%AWang, Yaojing%AYao, Yuan%AXu, Feng%AZhu, Yada%ATong, Hanghang%D2022%I %K %MOSTI ID: 10332503 %PMedium: X %TStructure Meets Sequences: Predicting Network of Co-evolving Sequences %XCo-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. Country unknown/Code not availablehttps://doi.org/10.1145/3488560.3498411OSTI-MSA