Semi-supervised Embedding in Attributed Networks with Outliers

In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive settings while explicitly alleviating noise effects from outliers. Experimental results on various datasets drawn from the web, text and image domains demonstrate the advantages of SEANO over the state-of-the-art methods in semi-supervised classification under transductive as well as inductive settings. We also show that a subset of parameters in SEANO are interpretable as outlier scores and can significantly outperform baseline methods when applied for detecting network outliers. Finally, we present the use of SEANO in a challenging real-world setting - flood mapping of satellite images and show that it is able to outperform modern remote sensing algorithms for this task. 1 Introduction Many applications are modeled and analyzed as attributed networks, where vertices represent entities with attributes and edges express the interactions or relationships between entities. In many scenarios, one also has knowledge about the labels of some vertices in an attributed network. Such networks are referred to as partially labeled attributed networks (PLANs). While PLANs contain much richer information than plain networks, they are also more challenging to analyze.