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Paper Citation Record · LEDGER

Federated Contrastive Learning of Graph-Level Representations

As of 15 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2411.12098.

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

pith.paper-citation-record.v1
2411.12098 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

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measured 64 of 64 standing notices

One-hop event checks from named stored sources.

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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

64 of 64 outbound references displayed

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External citation measurements

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Outbound references

Observation 03d6f700-896f-48d6-a56f-1cafec0addd3 · outbound

This paper cites Survey of graph database models,.

Federated Contrastive Learning of Graph-Level Representations Survey of graph database models,

Reference 1

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Observation 2b4111b9-0108-4f12-b4d4-f36bb0cbd4cd · outbound

This paper cites Cgnn: Traffic classification with graph neural network,.

Federated Contrastive Learning of Graph-Level Representations Cgnn: Traffic classification with graph neural network,

Reference 2

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Observation 559bdfab-b79d-4e71-94cc-41d7eb0a73f3 · outbound

This paper cites Traffic classification based on graph convolutional network,.

Federated Contrastive Learning of Graph-Level Representations Traffic classification based on graph convolutional network,

Reference 3

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This paper cites Learned protein embeddings for machine learning,.

Federated Contrastive Learning of Graph-Level Representations Learned protein embeddings for machine learning,

Reference 4

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Observation 5f8cdeac-c9a5-450a-9ba7-b0fac5a6e5d8 · outbound

This paper cites Structure-based protein function prediction using graph convolutional networks,.

Federated Contrastive Learning of Graph-Level Representations Structure-based protein function prediction using graph convolutional networks,

Reference 5

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Observation f614c2b7-4803-4d36-a23c-36c160827eac · outbound

This paper cites Comparison of descriptor spaces for chemical compound retrieval and classification,.

Federated Contrastive Learning of Graph-Level Representations Comparison of descriptor spaces for chemical compound retrieval and classification,

Reference 6

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Observation d2abc580-49d2-4866-8cd0-5c584d54e0e8 · outbound

This paper cites Moleculenet: A benchmark for molecular machine learning,.

Federated Contrastive Learning of Graph-Level Representations Moleculenet: A benchmark for molecular machine learning,

Reference 7

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Observation 07030edc-e33e-4d1f-b0db-9e8eec89199d · outbound

This paper cites Utilizing graph machine learning within drug discovery and development,.

Federated Contrastive Learning of Graph-Level Representations Utilizing graph machine learning within drug discovery and development,

Reference 8

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Observation 9db9caf3-3dd1-4d70-acec-46fc89954ea3 · outbound

This paper cites Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor- based and graph-based models.

Federated Contrastive Learning of Graph-Level Representations Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor- based and graph-based models

Reference 9

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Observation 12bc2458-093e-472d-bfe2-8cd6efc455fb · outbound

This paper cites Tactilegcn: A graph convolutional network for predicting grasp stability with tactile sensors,.

Federated Contrastive Learning of Graph-Level Representations Tactilegcn: A graph convolutional network for predicting grasp stability with tactile sensors,

Reference 10

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Observation 9aefe722-30d0-4b92-92e3-97e6efd5e779 · outbound

This paper cites Towards Federated Graph Learning for Collaborative Financial Crimes Detection.

Federated Contrastive Learning of Graph-Level Representations Towards Federated Graph Learning for Collaborative Financial Crimes Detection

Reference 11

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Observation a8a443fc-e474-427f-8aa5-1168a98bf48f · outbound

This paper cites Fedgnn: Federated graph neural network for privacy-preserving recommendation,.

Federated Contrastive Learning of Graph-Level Representations Fedgnn: Federated graph neural network for privacy-preserving recommendation,

Reference 12

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Observation e0a1ed58-ce52-46de-82a6-64acde24cbec · outbound

This paper cites Subgraph federated learning with missing neighbor generation,.

Federated Contrastive Learning of Graph-Level Representations Subgraph federated learning with missing neighbor generation,

Reference 13

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Observation fd3048f6-2297-4bc6-b5f3-b6bf387e92b3 · outbound

This paper cites Federated myopic community detection with one-shot communication,.

