Evaluation of joint modeling techniques for node embedding and community detection on graphs
Evaluation of joint modeling techniques for node embedding and community detection on graphs
Novel joint techniques capture both the microscopic context and the mesoscopic structure of networks by leveraging two previously separated fields of research: node representation learning (NRL) and community detection (CD). However, several limitations exist in the literature. First, a comprehensive comparison between these joint NRL-CD techniques is non-existent. Second, baseline techniques, datasets, evaluation metrics, and classification algorithms differ significantly between each method. Thirdly, the literature lacks a synchronized experimental approach, thus rendering comparison between these methods strenuous. To overcome these limitations, we present a uni-fied experimental setup mutually comparing six joint NRL-CD techniques and comparing them with corresponding NRL/CD baselines in three different settings: non-overlapping and over-lapping CD and node classification. Our results show that joint methods underperform on the node classification task but achieve relatively solid results for overlapping community detection. Our research contribution is two-fold: first, we show specific weaknesses of selected joint techniques in different tasks and data sets; and second, we suggest a more thorough experimental setup to benchmark joint techniques with simpler NRL and CD techniques.
Community Detection, Joint Modeling Techniques, Node Representation Learning
403-410
Hiel, Simon
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Nicolaers, Lore
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Vazquez, Carlos Ortega
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Mitrovic, Sandra
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Baesens, Bart
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De Weerdt, Jochen
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Hiel, Simon
bc470da1-b595-49e7-be32-da5eb89b37bd
Nicolaers, Lore
72ed0c16-566b-454d-b3fc-bd4f99247e9e
Vazquez, Carlos Ortega
d85acf2d-9dba-48e5-be1d-d2d372d43ac8
Mitrovic, Sandra
106b73e6-56b8-46a4-a0ab-e9f4e3351065
Baesens, Bart
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De Weerdt, Jochen
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Hiel, Simon, Nicolaers, Lore, Vazquez, Carlos Ortega, Mitrovic, Sandra, Baesens, Bart and De Weerdt, Jochen
(2022)
Evaluation of joint modeling techniques for node embedding and community detection on graphs.
An, Jisun, Charalampos, Chelmis and Magdy, Walid
(eds.)
In Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022.
IEEE.
.
(In Press)
(doi:10.1109/ASONAM55673.2022.10068594).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Novel joint techniques capture both the microscopic context and the mesoscopic structure of networks by leveraging two previously separated fields of research: node representation learning (NRL) and community detection (CD). However, several limitations exist in the literature. First, a comprehensive comparison between these joint NRL-CD techniques is non-existent. Second, baseline techniques, datasets, evaluation metrics, and classification algorithms differ significantly between each method. Thirdly, the literature lacks a synchronized experimental approach, thus rendering comparison between these methods strenuous. To overcome these limitations, we present a uni-fied experimental setup mutually comparing six joint NRL-CD techniques and comparing them with corresponding NRL/CD baselines in three different settings: non-overlapping and over-lapping CD and node classification. Our results show that joint methods underperform on the node classification task but achieve relatively solid results for overlapping community detection. Our research contribution is two-fold: first, we show specific weaknesses of selected joint techniques in different tasks and data sets; and second, we suggest a more thorough experimental setup to benchmark joint techniques with simpler NRL and CD techniques.
Text
Evaluation_of_Joint_Modeling_Techniques_for_Node_Embedding_and_Community_Detection_on_Graphs
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Accepted/In Press date: 10 November 2022
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Publisher Copyright:
© 2022 IEEE.
Venue - Dates:
14th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2022, , Virtual, Online, Turkey, 2022-11-10 - 2022-11-13
Keywords:
Community Detection, Joint Modeling Techniques, Node Representation Learning
Identifiers
Local EPrints ID: 478055
URI: http://eprints.soton.ac.uk/id/eprint/478055
PURE UUID: d21aa512-83ed-456a-b924-c163348c66a5
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Date deposited: 21 Jun 2023 16:40
Last modified: 17 Mar 2024 02:59
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Contributors
Author:
Simon Hiel
Author:
Lore Nicolaers
Author:
Carlos Ortega Vazquez
Author:
Sandra Mitrovic
Author:
Jochen De Weerdt
Editor:
Jisun An
Editor:
Chelmis Charalampos
Editor:
Walid Magdy
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