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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

TIMERS: Error-Bounded SVD Restart on Dynamic Networks

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Ziwei Zhang

Tsinghua University


Peng Cui

Tsinghua University


Jian Pei

Simon Fraser University


Xiao Wang

Tsinghua University


Wenwu Zhu

Tsinghua University


DOI:

10.1609/aaai.v32i1.11299


Abstract:

Singular Value Decomposition (SVD) is a popular approach in various network applications, such as link prediction and network parameter characterization. Incremental SVD approaches are proposed to process newly changed nodes and edges in dynamic networks. However, incremental SVD approaches suffer from serious error accumulation inevitably due to approximation on incremental updates. SVD restart is an effective approach to reset the aggregated error, but when to restart SVD for dynamic networks is not addressed in literature. In this paper, we propose TIMERS, Theoretically Instructed Maximum-Error-bounded Restart of SVD, a novel approach which optimally sets the restart time in order to reduce error accumulation in time. Specifically, we monitor the margin between reconstruction loss of incremental updates and the minimum loss in SVD model. To reduce the complexity of monitoring, we theoretically develop a lower bound of SVD minimum loss for dynamic networks and use the bound to replace the minimum loss in monitoring. By setting a maximum tolerated error as a threshold, we can trigger SVD restart automatically when the margin exceeds this threshold. We prove that the time complexity of our method is linear with respect to the number of local dynamic changes, and our method is general across different types of dynamic networks. We conduct extensive experiments on several synthetic and real dynamic networks. The experimental results demonstrate that our proposed method significantly outperforms the existing methods by reducing 27% to 42% in terms of the maximum error for dynamic network reconstruction when fixing the number of restarts. Our method reduces the number of restarts by 25% to 50% when fixing the maximum error tolerated.

Topics: AAAI

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HOW TO CITE:

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu TIMERS: Error-Bounded SVD Restart on Dynamic Networks Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu TIMERS: Error-Bounded SVD Restart on Dynamic Networks AAAI 2018, .

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu (2018). TIMERS: Error-Bounded SVD Restart on Dynamic Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu. TIMERS: Error-Bounded SVD Restart on Dynamic Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu. 2018. TIMERS: Error-Bounded SVD Restart on Dynamic Networks. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu. (2018) "TIMERS: Error-Bounded SVD Restart on Dynamic Networks", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu, "TIMERS: Error-Bounded SVD Restart on Dynamic Networks", AAAI, p., 2018.

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu. "TIMERS: Error-Bounded SVD Restart on Dynamic Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu. "TIMERS: Error-Bounded SVD Restart on Dynamic Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Ziwei Zhang||Peng Cui||Jian Pei||Xiao Wang||Wenwu Zhu. TIMERS: Error-Bounded SVD Restart on Dynamic Networks. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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