Federated Contrastive Learning of Graph-Level Representations Federated myopic community detection with one-shot communication,

Reference 14

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This paper cites Communication-efficient learning of deep networks from decentralized data,.

Federated Contrastive Learning of Graph-Level Representations Communication-efficient learning of deep networks from decentralized data,

Reference 15

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Source-reported events for the cited work

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Observation 06144413-6fce-43ca-9c16-4b8e3fa9b1e8 · outbound

This paper cites Federated Graph Classification over Non-IID Graphs.

Federated Contrastive Learning of Graph-Level Representations Federated Graph Classification over Non-IID Graphs

Reference 16

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This paper cites Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches,.

Federated Contrastive Learning of Graph-Level Representations Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches,

Reference 17

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Observation c6e5c221-8457-401e-8a4b-c87666d323c9 · outbound

This paper cites Nf-gnn: network flow graph neural networks for malware detection and classification,.

Federated Contrastive Learning of Graph-Level Representations Nf-gnn: network flow graph neural networks for malware detection and classification,

Reference 18

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Observation 0c772ad0-e346-4829-ac85-6a283be267d3 · outbound

This paper cites Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization,.

Federated Contrastive Learning of Graph-Level Representations Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization,

Reference 19

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Observation f26aa66a-1c44-49e4-bd15-bd3e833f528f · outbound

This paper cites Density functional theory,.

Federated Contrastive Learning of Graph-Level Representations Density functional theory,

Reference 20

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Federated Contrastive Learning of Graph-Level Representations Semi-supervised classification with graph convolutional networks,

Reference 21

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This paper cites How powerful are graph neural networks?.

Federated Contrastive Learning of Graph-Level Representations How powerful are graph neural networks?

Reference 22

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This paper cites An end- to-end deep learning architecture for graph classification,.

Federated Contrastive Learning of Graph-Level Representations An end- to-end deep learning architecture for graph classification,

Reference 23

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Federated Contrastive Learning of Graph-Level Representations Hierarchical graph representation learning with differentiable pooling,

Reference 24

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Observation 1bec20f2-61e9-4b83-8670-548e5fd36c02 · outbound

This paper cites graph2vec: Learning Distributed Representations of Graphs.

Federated Contrastive Learning of Graph-Level Representations graph2vec: Learning Distributed Representations of Graphs

Reference 25

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This paper cites Strategies for Pre-training Graph Neural Networks.

Federated Contrastive Learning of Graph-Level Representations Strategies for Pre-training Graph Neural Networks

Reference 26

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Federated Contrastive Learning of Graph-Level Representations Biological network comparison using graphlet degree distri- bution,

Reference 27

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This paper cites Graph invariant kernels,.

Federated Contrastive Learning of Graph-Level Representations Graph invariant kernels,

Reference 28

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Observation 6c63ec47-cc80-4d45-9d3a-8212a04fefca · outbound

This paper cites Contrastive multi-view representa- tion learning on graphs,.

Federated Contrastive Learning of Graph-Level Representations Contrastive multi-view representa- tion learning on graphs,

Reference 29

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Federated Contrastive Learning of Graph-Level Representations A simple framework for contrastive learning of visual representations,

Reference 30

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Federated Contrastive Learning of Graph-Level Representations Learning word embeddings efficiently with noise-contrastive estimation,

Reference 31

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Federated Contrastive Learning of Graph-Level Representations Model-contrastive federated learning,

Reference 32

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Observation bff39497-237b-462f-bc08-6e464265b9b8 · outbound

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Federated Contrastive Learning of Graph-Level Representations FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning

Reference 33

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Observation 32c68ee5-7a00-4ee7-930c-91e53fb4b5c7 · outbound

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Federated Contrastive Learning of Graph-Level Representations Federated Unsupervised Representation Learning

Reference 34

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Observation 396e56e4-5984-4744-98fb-4978f54de76f · outbound

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Federated Contrastive Learning of Graph-Level Representations Ensemble distillation for robust model fusion in federated learning,

Reference 35

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Observation b5cc4dc5-3db8-4e96-a061-833be4cd940a · outbound

This paper cites Comparing kullback- leibler divergence and mean squared error loss in knowledge distilla- tion,.

