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Entity Resolution in Dynamic Heterogeneous Networks

Authors:

Shubhranshu Shekhar,

Sriram RavindranAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 662 - 668

https://doi.org/10.1145/3366424.3391264

Published: 20 April 2020 Publication History

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Abstract

Networks evolve continuously over time not only with the addition and deletion of links and nodes but also with changes in the importance of edges. Even though many networks contain this type of temporal weightings, vast majority of research in network representation learning and classification has focused on static snapshots of the graph, while largely ignoring the temporal dynamics. In this work, we describe two approaches for incorporating weighted temporal information into network embedding methods such as Graph Convolutional Networks (GCNs). While the first approach aggregates time-weighted edges and nodes, the second approach uses temporal random walks to find relevant convolution nodes. With experiments on public and proprietary datasets, we demonstrate the effectiveness of the proposed TimeSage for link prediction tasks. By applying these predictions, we show improvements in our task of identifying fraudulent actors on a large e-commerce website selling software as subscriptions.

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Cited By

Yan MFan WWang YXie M(2024)Enriching Relations with Additional Attributes for ERProceedings of the VLDB Endowment10.14778/3681954.368198717:11(3109-3123)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681987
Mo XTang RLiu H(2023)A relation-aware heterogeneous graph convolutional network for relationship predictionInformation Sciences10.1016/j.ins.2022.12.059623(311-323)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.12.059
Wang LShen YChen L(2023)TE-DyGE: Temporal Evolution-Enhanced Dynamic Graph Embedding NetworkDatabase Systems for Advanced Applications10.1007/978-3-031-30675-4_13(183-198)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30675-4_13

Index Terms

Entity Resolution in Dynamic Heterogeneous Networks
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

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Published: 20 April 2020

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WWW '20

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Cited By

Yan MFan WWang YXie M(2024)Enriching Relations with Additional Attributes for ERProceedings of the VLDB Endowment10.14778/3681954.368198717:11(3109-3123)Online publication date: 30-Aug-2024
https://doi.org/10.14778/3681954.3681987
Mo XTang RLiu H(2023)A relation-aware heterogeneous graph convolutional network for relationship predictionInformation Sciences10.1016/j.ins.2022.12.059623(311-323)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.12.059
Wang LShen YChen L(2023)TE-DyGE: Temporal Evolution-Enhanced Dynamic Graph Embedding NetworkDatabase Systems for Advanced Applications10.1007/978-3-031-30675-4_13(183-198)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-30675-4_13
Mekki NBerrabah D(2022)Entity Resolution in graph databases: comparison study2022 3rd International Conference on Embedded & Distributed Systems (EDiS)10.1109/EDiS57230.2022.9996480(111-116)Online publication date: 2-Nov-2022
https://doi.org/10.1109/EDiS57230.2022.9996480

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