TY - GEN
T1 - Structure meets sequences
T2 - 15th ACM International Conference on Web Search and Data Mining, WSDM 2022
AU - Wang, Yaojing
AU - Yao, Yuan
AU - Xu, Feng
AU - Zhu, Yada
AU - Tong, Hanghang
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China (No. 61690204) and the Collaborative Innovation Center of Novel Software Technology and Industrialization. Hanghang Tong is partially supported by NSF (1947135, 2134079 and 1939725 ). Yuan Yao is the corresponding author.
Publisher Copyright:
© 2022 ACM.
PY - 2022/2/11
Y1 - 2022/2/11
N2 - 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.
AB - 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.
KW - Co-evolving sequences
KW - Network structure
KW - Sequence prediction
UR - http://www.scopus.com/inward/record.url?scp=85125785899&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125785899&partnerID=8YFLogxK
U2 - 10.1145/3488560.3498411
DO - 10.1145/3488560.3498411
M3 - Conference contribution
AN - SCOPUS:85125785899
T3 - WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
SP - 1090
EP - 1098
BT - WSDM 2022 - Proceedings of the 15th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery
Y2 - 21 February 2022 through 25 February 2022
ER -