Federated Contrastive Learning of Graph-Level Representations Comparing kullback- leibler divergence and mean squared error loss in knowledge distilla- tion,

Reference 36

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raw_fallback, observed 2026-08-12T17:59:31.714148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.877480Z digest=sha256:93f707519d42247321f90e86b09286ea6dc36122b239a0eb6496b6fae560c61e

Observation 08174ad5-5a94-4cfd-aac9-4c9e68b1c22e · outbound

This paper cites Fast subtree kernels on graphs,.

Federated Contrastive Learning of Graph-Level Representations Fast subtree kernels on graphs,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.696542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.882029Z digest=sha256:428154c0c92fe085b88d1d5bc418e734db4346dd41f7a0cc4f2fb2649b0f4f0f

Observation 08db4752-7e8e-48eb-9420-606fe1948517 · outbound

This paper cites Graph kernels,.

Federated Contrastive Learning of Graph-Level Representations Graph kernels,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.678357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.886309Z digest=sha256:2926683eecd06b1dd3ace621e4d1b64c98fe381b1c9a8a160635b14d80186b92

Observation 304c3d4c-e1f0-4c72-bfb5-348ce25d5343 · outbound

This paper cites Deep graph kernels,.

Federated Contrastive Learning of Graph-Level Representations Deep graph kernels,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.659324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.890729Z digest=sha256:c8201251e2232754c73c2521c1037f77497af11c4b2fd148e9cfc9829e63712e

Observation 0aa5241f-ce29-4cb4-a3a1-96978f18a479 · outbound

This paper cites Node, motif and subgraph: Leveraging network functional blocks through structural convolution,.

Federated Contrastive Learning of Graph-Level Representations Node, motif and subgraph: Leveraging network functional blocks through structural convolution,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.640770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.895467Z digest=sha256:9e5ea270e36f7ac4d7cc542581a666a2f2222a7a63e3bc84c9c6af463f2b452e

Observation cce95cce-1363-4066-93ba-2cc13a1ee22b · outbound

This paper cites Learning deep representations by mutual information estimation and maximization,.

Federated Contrastive Learning of Graph-Level Representations Learning deep representations by mutual information estimation and maximization,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.622711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.900132Z digest=sha256:9ec6ad514e3ed1dc1c3abf1bb6706b760140f8c8f0fa7569bacef24eade076cb

Observation 7b61bd9a-4e94-43f9-9359-31dbf74646fe · outbound

This paper cites Contrastive multiview coding,.

Federated Contrastive Learning of Graph-Level Representations Contrastive multiview coding,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.603103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.904618Z digest=sha256:4e234f8cc5577eee31127f99f781b924fcdb975605a63185d67abf9059a2226a

Observation bf1ca972-d21a-4bfe-acaa-cf126224495c · outbound

This paper cites Improved deep metric learning with multi-class n-pair loss objective,.

Federated Contrastive Learning of Graph-Level Representations Improved deep metric learning with multi-class n-pair loss objective,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.584657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.909212Z digest=sha256:4fcbc9c9cddb16c9060ba045dadcbdfa11ae4dde0ae39b4d894fb6de0f11d72b

Observation feb81784-cd94-492f-9391-a4fa84be7089 · outbound

This paper cites Unsupervised feature learning via non-parametric instance discrimination,.

Federated Contrastive Learning of Graph-Level Representations Unsupervised feature learning via non-parametric instance discrimination,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.563636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.914217Z digest=sha256:761845b53c764acbc41506262743f93ee4ee8a6aeed25c408767ec8ef186f91a

Observation 384e9f34-cebc-4004-9fef-aa34f3453e88 · outbound

This paper cites Gcc: Graph contrastive coding for graph neural network pre- training,.

