Paper Citation Record · LEDGER

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models

As of 10 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 1 inbound Pith citation observation for arXiv:2507.04027.

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

pith.paper-citation-record.v1
2507.04027 v1

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:03:16.209528Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:24:40.657236Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-06T17:24:46.971086Z

Reference resolution

54 of 54 outbound references displayed

verified exact1
verified fuzzy40
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation aa0918ed-3e25-4cd4-b83e-ba3198cfc255 · outbound

This paper cites Rosvall, A.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Rosvall, A

Reference 1

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Observation 9ea82d32-5c52-459e-862b-388ac93ca869 · outbound

This paper cites Pflieger and C.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Pflieger and C

Reference 2

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Observation 5ab7a421-e012-409a-a0d0-a42f9d57ed34 · outbound

This paper cites Jiang and C.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Jiang and C

Reference 3

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Observation 11cde00f-6ae6-427c-b53f-8d20ed42fd3e · outbound

This paper cites an unresolved cited work.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Unresolved cited work

Reference 4

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Observation a160aa57-92d0-4c45-9f51-6a310504f170 · outbound

This paper cites Urban road network expansion and its driving variables: A case study of nanjing city.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Urban road network expansion and its driving variables: A case study of nanjing city

Reference 5

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Observation 29ab50fa-bc4c-4939-8f5b-d105f03689d5 · outbound

This paper cites an unresolved cited work.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Unresolved cited work

Reference 6

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Observation 5abac61f-8d81-4d32-8ca2-ad7b0c17667d · outbound

This paper cites Lee and J.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Lee and J

Reference 7

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Observation 251a3428-70d7-4eab-8e4b-a46cde7f2ba9 · outbound

This paper cites an unresolved cited work.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Unresolved cited work

Reference 8

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Observation 1802462b-6770-4647-985d-142a6615466f · outbound

This paper cites node2vec: Scalable feature learning for networks.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models node2vec: Scalable feature learning for networks

Reference 9

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Observation 58ae9cfc-fb10-4721-adb1-23bcf54107d2 · outbound

This paper cites Line: Large-scale information network embedding.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Line: Large-scale information network embedding

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.544883Z

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Observation 686e489a-8a40-4a92-971d-c3e69e0b60e2 · outbound

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

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Semi-Supervised Classification with Graph Convolutional Networks

Reference 11

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Observation dc98524e-d566-49cd-be94-3422070c6bc1 · outbound

This paper cites Sobolevsky and A.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Sobolevsky and A

Reference 12

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Observation 000867b6-bb31-4fd7-8d03-fda764cd2daf · outbound

This paper cites Velickovic, G.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Velickovic, G

Reference 13

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Observation 0b784a32-e314-4bb0-ac2a-fc7a0104abee · outbound

This paper cites Kempinska and R.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Kempinska and R

Reference 14

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Observation fd594afe-fde5-4095-981a-00c77dc501c4 · outbound

This paper cites Pagani, A.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Pagani, A

Reference 15

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Observation 672e4ae0-3b08-4624-825a-a50b19b8e5ae · outbound

This paper cites Huang, D.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Huang, D

Reference 16

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Observation 59be4ee9-175b-45be-b6fc-c9b4ab2940a4 · outbound

This paper cites Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT).

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Effective Urban Region Representation Learning Using Heterogeneous Urban Graph Attention Network (HUGAT)

Reference 17

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Observation e3983c36-eaf9-425e-b439-737ac6737727 · outbound

This paper cites Mishina et al.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Mishina et al

Reference 18

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Observation 39189e9a-f30f-4d09-8ba3-2d001fbf697b · outbound

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Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Unresolved cited work

Reference 19

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Observation 1597fbb0-436b-43bf-a2a7-7385d9bb1707 · outbound

This paper cites an unresolved cited work.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Unresolved cited work

Reference 20

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Observation 62e09b3a-ba67-4667-bae3-67a1f6f426f0 · outbound

This paper cites Graph neural network approach to predict the effects of road capacity reduction policies: A case study for paris, france, 2024.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Graph neural network approach to predict the effects of road capacity reduction policies: A case study for paris, france, 2024

Reference 21

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Observation 477fecec-74d2-48d9-bf21-00bcd41b5983 · outbound

This paper cites A multi-modal graph neural network approach to traffic risk forecasting in smart urban sensing.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models A multi-modal graph neural network approach to traffic risk forecasting in smart urban sensing

Reference 22

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Observation a26d8f3b-495a-4535-914e-e64d8fd0a4ce · outbound

This paper cites Heterogeneous graph neural networks with post-hoc explanations for multi-modal and explainable land use in- ference, 2024.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Heterogeneous graph neural networks with post-hoc explanations for multi-modal and explainable land use in- ference, 2024

Reference 23

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Observation a5d16ebb-6af7-40fa-afcb-674b1c715485 · outbound

This paper cites Khulbe, A.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Khulbe, A

Reference 24

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Observation 367005ec-6d01-4605-ac90-36a4f736a047 · outbound

This paper cites Longitudinal employer-household dynamics.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Longitudinal employer-household dynamics

Reference 25

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Observation a8f78e51-086e-49fb-8e0f-290fdf45826c · outbound

This paper cites American community survey data.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models American community survey data

