Active Learning for Node Classification in Assortative and Disassortative Networks

TL;DR

This paper introduces an active learning algorithm for node classification in networks that effectively handles both assortative and disassortative structures without prior assumptions about connection patterns.

Contribution

The authors develop a novel information-theoretic active learning method that adapts to various network structures for accurate node label prediction.

Findings

Performs well on social, linguistic, and ecological networks

No assumptions about class connectivity patterns required

Effective in both assortative and disassortative networks

Abstract

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.