In many real-world networks, nodes have class labels, attributes, or variables that affect the network's topology. If the topology of the network is known but the labels of the nodes are hidden, we would like to select a small subset of nodes such that, if we knew their labels, we could accurately predict the labels of all the other nodes. We develop an active learning algorithm for this problem which uses information-theoretic techniques to choose which nodes to explore. We test our algorithm on networks from three different domains: a social network, a network of English words that appear adjacently in a novel, and a marine food web. Our algorithm makes no initial assumptions about how the groups connect, and performs well even when faced with quite general types of network structure. In particular, we do not assume that nodes of the same class are more likely to be connected to each other---only that they connect to the rest of the network in similar ways.

本文提出了一种使用信息论技术进行主动学习的算法，旨在根据网络拓扑结构和节点标签的联系，选择最能准确预测其他节点标签的子集节点，以便在不知道节点标签的情况下对所有节点进行标注预测，并在社交网络、英语小说中出现的单词以及海洋食物网络中的三个不同领域的网络上进行了测试。

同配与非同配网络中节点分类的主动学习