Extraction of conjugate main-stream structures from a complex network flow

Tamura, Koutarou; Takayasu, Hideki; Takayasu, Misako

doi:10.1103/physreve.91.042815

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…We can extract a basin structure that nearly fits the loopless tree-like structure from any given flow network by considering the limit of γ → ∞; for money flow among firms, the tree-like structure roughly represents the hierarchical relations of the production process of final goods from raw materials. The idea is almost equivalent to the basin structure of the flow network that we proposed previously 24 .…”

Section: Discussionmentioning

confidence: 99%

“…These types of diffusion-type generalized linear transport are being analyzed intensively for various complex networks 16 – 20 , 23 and used for an elementary process in a social transport model 20 , although the gravity interaction is commonly observed and reported in many fields. On the basis of the bias of the flow, the authors proposed a method to extract the mainstream and basin structure of complex network flow and demonstrated a money flow case in a business transaction network 24 .…”

Section: Introductionmentioning

confidence: 99%

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Diffusion-localization transition caused by nonlinear transport on complex networks

Tamura

Takayasu

2018

Sci Rep

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We analyzed nonlinear transport as defined for directed complex networks, where the flux from one node to a neighboring node is given preferentially according to the scalar quantities at the neighbor nodes. This is known as the generalized gravity interaction. In our research, we discovered a novel phase transition type. In the diffusion phase, the scalar quantity is scattered over the whole system, whereas in the localization phase, the flow tends to form localized confluence patterns owing to nonlinearity, resulting in the appearance of special nodes that irreversibly attract huge amounts of flow. We analytically considered the transition for selected network configurations, demonstrating that the transition point depends on the network topology. We also demonstrated that the diffusion phase of this transport model fits well with data from business firms, implying that the whole network structure can be used to model money flow in the real world.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diffusion-localization transition caused by nonlinear transport on complex networks

Tamura

Takayasu

2018

Sci Rep

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“…However, there is little knowledge how to apply any methodology of network analysis. For analyzing network flows on a single type of nodes and edges, there is a known statistical method to extract main-stream structures for a given complex network flow by trimming less effective links [6]. And human mobility patterns are found to be highly biased and easily predictable based on some analyses about network motifs [7,8].…”

Section: Introductionmentioning

confidence: 99%

Finding Hidden Location Patterns of Two Competitive Supermarkets in Thailand

Khumsri¹,

Fujihara²

2017

Proceedings of the Asia-Pacific Econophysics Conference 2016 — Big Data Analysis and Modeling Toward Super Smart Society — (APE

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There are two famous supermarkets in Thailand: Big C and Lotus. They are the highest competitive supermarkets whose hold the most market share by lots of promotions and also gather all convenience services including banking, restaurant, and others. In recent years, they gradually expand their stores and they take a similar strategy to determine where to locate a store. It is important for them to consider store allocation to obtain new customers efficiently. To consider this, we gather geographical locations of these supermarkets from Twitter using Twitter API. We gathered tweets having these supermarket names and geotags for seven months. To extract hidden location patterns from gathered data, we introduce location motif which is a directed subgraph whose edges are linked to every pair of the shortest-distance opponent node. We investigate every possible configuration of location motif when they have a small number of nodes and find that the configuration increases exponentially. We also visualize location motifs generated from gathered data on the map of Thailand and count the frequency of observed location motifs. As a result, we find that even if the possible location motifs exponentially increase as the number of nodes grows, limited location motifs can be observed. Using location motif, we successfully find an evidence of biased store allocation in reality.

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Extraction of conjugate main-stream structures from a complex network flow

Cited by 2 publications

References 29 publications

Diffusion-localization transition caused by nonlinear transport on complex networks

Diffusion-localization transition caused by nonlinear transport on complex networks

Finding Hidden Location Patterns of Two Competitive Supermarkets in Thailand

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