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Social Network Analysis and Mining
Published in:

01-12-2023 | Original Article

Modeling and predicting individual transitions within the homelessness system

Authors: Khandker Sadia Rahman, Charalampos Chelmis

Published in: Social Network Analysis and Mining | Issue 1/2023

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Abstract

The article 'Modeling and predicting individual transitions within the homelessness system' explores the complex issue of homelessness by introducing a sophisticated model to predict future service assignments for individuals experiencing homelessness. The model employs machine learning techniques to infer a network of homeless services from administrative data, capturing the intricate dynamics of the homeless system. By defining a similarity score for trajectories, the model predicts the most likely service an individual will be assigned to next, based on their historical data. The experimental evaluation demonstrates the model's ability to accurately match observed sequences, outperforming baseline methods. This innovative approach can serve as a building block for more complex applications, such as recommending service assignments, and addresses the need for data-driven solutions in the homelessness sector.
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Metadata
Title
Modeling and predicting individual transitions within the homelessness system
Authors
Khandker Sadia Rahman
Charalampos Chelmis
Publication date
01-12-2023
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2023
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-023-01083-y

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