  Multi-label network classification is a well-known task that is being used in
a wide variety of web-based and non-web-based domains. It can be formalized as
a multi-relational learning task for predicting nodes labels based on their
relations within the network. In sparse networks, this prediction task can be
very challenging when only implicit feedback information is available such as
in predicting user interests in social networks. Current approaches rely on
learning per-node latent representations by utilizing the network structure,
however, implicit feedback relations are naturally sparse and contain only
positive observed feedbacks which mean that these approaches will treat all
observed relations as equally important. This is not necessarily the case in
real-world scenarios as implicit relations might have semantic weights which
reflect the strength of those relations. If those weights can be approximated,
the models can be trained to differentiate between strong and weak relations.
In this paper, we propose a weighted personalized two-stage multi-relational
matrix factorization model with Bayesian personalized ranking loss for network
classification that utilizes basic transitive node similarity function for
weighting implicit feedback relations. Experiments show that the proposed model
significantly outperforms the state-of-art models on three different real-world
web-based datasets and a biology-based dataset.
