Link Discovery with Unlabeled Data
Date Issued
2014
Date
2014
Author(s)
Kuo, Tsung-Ting
Abstract
Many social, academic, biological, geographical, and information systems can be described by networks. Link discovery is a kind of task aiming at identifying hidden links in a social network. However, in some cases, the labels of the links to be discovered is not available. In this dissertation, we investigate such a novel aspect of the link discovery task: the problem of discovering unlabeled links. Specifically, we conduct two studies to predict two kinds of unlabeled links respectively: links that represents unlabeled relationship in heterogeneous networks, and links that represents unlabeled diffusion in homogeneous networks. The main challenge of these tasks are the lack of labeled data, thus prevents the direct exploiting of traditional classification approaches. To address this challenge, we design learning-based frameworks to integrate diverse information and solve the corresponding link discovery problems in the two studies. Also, we conduct experiments on various real-world datasets to evaluate our proposed frameworks. The promising experiment results not only demonstrates the usefulness of the proposed models, but also indicates that discovering links without labeled data is feasible in many practical scenarios.
Subjects
連結發現
連結預測
資料探勘
機器學習
社群網路
機率圖形學習模型
自然語言處理
Type
thesis
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