混合网络中节点分类的主动学习
Active Learning for Node Classification in Assortative and Disassortative Networks
摘要：
在许多现实世界的网络中，节点具有影响网络拓扑的类标签、属性或变量。如果网络的拓扑结构是已知的，但节点的标签是隐藏的，我们希望选择一个小的节点子集，这样，如果我们知道它们的标签，我们就可以准确地预测所有其他节点的标签。我们开发了一个主动学习算法，这个问题使用信息理论的技术来选择哪些节点进行探索。我们测试我们的算法从三个不同的域的网络：社交网络，网络的英语单词出现在小说中相邻，和海洋食物网。我们的算法没有对组如何连接进行初始假设，即使面对非常一般的网络结构类型，也表现良好。特别是，我们不假设同一类的节点更有可能相互连接-只是假设它们以类似的方式连接到网络的其余部分。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
