Sequential Motifs in Observed Walks

LaRock, Timothy; Scholtes, Ingo; Eliassi-Rad, Tina

doi:10.48550/arxiv.2112.05642

Cited by 2 publications

(2 citation statements)

References 66 publications

(109 reference statements)

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“…They observe highly different distributions of these motifs between real-world and artificial networks. In [47], the authors analyze the distribution of motifs in directed networks, which they call sequential motifs. They propose a connection between sequential motifs and higher order networks, and analyze data from passenger trips through the airport network in the United States and also article navigation in Wikipedia.…”

Section: Related Workmentioning

confidence: 99%

City Motifs as Revealed by Similarity Between Hierarchical Features

Domingues¹,

Tokuda²,

Costa³

2022

Preprint

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Several natural and theoretical networks can be broken down into smaller portions, or subgraphs corresponding to neighborhoods. The more frequent of these neighborhoods can then be understood as motifs of the network, being therefore important for better characterizing and understanding of the overall structure. Several developments in network science have relied on this interesting concept, with ample applications in areas including systems biology, computational neuroscience, economy and ecology. The present work aims at reporting an unsupervised methodology capable of identifying motifs respective to streets networks, the latter corresponding to graphs obtained from city plans by considering street junctions and terminations as nodes while the links are defined by the streets. Remarkable results are described, including the identification of nine stable and informative motifs, which have been allowed by three critically important factors: (i) adoption of five hierarchical measurements to locally characterize the neighborhoods of nodes in the streets networks; (ii) adoption of an effective coincidence methodology for translating datasets into networks; and (iii) definition of the motifs in statistical terms by using community finding methodology. The nine identified motifs are characterized and discussed from several perspective, including their mutual similarity, visualization, histograms of measurements, and geographical adjacency in the original cities. Also presented is the analysis of the effect of the adopted features on the obtained networks as well as a simple supervised learning method capable of assigning reference motifs to cities.

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Section: Related Workmentioning

confidence: 99%

City Motifs as Revealed by Similarity Between Hierarchical Features

Domingues¹,

Tokuda²,

Costa³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…They observed highly different distributions of these motifs between realworld and artificial networks. In [55], the authors analyzed the distribution of motifs in directed networks, which they called sequential motifs. They propose a connection between sequential motifs and higher order networks, and analyze data from passenger trips through the airport network in the United States and also article navigation in Wikipedia.…”

Section: Related Workmentioning

confidence: 99%

Identification of city motifs: a method based on modularity and similarity between hierarchical features of urban networks

2022

View full text Add to dashboard Cite

Several natural and theoretical networks can be broken down into smaller portions, henceforth called neighborhoods. The more frequent of these can then be understood as motifs of the network, being therefore important for better characterizing and understanding of its overall structure. Several developments in network science have relied on this interesting concept, with ample applications in areas including systems biology, computational neuroscience, economy and ecology. The present work aims at reporting a methodology capable of automatically identifying motifs respective to streets networks, i.e. graphs obtained from city plans by considering street junctions and terminations as nodes while the links are defined by the streets. Interesting results are described, including the identification of nine characteristic motifs, which have been obtained by three important considerations: (i) adoption of five hierarchical measurements to locally characterize the neighborhoods of nodes in the streets networks; (ii) adoption of an effective coincidence similarity methodology for translating datasets into networks; and (iii) definition of the motifs in statistical terms by using community finding methodology. The nine identified motifs are characterized and discussed from several perspectives, including their mutual similarity, visualization, histograms of measurements, and geographical adjacency in the original cities. Also presented is the analysis of the effect of the adopted features on the obtained networks as well as a simple supervised learning method capable of assigning reference motifs to cities.

show abstract

Sequential Motifs in Observed Walks

Cited by 2 publications

References 66 publications

City Motifs as Revealed by Similarity Between Hierarchical Features

City Motifs as Revealed by Similarity Between Hierarchical Features

Identification of city motifs: a method based on modularity and similarity between hierarchical features of urban networks

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