Reference 26

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Observation 2ae7df9b-b626-489f-ae71-57fd3387736e · outbound

This paper cites Nyc 311 data.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Nyc 311 data

Reference 27

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Observation d224dc2e-47fb-452f-9f7c-bc05ceb1ab35 · outbound

This paper cites Direction aware positional and structural encoding for directed graph neural networks.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Direction aware positional and structural encoding for directed graph neural networks

Reference 28

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Observation 3bc3d7de-fb36-411f-9404-c347d1e73473 · outbound

This paper cites A Generalization of Transformer Networks to Graphs.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models A Generalization of Transformer Networks to Graphs

Reference 29

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This paper cites Recipe for a general, powerful, scalable graph transformer.Advances in Neural Information Processing Systems, 35:14501–14515, 2022.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Recipe for a general, powerful, scalable graph transformer.Advances in Neural Information Processing Systems, 35:14501–14515, 2022

Reference 30

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This paper cites Rethinking graph transformers with spectral attention.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Rethinking graph transformers with spectral attention

Reference 31

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Observation d516ad65-d586-4a29-8203-32af2ba5e65a · outbound

This paper cites Do transformers really perform badly for graph representation? In Advances in Neural Information Processing Systems, 2021.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Do transformers really perform badly for graph representation? In Advances in Neural Information Processing Systems, 2021

Reference 32

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Observation f0fc1cf5-ae68-4639-abfc-5caf7d3d10f7 · outbound

This paper cites Distance encoding: Design provably more powerful neural networks for graph representation learning.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Distance encoding: Design provably more powerful neural networks for graph representation learning

Reference 33

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This paper cites Graph neu- ral networks with learnable structural and positional representations.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Graph neu- ral networks with learnable structural and positional representations

Reference 34

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This paper cites Rewiring with Positional Encodings for Graph Neural Networks.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Rewiring with Positional Encodings for Graph Neural Networks

Reference 35

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This paper cites Sume: Semantic-enhanced urban mobility network embedding for user demographic inference.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Sume: Semantic-enhanced urban mobility network embedding for user demographic inference

Reference 36

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Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Unresolved cited work

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This paper cites Jain and Richard C.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Jain and Richard C

Reference 38

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Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Unresolved cited work

Reference 39

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Observation f4d2d34a-76ad-440a-9d1e-ec293b444c98 · outbound

This paper cites Distance deterrence comparison in urban commute among different socioeconomic groups: A normalized linear piece-wise gravity model.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Distance deterrence comparison in urban commute among different socioeconomic groups: A normalized linear piece-wise gravity model

Reference 40

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This paper cites Impact of income on urban commute across major cities in us.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Impact of income on urban commute across major cities in us

Reference 41

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This paper cites How Powerful are Graph Neural Networks?.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models How Powerful are Graph Neural Networks?

Reference 42

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This paper cites Hicks and P.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Hicks and P

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:18.077160Z

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Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Unresolved cited work

Reference 44

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Observation 56b003cd-bb99-409b-9402-0c8add126553 · outbound

This paper cites Leslie and Breandán Ó hUallacháin.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Leslie and Breandán Ó hUallacháin

Reference 45

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This paper cites Structural deep network embedding.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Structural deep network embedding

Reference 46

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This paper cites Aggarwal, and Thomas S.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Aggarwal, and Thomas S

Reference 47

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Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Unresolved cited work

Reference 48

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This paper cites Yap and F.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Yap and F

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:17.467357Z

Source-reported events for the cited work

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Observation e6d2ad28-0cae-4171-ba65-3e26e821fb59 · outbound

This paper cites https://data.boston.gov/dataset/311-service-requests/resource/ f53ebccd-bc61-49f9-83db-625f209c95f5 , 2023.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models https://data.boston.gov/dataset/311-service-requests/resource/ f53ebccd-bc61-49f9-83db-625f209c95f5 , 2023

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:17.250048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bc0582eb-8697-423d-af8f-6abb45dec698 · outbound

This paper cites https://data.cityofchicago.org/Service-Requests/311-Service-Requests/ v6vf-nfxy, 2023.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models https://data.cityofchicago.org/Service-Requests/311-Service-Requests/ v6vf-nfxy, 2023

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:17.116619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a630537-82ed-4c71-a68a-a9cacafb4f95 · outbound

This paper cites The pagerank citation ranking: bringing order to the web.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models The pagerank citation ranking: bringing order to the web

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:16.969368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 31c9bc7f-fff6-4c29-ba7f-563393c2f6bb · outbound

This paper cites Measuring the vibrancy of urban neighborhoods using mobile phone data with an improved pagerank algorithm.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Measuring the vibrancy of urban neighborhoods using mobile phone data with an improved pagerank algorithm

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:16.801564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 09328b98-2330-4fc5-a951-b412e745f10d · outbound

This paper cites Ranking spaces for predicting human movement in an urban environment.

Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models Ranking spaces for predicting human movement in an urban environment

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:03:16.655699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation 22418715-20e8-46bc-b7c4-380779149e63 · inbound

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities cites this paper.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Commute Networks as a Signature of Urban Socioeconomic Performance: Evaluating Mobility Structures with Deep Learning Models

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-06T17:24:47.172121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.