Learning to Predict Transitions within the Homelessness System from Network Trajectories

Khandker Sadia Rahman; Charalampos Chelmis

doi:10.1109/ASONAM55673.2022.10068708

Learning to Predict Transitions within the Homelessness System from Network Trajectories

Khandker Sadia Rahman
, Charalampos Chelmis

Computer Science

University at Albany

University at Albany

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

This study infers the unobserved underlying network of homeless services from administrative data collected by homeless service providers. Both the structure of the inferred network, and historical observations, are used to identify individuals with similar trajectories so that their next assignments can be predicted. Experimental evaluation shows that the proposed approach performs well not only on predicting exit from the system, or simply guessing high frequency services (as most baselines), but is also successful in less frequent scenarios.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
Editors	Jisun An, Chelmis Charalampos, Walid Magdy
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	181-185
Number of pages	5
ISBN (Electronic)	9781665456616
DOIs	https://doi.org/10.1109/ASONAM55673.2022.10068708
State	Published - 2022
Event	14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022 - Virtual, Online, Turkey Duration: Nov 10 2022 → Nov 13 2022

Publication series

Name	Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

Conference

Conference	14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022
Country/Territory	Turkey
City	Virtual, Online
Period	11/10/22 → 11/13/22

Keywords

Complex systems
network inference
similarity

Access to Document

10.1109/ASONAM55673.2022.10068708

Cite this

Rahman, K. S., & Chelmis, C. (2022). Learning to Predict Transitions within the Homelessness System from Network Trajectories. In J. An, C. Charalampos, & W. Magdy (Eds.), Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022 (pp. 181-185). (Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM55673.2022.10068708

Rahman, Khandker Sadia ; Chelmis, Charalampos. / Learning to Predict Transitions within the Homelessness System from Network Trajectories. Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022. editor / Jisun An ; Chelmis Charalampos ; Walid Magdy. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 181-185 (Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022).

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title = "Learning to Predict Transitions within the Homelessness System from Network Trajectories",

abstract = "This study infers the unobserved underlying network of homeless services from administrative data collected by homeless service providers. Both the structure of the inferred network, and historical observations, are used to identify individuals with similar trajectories so that their next assignments can be predicted. Experimental evaluation shows that the proposed approach performs well not only on predicting exit from the system, or simply guessing high frequency services (as most baselines), but is also successful in less frequent scenarios.",

keywords = "Complex systems, network inference, similarity",

author = "Rahman, \{Khandker Sadia\} and Charalampos Chelmis",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022 ; Conference date: 10-11-2022 Through 13-11-2022",

year = "2022",

doi = "10.1109/ASONAM55673.2022.10068708",

language = "English",

series = "Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "181--185",

editor = "Jisun An and Chelmis Charalampos and Walid Magdy",

booktitle = "Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022",

}

Rahman, KS & Chelmis, C 2022, Learning to Predict Transitions within the Homelessness System from Network Trajectories. in J An, C Charalampos & W Magdy (eds), Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022. Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022, Institute of Electrical and Electronics Engineers Inc., pp. 181-185, 14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022, Virtual, Online, Turkey, 11/10/22. https://doi.org/10.1109/ASONAM55673.2022.10068708

Learning to Predict Transitions within the Homelessness System from Network Trajectories. / Rahman, Khandker Sadia; Chelmis, Charalampos.
Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022. ed. / Jisun An; Chelmis Charalampos; Walid Magdy. Institute of Electrical and Electronics Engineers Inc., 2022. p. 181-185 (Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning to Predict Transitions within the Homelessness System from Network Trajectories

AU - Rahman, Khandker Sadia

AU - Chelmis, Charalampos

PY - 2022

Y1 - 2022

N2 - This study infers the unobserved underlying network of homeless services from administrative data collected by homeless service providers. Both the structure of the inferred network, and historical observations, are used to identify individuals with similar trajectories so that their next assignments can be predicted. Experimental evaluation shows that the proposed approach performs well not only on predicting exit from the system, or simply guessing high frequency services (as most baselines), but is also successful in less frequent scenarios.

AB - This study infers the unobserved underlying network of homeless services from administrative data collected by homeless service providers. Both the structure of the inferred network, and historical observations, are used to identify individuals with similar trajectories so that their next assignments can be predicted. Experimental evaluation shows that the proposed approach performs well not only on predicting exit from the system, or simply guessing high frequency services (as most baselines), but is also successful in less frequent scenarios.

KW - Complex systems

KW - network inference

KW - similarity

UR - https://www.scopus.com/pages/publications/85151970465

U2 - 10.1109/ASONAM55673.2022.10068708

DO - 10.1109/ASONAM55673.2022.10068708

M3 - Conference contribution

T3 - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

SP - 181

EP - 185

BT - Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

A2 - An, Jisun

A2 - Charalampos, Chelmis

A2 - Magdy, Walid

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022

Y2 - 10 November 2022 through 13 November 2022

ER -

Rahman KS, Chelmis C. Learning to Predict Transitions within the Homelessness System from Network Trajectories. In An J, Charalampos C, Magdy W, editors, Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 181-185. (Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022). doi: 10.1109/ASONAM55673.2022.10068708

Learning to Predict Transitions within the Homelessness System from Network Trajectories

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Keywords

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