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Structure Meets Sequences: Predicting Network of Co-evolving Sequences

Published: 15 February 2022 Publication History

Abstract

Co-evolving sequences are ubiquitous in a variety of applications, where different sequences are often inherently inter-connected with each other. We refer to such sequences, together with their inherent connections modeled as a structured network, as network of co-evolving sequences (NoCES). Typical NoCES applications include road traffic monitoring, company revenue prediction, motion capture, etc. To date, it remains a daunting challenge to accurately model NoCES due to the coupling between network structure and sequences. In this paper, we propose to modeling \pname\ with the aim of simultaneously capturing both the dynamics and the interplay between network structure and sequences. Specifically, we propose a joint learning framework to alternatively update the network representations and sequence representations as the sequences evolve over time. A unique feature of our framework lies in that it can deal with the case when there are co-evolving sequences on both network nodes and edges. Experimental evaluations on four real datasets demonstrate that the proposed approach (1) outperforms the existing competitors in terms of prediction accuracy, and (2) scales linearly w.r.t. the sequence length and the network size.

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  • (2023)Learning Joint Relational Co-evolution in Spatial-Temporal Knowledge Graph for SMEs Supply Chain PredictionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599855(4426-4436)Online publication date: 6-Aug-2023

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  1. Structure Meets Sequences: Predicting Network of Co-evolving Sequences

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      cover image ACM Conferences
      WSDM '22: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining
      February 2022
      1690 pages
      ISBN:9781450391320
      DOI:10.1145/3488560
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      Published: 15 February 2022

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      Author Tags

      1. co-evolving sequences
      2. network structure
      3. sequence prediction

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      • (2023)Learning Joint Relational Co-evolution in Spatial-Temporal Knowledge Graph for SMEs Supply Chain PredictionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599855(4426-4436)Online publication date: 6-Aug-2023

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