Paper Citation Record · LEDGER

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning

As of 22 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.15195.

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

pith.paper-citation-record.v1
2507.15195 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:43:41.146034Z

measured 41 of 41 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 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

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 017355c9-2464-4dc1-b2b7-4bf7bce8f86a · outbound

This paper cites Controllability of structural brain networks,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Controllability of structural brain networks,

Reference 1

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Source-reported events for the cited work

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Observation 022729a8-21ab-420b-a85f-6df4fe5995e7 · outbound

This paper cites Friedland, Control system design: an introduction to state-space methods.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Friedland, Control system design: an introduction to state-space methods

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:48.041730Z

Source-reported events for the cited work

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Observation f89d2517-f949-4b35-b5de-4a0c318076a9 · outbound

This paper cites Controllability metrics, limitations and algorithms for complex net- works,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Controllability metrics, limitations and algorithms for complex net- works,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:47.800301Z

Source-reported events for the cited work

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Observation e7da472b-7c47-460d-a3ea-a828f1658c72 · outbound

This paper cites an unresolved cited work.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-06T15:43:47.651105Z

Source-reported events for the cited work

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Observation a8a29f7b-014b-4e97-8d29-13a4fba433b9 · outbound

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

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Semi-supervised classification with graph convolutional networks,

Reference 5

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Source-reported events for the cited work

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Observation 826df278-1c43-449e-9a8b-e499ea13b828 · outbound

This paper cites Inductive representation learning on large graphs,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Inductive representation learning on large graphs,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:38.385024Z

Source-reported events for the cited work

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Observation 71bcb869-af50-48c7-8415-31ee8782dc44 · outbound

This paper cites How powerful are graph neural networks?.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning How powerful are graph neural networks?

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:47.486460Z

Source-reported events for the cited work

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Observation d8f48e11-6599-40a2-9b5e-2a1b1f740232 · outbound

This paper cites Contributions to the theory of optimal control,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Contributions to the theory of optimal control,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:47.245213Z

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 740b7c8d-e81f-433b-9166-22a55ffadfcc · outbound

This paper cites Diffusion improves graph learning,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Diffusion improves graph learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:47.029380Z

Source-reported events for the cited work

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Observation e7ff547f-0dd0-4973-a1f0-4e8c5e9f2b15 · outbound

This paper cites Centrality in social networks: Conceptual clarification,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Centrality in social networks: Conceptual clarification,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:46.825756Z

Source-reported events for the cited work

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Observation 0892778f-e412-4ea7-af8c-4095804a2982 · outbound

This paper cites Network controllability in transmodal cortex predicts positive psychosis spectrum symptoms,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Network controllability in transmodal cortex predicts positive psychosis spectrum symptoms,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:46.607274Z

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 599fa0fe-e69d-4c22-bf4c-cc5a6450d41a · outbound

This paper cites Improving graph machine learning perfor- mance through feature augmentation based on network control theory,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Improving graph machine learning perfor- mance through feature augmentation based on network control theory,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:46.436395Z

Source-reported events for the cited work

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Observation cc857a7f-93e0-40fe-ba37-3df00f444580 · outbound

This paper cites Network controllability perspectives on graph representation,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Network controllability perspectives on graph representation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:46.267109Z

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 1ac67e6e-9fb6-430b-be0f-efe14a3ac429 · outbound

This paper cites Using network control theory to study the dynamics of the structural connectome,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Using network control theory to study the dynamics of the structural connectome,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:46.024938Z

Source-reported events for the cited work

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Observation 66615ebb-f914-4286-9d27-39c9c86c49e1 · outbound

This paper cites Linear dynamics and control of brain networks,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Linear dynamics and control of brain networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:45.847369Z

Source-reported events for the cited work

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Observation 8113f451-3553-4647-a393-7e9ed3ba477c · outbound

This paper cites Control the GNN: Utilizing Neural Controller with Lyapunov Stability for Test-Time Feature Reconstruction.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Control the GNN: Utilizing Neural Controller with Lyapunov Stability for Test-Time Feature Reconstruction

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-06T15:43:41.474538Z

Source-reported events for the cited work

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Observation c5e13e97-ddf2-4f33-9797-1241e08ce75f · outbound

This paper cites A survey on graph kernels,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning A survey on graph kernels,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:45.627228Z

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 988b2520-eefe-4141-bb8e-acfea5be827d · outbound

This paper cites Wasserstein weisfeiler-lehman graph kernels,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Wasserstein weisfeiler-lehman graph kernels,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:45.461568Z

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 434f565b-4f66-4061-aadf-e288f395473b · outbound

This paper cites The multiscale laplacian graph kernel,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning The multiscale laplacian graph kernel,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:45.240177Z

Source-reported events for the cited work

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Observation c9167eb4-6914-4d05-a407-1854dd332b44 · outbound

