Active learning for node classification in assortative and disassortative networks

Authors:
Cristopher Moore;Xiaoran Yan;Yaojia Zhu;Jean-Baptiste Rouquier;Terran Lane
Affiliations:
University of New Mexico, Albuquerque, NM, USA;University of New Mexico, Albuquerque, NM, USA;University of New Mexico, Albuquerque, NM, USA;Rhône-Alpes Complex Systems Institute and ENS Lyon, Lyon, France;University of New Mexico, Albuquerque, NM, USA
Venue:
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2011

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Abstract

In many real-world networks, nodes have class labels 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.