Federated Contrastive Learning of Graph-Level Representations Gcc: Graph contrastive coding for graph neural network pre- training,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.546374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.918775Z digest=sha256:37141612571d31cf1c02e6f0819aebc285ec1633da18e45fe1b33393264275b3

Observation 59966ff9-88fb-4dfc-a2dd-c7891ebf87f8 · outbound

This paper cites Collaborative unsupervised visual representation learning from decentralized data,.

Federated Contrastive Learning of Graph-Level Representations Collaborative unsupervised visual representation learning from decentralized data,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.531107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.922973Z digest=sha256:9c37a927da6badcd4f2df903a69c4abe1c9b049e7014596bb57fe7cd41e2387f

Observation ae4b8a67-14f0-42a0-974d-34d14153c3eb · outbound

This paper cites FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks.

Federated Contrastive Learning of Graph-Level Representations FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-12T17:59:30.926762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:59:30.926762Z digest=sha256:ce943d86ee212f229637696d92740b68578ef6eb10163d405f9f40bfa26af018

Observation 464fdf26-8862-43e8-96fa-8bca842b77cb · outbound

This paper cites Spreadgnn: Serverless multi-task federated learning for graph neural networks,.

Federated Contrastive Learning of Graph-Level Representations Spreadgnn: Serverless multi-task federated learning for graph neural networks,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.515877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.931016Z digest=sha256:726d0e6cea94e4763fa6fa72a40993683e174c40bbf4ee86302ac3594d09da79

Observation 2818e80c-c981-4195-9417-3b017477bca9 · outbound

This paper cites Born again neural networks,.

Federated Contrastive Learning of Graph-Level Representations Born again neural networks,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.501804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.935364Z digest=sha256:3149d392c7faf674170f04ee0b35ea98523f6199a33dded9b7ad8c3e74bced41

Observation fcfe77c6-363b-4645-a47d-e31df2d5f051 · outbound

This paper cites Understanding and Improving Knowledge Distillation.

Federated Contrastive Learning of Graph-Level Representations Understanding and Improving Knowledge Distillation

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-12T17:59:30.939776Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:59:30.939776Z digest=sha256:ad98e10cfb993fb2594e73fd54c022b66a14409bb657d8097c1eed0e3353c0c2

Observation 89394aca-2c2c-4481-909a-668d82cc299b · outbound

This paper cites Diffusion Improves Graph Learning.

Federated Contrastive Learning of Graph-Level Representations Diffusion Improves Graph Learning

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-12T17:59:30.944475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:59:30.944475Z digest=sha256:a5c4c169f4270f5ff05d333dc4eefd47cf8cc38d2606120bf465f0d9aa596b1e

Observation 590f3522-52bb-48ff-8de3-458cec657666 · outbound

This paper cites Representation learning on graphs with jumping knowledge networks,.

Federated Contrastive Learning of Graph-Level Representations Representation learning on graphs with jumping knowledge networks,

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-12T17:59:30.949756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:59:30.949756Z digest=sha256:d993380f4ccbfa7791ab7ec5d137593fcc0475b97763a63c27f6943b92230609

Observation 2367d88d-7446-4125-844e-a7f39b050a31 · outbound

This paper cites Protein function prediction via graph kernels,.

Federated Contrastive Learning of Graph-Level Representations Protein function prediction via graph kernels,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.478370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.954060Z digest=sha256:db969d45ec58d0106d7f1087925556886e4ae655425ed0a4fa9311f06e7dbfed

Observation 5cbcc172-7d9f-4db7-920f-209595ce1ae8 · outbound

This paper cites Spline-fitting with a genetic algorithm: a method for developing classification structure- activity relationships,.

Federated Contrastive Learning of Graph-Level Representations Spline-fitting with a genetic algorithm: a method for developing classification structure- activity relationships,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.463972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.958540Z digest=sha256:68b4793bee404b2eec51c9b6a41e8334be99ec821ba9aa282141ec3f97cebf54

Observation 23c0fc8b-0ceb-42dc-aba3-bbfb38bf7c66 · outbound

This paper cites Comparison of descriptor spaces for chemical compound retrieval and classification,.

