Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities | Paper Citation Record

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This paper cites Defining a city—delineating urban areas using cell-phone data.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Defining a city—delineating urban areas using cell-phone data

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This paper cites Delineation of the Shanghai megacity region of China from a commuting perspective: Study based on cell phone network data in the Yangtze River Delta.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Delineation of the Shanghai megacity region of China from a commuting perspective: Study based on cell phone network data in the Yangtze River Delta

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This paper cites Detecting the regional delineation from a network of social media user interactions with spatial constraint: A case study of Shenzhen, China.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Detecting the regional delineation from a network of social media user interactions with spatial constraint: A case study of Shenzhen, China

Reference 3

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This paper cites Redrawing the map of Great Britain from a network of human interactions.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Redrawing the map of Great Britain from a network of human interactions

Reference 4

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Community detection in graphs

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities The analysis and delimitation of Central Business District using network kernel density estimation

Reference 6

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This paper cites Mobility Networks as a Predictor of Socioeconomic Status in Urban Systems.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Mobility Networks as a Predictor of Socioeconomic Status in Urban Systems

Reference 7

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

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

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This paper cites A Community Detection and Graph-Neural-Network-Based Link Prediction Approach for Scientific Literature.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities A Community Detection and Graph-Neural-Network-Based Link Prediction Approach for Scientific Literature

Reference 9

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Graph neural network inspired algorithm for unsupervised network community detection

Reference 10

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This paper cites Prediction of Urban Population-Facilities Interactions with Graph Neural Network.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Prediction of Urban Population-Facilities Interactions with Graph Neural Network

Reference 11

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Community detection in networks using graph embeddings

Reference 12

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This paper cites Urban delineation through a prism of intraday commute patterns.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Urban delineation through a prism of intraday commute patterns

Reference 13

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This paper cites Human mobility and socioeconomic status: Analysis of Singapore and Boston.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Human mobility and socioeconomic status: Analysis of Singapore and Boston

Reference 14

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This paper cites Relying on the Census in Urban Social Science.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Relying on the Census in Urban Social Science

Reference 15

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This paper cites Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Land development, land use, and urban sprawl in Puerto Rico integrating remote sensing and population census data

Reference 16

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This paper cites Validating the use of census data on education as a measure of socioeconomic status in an occupational cohort.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Validating the use of census data on education as a measure of socioeconomic status in an occupational cohort

Reference 17

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This paper cites Use of Census-based Aggregate Variables to Proxy for Socioeconomic Group: Evidence from National Samples.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Use of Census-based Aggregate Variables to Proxy for Socioeconomic Group: Evidence from National Samples

Reference 18

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This paper cites Longitudinal Employer-Household Dynamics (LEHD) Data, Snapshot Release S2023; 2023.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Longitudinal Employer-Household Dynamics (LEHD) Data, Snapshot Release S2023; 2023

Reference 19

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This paper cites Delineating geographical regions with networks of human interactions in an extensive set of countries.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Delineating geographical regions with networks of human interactions in an extensive set of countries

Reference 20

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Uncovering space-independent communities in spatial networks

Reference 21

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

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Distance deterrence comparison in urban commute among different socioeconomic groups: A normalized linear piece-wise gravity model

Reference 22

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Impact of income on urban commute across major cities in US

Reference 23

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Recipe for a General, Powerful, Scalable graph transformer

Reference 24

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities A Generalization of Transformer Networks to Graphs

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

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Do transformers really perform badly for graph representation? In: Advances in Neural Information Processing Systems; 2021

Reference 26

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities The singular value decomposition: Its computation and some applications

Reference 27

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Implicit SVD for Graph Representation Learning

Reference 28

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Community detection in graphs using singular value decomposition

Reference 29

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This paper cites Learning 3d representations of molecular chirality with invariance to bond rotations.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Learning 3d representations of molecular chirality with invariance to bond rotations

Reference 30

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Rewiring with positional encodings for graph neural networks

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Distance encoding: Design provably more powerful neural networks for graph representation learning

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Graph neural networks with learnable structural and positional representations

Reference 33

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities SUME: Semantic-enhanced Urban Mobility Network Embedding for User Demographic Inference

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities NodeSense2Vec: Spatiotemporal Context-Aware Network Embedding for Heterogeneous Urban Mobility Data

Reference 35

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Semi-supervised classification with graph convolutional networks

Reference 36

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This paper cites How powerful are graph neural networks? arXiv preprint arXiv:181000826.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities How powerful are graph neural networks? arXiv preprint arXiv:181000826

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Deeper insights into graph convolutional networks for semi-supervised learning

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Inductive representation learning on large graphs

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities General optimization technique for high-quality community detection in complex networks

Reference 40

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Community Detection via Maximization of Modularity and Its Variants

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities A review of stochastic block models and extensions for graph clustering

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models

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This paper cites On the Impact of Income, Age, and Travel Distance on the Value of Time.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities On the Impact of Income, Age, and Travel Distance on the Value of Time

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Information theoretic network approach to socioeconomic correlations

Reference 45

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This paper cites The use of Csiszár’s divergence to assess dissimilarities of income distributions of EU countries.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities The use of Csiszár’s divergence to assess dissimilarities of income distributions of EU countries

Reference 46

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Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities Income distributions and decomposable divergence measures

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This paper cites 13956 of Lecture Notes in Computer Science.

Urban delineation through the lens of commute networks: Leveraging graph embeddings to distinguish socioeconomic groups in cities 13956 of Lecture Notes in Computer Science

Reference 2023

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