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

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Convolutional neural networks on graphs with fast localized spectral filtering,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:45.073882Z

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 7c9eba34-48a6-4d28-a1c9-d99834ae8ef0 · outbound

This paper cites Spectral networks and locally connected networks on graphs,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Spectral networks and locally connected networks on graphs,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:44.851613Z

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 4d567ec1-df01-48d8-9ccc-4da735b3ae45 · outbound

This paper cites Residual Gated Graph ConvNets.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Residual Gated Graph ConvNets

Reference 22

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Source-reported events for the cited work

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Observation b3d706e8-e4cb-434f-8168-cc9e33829344 · outbound

This paper cites Graph attention networks,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Graph attention networks,

Reference 23

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Source-reported events for the cited work

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Observation 10693b33-7e36-455d-8ddd-561bffad2fac · outbound

This paper cites Weisfeiler and leman go neural: Higher-order graph neural networks,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Weisfeiler and leman go neural: Higher-order graph neural networks,

Reference 24

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Source-reported events for the cited work

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Observation 800195ff-c282-405c-ad58-b69ab56f4b81 · outbound

This paper cites Graph transformer networks,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Graph transformer networks,

Reference 25

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Source-reported events for the cited work

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Observation d64b13cb-22f6-43eb-a69d-70945a5db4c1 · outbound

This paper cites Hierarchical graph representation learning with differentiable pooling,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Hierarchical graph representation learning with differentiable pooling,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:44.414249Z

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 fa298d5a-7e81-467e-b177-56d2f235ccd2 · outbound

This paper cites Edge contraction pooling for graph neural networks,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Edge contraction pooling for graph neural networks,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:44.184824Z

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 29c92829-bbf2-4b8a-9a8e-51a86c193db7 · outbound

This paper cites A comprehensive survey on graph neural networks,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning A comprehensive survey on graph neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:43.998361Z

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 13727764-0883-44b5-9cf8-9ef139f37eca · outbound

This paper cites A simple yet effective baseline for non-attributed graph classification.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning A simple yet effective baseline for non-attributed graph classification

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:40.385675Z

Source-reported events for the cited work

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Observation 40a8eb8b-0f9e-45de-a8bc-b5f2c881f7d4 · outbound

This paper cites Attention models in graphs: A survey,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Attention models in graphs: A survey,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:43.734870Z

Source-reported events for the cited work

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Observation 83a9321c-c4b6-4833-b0b7-e41044322e89 · outbound

This paper cites Laplacian eigenmaps for dimensionality reduction and data representation,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Laplacian eigenmaps for dimensionality reduction and data representation,

Reference 31

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Source-reported events for the cited work

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Observation eba0ee09-d4e0-46be-bd8c-3a65feab6ff7 · outbound

This paper cites Benchmarking graph neural networks,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Benchmarking graph neural networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:43.544468Z

Source-reported events for the cited work

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Observation 5de4c936-a1a4-48b9-aea7-fed8a9dfd771 · outbound

This paper cites struc2vec: Learning node representations from structural identity,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning struc2vec: Learning node representations from structural identity,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:43.354325Z

Source-reported events for the cited work

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Observation 073358b1-8af7-4197-9416-54148a8c8814 · outbound

This paper cites role2vec: Role-based network embeddings,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning role2vec: Role-based network embeddings,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:40.683380Z

Source-reported events for the cited work

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Observation dd4bd52f-01c0-4b72-a488-db7bda048007 · outbound

This paper cites Learning structural node embeddings via diffusion wavelets,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Learning structural node embeddings via diffusion wavelets,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:43.143095Z

Source-reported events for the cited work

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Observation 80b55cba-8147-4210-b8d2-15aa451a81fc · outbound

This paper cites A unified view on graph neural networks as graph signal denoising,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning A unified view on graph neural networks as graph signal denoising,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:42.961088Z

Source-reported events for the cited work

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Observation 4558c9dc-7a02-4c8e-81b2-2c177e757385 · outbound

This paper cites Control-based graph embeddings with data augmentation for contrastive learning,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Control-based graph embeddings with data augmentation for contrastive learning,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:42.794775Z

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 0193dfe5-0c2a-44fb-b7db-73cda9d49bfa · outbound

This paper cites Social network analysis: Methods and applications,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Social network analysis: Methods and applications,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:42.589795Z

Source-reported events for the cited work

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Observation ceb1c0fc-851f-4908-b859-26cd18034e74 · outbound

This paper cites Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:42.312590Z

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 3190c2db-034e-49c7-82a2-02011a8f6dfb · outbound

This paper cites Masked label prediction: Unified message passing model for semi-supervised classification,.

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning Masked label prediction: Unified message passing model for semi-supervised classification,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:42.054029Z

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 2376c2d0-feae-4714-9cc1-f1aad96f9557 · outbound

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

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning An end-to-end deep learning architecture for graph classification,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:43:41.743202Z

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

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