Networked data, extracted from social media, web pages, and bibliographic databases, can contain entities of multiple classes, interconnected through different types of links. In this paper, we focus on the problem of performing multi-label classification on networked data, where the instances in the network can be assigned multiple labels. In contrast to traditional content-only classification methods, relational learning succeeds in improving classification performance by leveraging the correlation of the labels between linked instances. However, instances in a network can be linked for various causal reasons, hence treating all links in a homogeneous way can limit the performance of relational classifiers. In this paper, we propose a multi-label iterative relational neighbor classifier that employs social context features (SCRN). Our classifier incorporates a class propagation probability distribution obtained from instances' social features, which are in turn extracted from the network topology. This class-propagation probability captures the node's intrinsic likelihood of belonging to each class, and serves as a prior weight for each class when aggregating the neighbors' class labels in the collective inference procedure. Experiments on several real-world datasets demonstrate that our proposed classifier boosts classification performance over common benchmarks on networked multi-label data.
Wang and Sukthankar REF propose a multi-label relational neighbor classification algorithm by incorporating a class propagated probability obtained from edge clustering.
Multi-label relational neighbor classification using social context features
KDD '13
Computer Science