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Combining temporal aspects of dynamic networks with Node2Vec for a more efficient dynamic link prediction

Combining temporal aspects of dynamic networks with Node2Vec for a more efficient dynamic link prediction
Combining temporal aspects of dynamic networks with Node2Vec for a more efficient dynamic link prediction

In many real-life applications it is crucial to be able to, given a collection of link states of a network in a certain time period, accurately predict the link state of the network at a future time. This is known as dynamic link prediction, which compared to its static counterpart is more complex, as capturing the temporal characteristics is a non-trivial task. This explains while still majority of today's research in network representation learning focuses on static setting ignoring temporal information. In this work, we focus on one such case and aim at extending node2vec, representation learning method successfully applied for static link prediction, to a dynamic setup. This extended method is applied and validated on several real-life networks with different properties. Results show that taking into account dynamic aspect outperforms static approach. Additionally, based on the network properties, recommendations are given for the node2vec parameters.

(Dynamic) Link Prediction, Dynamic Networks, Graph Mining, Node/Edge Embedding, Representation Learning
1234-1241
Institute of Electrical and Electronics Engineers Inc.
De Winter, Sam
66ee030c-5b58-4f0f-8b7a-c1e1090d8d99
Decuypere, Tim
2087ce9c-d5d1-4cbb-ab7d-a41051dc4973
Mitrovic, Sandra
106b73e6-56b8-46a4-a0ab-e9f4e3351065
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47
De Winter, Sam
66ee030c-5b58-4f0f-8b7a-c1e1090d8d99
Decuypere, Tim
2087ce9c-d5d1-4cbb-ab7d-a41051dc4973
Mitrovic, Sandra
106b73e6-56b8-46a4-a0ab-e9f4e3351065
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
De Weerdt, Jochen
1eaa177f-03d0-47e5-b8b6-4fb419d49e47

De Winter, Sam, Decuypere, Tim, Mitrovic, Sandra, Baesens, Bart and De Weerdt, Jochen (2018) Combining temporal aspects of dynamic networks with Node2Vec for a more efficient dynamic link prediction. In Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018. Institute of Electrical and Electronics Engineers Inc. pp. 1234-1241 . (doi:10.1109/ASONAM.2018.8508272).

Record type: Conference or Workshop Item (Paper)

Abstract

In many real-life applications it is crucial to be able to, given a collection of link states of a network in a certain time period, accurately predict the link state of the network at a future time. This is known as dynamic link prediction, which compared to its static counterpart is more complex, as capturing the temporal characteristics is a non-trivial task. This explains while still majority of today's research in network representation learning focuses on static setting ignoring temporal information. In this work, we focus on one such case and aim at extending node2vec, representation learning method successfully applied for static link prediction, to a dynamic setup. This extended method is applied and validated on several real-life networks with different properties. Results show that taking into account dynamic aspect outperforms static approach. Additionally, based on the network properties, recommendations are given for the node2vec parameters.

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More information

Published date: 24 October 2018
Venue - Dates: 10th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2018, , Barcelona, Spain, 2018-08-28 - 2018-08-31
Keywords: (Dynamic) Link Prediction, Dynamic Networks, Graph Mining, Node/Edge Embedding, Representation Learning

Identifiers

Local EPrints ID: 426989
URI: http://eprints.soton.ac.uk/id/eprint/426989
PURE UUID: 32855e43-4261-403b-a1de-2fc98029b05d
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 20 Dec 2018 17:30
Last modified: 15 Jun 2022 01:37

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Contributors

Author: Sam De Winter
Author: Tim Decuypere
Author: Sandra Mitrovic
Author: Bart Baesens ORCID iD
Author: Jochen De Weerdt

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