NDLI: Online Matrix Completion for Signed Link Prediction

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Online Matrix Completion for Signed Link Prediction

Content Provider	ACM Digital Library
Author	Shen, Jie Xu, Huan Wang, Jing Li, Ping
Abstract	This work studies the binary matrix completion problem underlying a large body of real-world applications such as signed link prediction and information propagation. That is, each entry of the matrix indicates a binary preference such as "like" or "dislike", "trust" or "distrust". However, the performance of existing matrix completion methods may be hindered owing to three practical challenges: 1) the observed data are with binary label (i.e., not real value); 2) the data are typically sampled non-uniformly (i.e., positive links dominate the negative ones) and 3) a network may have a huge volume of data (i.e., memory and computational issue). In order to remedy these problems, we propose a novel framework which {i} maximizes the resemblance between predicted and observed matrices as well as penalizing the logistic loss to fit the binary data to produce binary estimates; {ii} constrains the matrix max-norm and maximizes the F-score to handle non-uniformness and {iii} presents online optimization technique, hence mitigating the memory cost. Extensive experiments performed on four large-scale datasets with up to hundreds of thousands of users demonstrate the superiority of our framework over the state-of-the-art matrix completion based methods and popular link prediction approaches.
Starting Page	475
Ending Page	484
Page Count	10
File Format	PDF
ISBN	9781450346757
DOI	10.1145/3018661.3018681
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Link prediction Low-rank Online learning Matrix completion
Content Type	Text
Resource Type	Article

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