Federated Contrastive Learning of Graph-Level Representations Comparison of descriptor spaces for chemical compound retrieval and classification,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.447863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.962999Z digest=sha256:c3a38dd94595c34a6523e40379f9375ea1e21f84e49e85c554076b7b2acb0d2f

Observation e024d2f2-e97a-4909-910e-ff348e252f5c · outbound

This paper cites TUDataset: A collection of benchmark datasets for learning with graphs.

Federated Contrastive Learning of Graph-Level Representations TUDataset: A collection of benchmark datasets for learning with graphs

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-12T17:59:30.967542Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:59:30.967542Z digest=sha256:883453025660e2220dc0b7ce0b0800924bbd5ec28949479e19b95465c7e34b49

Observation bbc6244d-ffeb-4067-b694-a3ea2e2450fd · outbound

This paper cites Fast graph representation learning with PyTorch Geometric,.

Federated Contrastive Learning of Graph-Level Representations Fast graph representation learning with PyTorch Geometric,

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-12T17:59:30.972462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:59:30.972462Z digest=sha256:051251346e884a853734e12090e05fdf5923483e3f69cbd4ccb41d32cedfa230

Observation e2c10302-e1da-44a6-a0d6-3f0ce8efde8a · outbound

This paper cites Decoupled weight decay regularization,.

Federated Contrastive Learning of Graph-Level Representations Decoupled weight decay regularization,

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-12T17:59:30.976975Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:59:30.976975Z digest=sha256:7ef08b439034de7ce8c3ada372e404821bc61b0ddc61f9554b347246fc2a270c

Observation d8794945-03d7-4d9d-8acd-f9d4895dfe8c · outbound

This paper cites Scikit-learn: Machine learning in Python,.

Federated Contrastive Learning of Graph-Level Representations Scikit-learn: Machine learning in Python,

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-12T17:59:30.981504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:59:30.981504Z digest=sha256:146201faebae29552a4960b10a10d07e444ecf0198690a2513166ba33e63ec09

Observation 5f6b70b1-1b91-4c8e-9c96-1b0dabc3c10c · outbound

This paper cites Federated optimization in heterogeneous networks,.

Federated Contrastive Learning of Graph-Level Representations Federated optimization in heterogeneous networks,

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-12T17:59:30.986247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:59:30.986247Z digest=sha256:71e7489f0e7576e977d8cad0e8a984d66835fb7dd70804cdd0d714e9e009a622

Observation 2f5e5079-74bd-4e26-8b11-151e480cdda2 · outbound

This paper cites A metric for distributions with applications to image databases,.

Federated Contrastive Learning of Graph-Level Representations A metric for distributions with applications to image databases,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.394075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.990893Z digest=sha256:f0d03e910f72645975a70ecb4e586a39f9116a0e81f622608516d616f0159be6

Observation edbbfba4-63ca-47d8-973e-d9ede7737bc7 · outbound

This paper cites Federated visual classification with real-world data distribution,.

Federated Contrastive Learning of Graph-Level Representations Federated visual classification with real-world data distribution,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.379255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:30.995802Z digest=sha256:e975a79ebdf9631576069e122dd86ed3723eacea03ae53732c3afcf48ece191d

Observation 66995c17-b890-4386-8601-f35265914a76 · outbound

This paper cites A k-means clustering algorithm,.

Federated Contrastive Learning of Graph-Level Representations A k-means clustering algorithm,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.364984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:31.000378Z digest=sha256:dce59ec533b3f71c58b10d70fc951e1b4a81bf2545007db64e8a1b129b544dcf

Observation e934fbd5-cb20-4ce3-9cda-2f345d2282d8 · outbound

This paper cites Accelerating t-sne using tree-based algorithms,.

Federated Contrastive Learning of Graph-Level Representations Accelerating t-sne using tree-based algorithms,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:59:31.348311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-12T17:59:31.004993Z digest=sha256:37e47ea5b3b8dcd81eb5b8d30785f5a72c2d391723c6abbe213e1bcc4a2bad1a

Pith citing papers

No inbound Pith citation observations are available.