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Title: Structure Meets Sequences: Predicting Network of Co-evolving Sequences
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 in- clude 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 NoCES with the aim of simultaneously capturing both the dynamics and the inter- play between network structure and sequences. Specifically, we propose a joint learning framework to alternatively update the network representations and sequence representations as the se- quences 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) out- performs the existing competitors in terms of prediction accuracy, and (2) scales linearly w.r.t. the sequence length and the network size.  more » « less
Award ID(s):
1939725 1947135 2134079
NSF-PAR ID:
10332503
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
WSDM
Page Range / eLocation ID:
1090 to 1098
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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