous computing systems,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale OpenCL: A parallel programming standard for heterogeneous computing systems,

Reference 27

Resolution: malformed identifier; no resolver link, observed 2026-08-03T19:15:07.717366Z

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Observation e9021aee-9c3c-4adf-bf88-081f6ccf4b9a · outbound

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

Morphling: Fast, Fused, and Flexible GNN Training at Scale Semi-supervised classification with graph convolutional networks,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:07.825632Z

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Observation d3410727-1088-4e2f-97d4-51a4995a5afc · outbound

This paper cites Inductive Representation Learning on Large Graphs.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Inductive Representation Learning on Large Graphs

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:08.089933Z

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Observation f0e74d50-64a9-4d50-8adb-beb593a01706 · outbound

This paper cites How Powerful are Graph Neural Networks?.

Morphling: Fast, Fused, and Flexible GNN Training at Scale How Powerful are Graph Neural Networks?

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:08.239038Z

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Observation b73d7918-9387-4558-8941-c075cbf85860 · outbound

This paper cites Graph Attention Networks.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Graph Attention Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:08.355665Z

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Observation b177dddc-1821-4d5f-a69c-4248a8e5a559 · outbound

This paper cites A fast and high quality multilevel scheme for partitioning irregular graphs,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale A fast and high quality multilevel scheme for partitioning irregular graphs,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:08.469104Z

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Observation 8eaa407a-2533-4cc4-8be3-9e27a1660299 · outbound

This paper cites Sparse tensor support,.

Morphling: Fast, Fused, and Flexible GNN Training at Scale Sparse tensor support,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:08.623648Z

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Observation 2686e6af-b878-409c-94be-7ea4ec448bf2 · outbound

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

Morphling: Fast, Fused, and Flexible GNN Training at Scale Semi-Supervised Classification with Graph Convolutional Networks

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:07.937714Z

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Observation 7102eb6d-3836-4de8-af27-5de27a869946 · outbound

This paper cites FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems.

Morphling: Fast, Fused, and Flexible GNN Training at Scale FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-03T19:15:06.529760Z

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Pith citing papers

No inbound Pith citation observations are available.