Group homophily based facility location selection in geo-social networks

Ma, Yuliang; Cui, Ningning; Jiang, Zhong‐Zhong; Yuan, Ye; Wang, Guoren

doi:10.1007/s11280-022-01008-3

Cited by 6 publications

(2 citation statements)

References 42 publications

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“…By leveraging user location data, relevant organizations can gain more accurate insights into population distribution and activities, facilitating timely alerts and the implementation of corresponding rescue and protection measures [7,8]. Additionally, it enables the delivery of localized content to users, enhancing their quality of life [9]. However, challenges arise in directly accessing users' residential location information due to factors such as personal privacy protection and platform policies [10,11].…”

Section: Cmes 2024mentioning

confidence: 99%

Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Zhang

2024

CMES

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Geolocating social media users aims to discover the real geographical locations of users from their publicly available data, which can support online location-based applications such as disaster alerts and local content recommendations. Social relationship-based methods represent a classical approach for geolocating social media. However, geographically proximate relationships are sparse and challenging to discern within social networks, thereby affecting the accuracy of user geolocation. To address this challenge, we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence (NGSI) to improve geolocation accuracy. Firstly, we propose a method for evaluating the homophily of locations based on the k-order neighborhood geographic distribution (k-NGD) similarity among users. There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users. Exploiting this distinction, we filter out non-location-proximate social relationships to enhance location homophily in the social network. To better utilize the location-proximate relationships in social networks, we propose a graph neural network algorithm based on the social structure influence. The algorithm enables us to perform a weighted aggregation of the information of users' multi-hop neighborhood, thereby mitigating the over-smoothing problem of user features and improving user geolocation performance. Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships. Moreover, compared with 7 existing social relationship-based user positioning methods, our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84% to 13.28%.

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Section: Cmes 2024mentioning

confidence: 99%

Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Zhang

2024

CMES

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“…Anvari 44 evaluated the location of a supply facility in China using the AHP and WSM without normalization. Ma et al 45 applied homophily-based relaxation algorithm optimization for the selection of a coffee shop location, which did not need normalization. However, Akyurt et al 46 selected the location for the best air flight base training using the measuring attractiveness by a categorical based evaluation technique (MACBETH) and ranking of alternatives through functional mapping of criterion subintervals into a single interval (RAFSI), which used the arithmetic and harmonic mean for normalization.…”

Section: Literature Reviewmentioning

confidence: 99%

Grey hybrid normalization with period based entropy weighting and relational analysis for cities rankings

Esangbedo,

Wei

2023

Sci Rep

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Researchers have addressed uncertainty in multicriteria decision making from the perspective of the performance values of the alternatives, weighting of the evaluation criteria, and the evaluation methods. Still, they are yet to address the uncertainty caused by the normalization approach. In this paper, we show that different normalization methods, namely sum normalization, min–max normalization, vector normalization, and maximization normalization, can result in different rankings of the alternatives while the performance values and weights are unchanged. We applied the grey system theory to address the problem of uncertainty in this study from three aspects: alternative performance values measurement, criteria weighting, and decision matrix/table normalization within a period. The grey hybrid normalization method is proposed as the main contribution in this paper. Then, we present the rankings of 48 cities under uncertainty to decide the location of a branch office of a Chinese electric vehicle manufacturer as a practical example based on the grey entropy weighting method and grey relational analysis with positive and negative references (GRA-PNR) within the period from the year 2019 to 2021. The research results using this approach ranked New York City the best, with a stock market capitalization of economy validity as the top contributor in terms of weighting. Finally, we used simple additive weighting with grey value and the technique for order of preference by similarity to ideal solution with grey value methods to validate the study results.

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Method of Selecting the Optimal Location of Barrier-Free Bus Stops Using Clustering

Kim

Pyun

Ryu

et al. 2022

Studies in Computational Intelligence

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Group homophily based facility location selection in geo-social networks

Cited by 6 publications

References 42 publications

Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Grey hybrid normalization with period based entropy weighting and relational analysis for cities rankings

Method of Selecting the Optimal Location of Barrier-Free Bus Stops Using Clustering

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