Embedding Dynamic Attributed Networks by Modeling the Evolution Processes

Zenan Xu, Zijing Ou, Qinliang Su, Jianxing Yu, Xiaojun Quan, ZhenKun Lin


Abstract
Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks. To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks. Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp. Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling. Extensive experiments are conducted on four real-world datasets from three different areas. It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks.
Anthology ID:
2020.coling-main.600
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6809–6819
Language:
URL:
https://aclanthology.org/2020.coling-main.600
DOI:
10.18653/v1/2020.coling-main.600
Bibkey:
Cite (ACL):
Zenan Xu, Zijing Ou, Qinliang Su, Jianxing Yu, Xiaojun Quan, and ZhenKun Lin. 2020. Embedding Dynamic Attributed Networks by Modeling the Evolution Processes. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6809–6819, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
Embedding Dynamic Attributed Networks by Modeling the Evolution Processes (Xu et al., COLING 2020)
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PDF:
https://aclanthology.org/2020.coling-main.600.pdf