Towards Fewer Seeds for Network Discovery
In most machine-learning problems, unlabeled data used in conjunction with a small amount of labeled data, can result in considerable improvement in learning accuracy. The goal of semi-supervised learning is to learn from these initial labeled data to predict class labels accurately. An important optimization question is to select these initial labeled instances efficiently. In this paper, we explore this problem. We propose two algorithms: one based on clustering and another based on random walk on the network. We consider four important criteria for selecting a data point as seed, with the general aim of choosing data points which are a good summary of the network. We show experimental results on different network datasets and show improvements over a recent result.
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