@inproceedings{eac32744bf564ea685b236102e871af5,
title = "INEAT: Incomplete network alignment",
abstract = "Network alignment and network completion are two fundamental cornerstones behind many high-impact graph mining applications. The state-of-the-arts have been addressing these tasks in parallel. In this paper, we argue that network alignment and completion are inherently complementary with each other, and hence propose to jointly address them so that the two tasks can benefit from each other. We formulate it from the optimization perspective, and propose an effective algorithm iNEAT to solve it. The proposed method offers two distinctive advantages. First (Alignment accuracy), our method benefits from higher-quality input networks while mitigates the effect of incorrectly inferred links introduced by the completion task itself. Second (Alignment efficiency), thanks to the low-rank structure of the complete networks and alignment matrix, the alignment can be significantly accelerated. The extensive experiments demonstrate the performance of our algorithm.",
keywords = "Incomplete network alignment, Low-rank, Network completion",
author = "Si Zhang and Hanghang Tong and Jie Tang and Jiejun Xu and Wei Fan",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 17th IEEE International Conference on Data Mining, ICDM 2017 ; Conference date: 18-11-2017 Through 21-11-2017",
year = "2017",
month = dec,
day = "15",
doi = "10.1109/ICDM.2017.160",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1189--1194",
editor = "George Karypis and Srinivas Alu and Vijay Raghavan and Xindong Wu and Lucio Miele",
booktitle = "Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017",
address = "United States",
}