  Network embedding aims to learn a latent, low-dimensional vector
representations of network nodes, effective in supporting various network
analytic tasks. While prior arts on network embedding focus primarily on
preserving network topology structure to learn node representations, recently
proposed attributed network embedding algorithms attempt to integrate rich node
content information with network topological structure for enhancing the
quality of network embedding. In reality, networks often have sparse content,
incomplete node attributes, as well as the discrepancy between node attribute
feature space and network structure space, which severely deteriorates the
performance of existing methods. In this paper, we propose a unified framework
for attributed network embedding-attri2vec-that learns node embeddings by
discovering a latent node attribute subspace via a network structure guided
transformation performed on the original attribute space. The resultant latent
subspace can respect network structure in a more consistent way towards
learning high-quality node representations. We formulate an optimization
problem which is solved by an efficient stochastic gradient descent algorithm,
with linear time complexity to the number of nodes. We investigate a series of
linear and non-linear transformations performed on node attributes and
empirically validate their effectiveness on various types of networks. Another
advantage of attri2vec is its ability to solve out-of-sample problems, where
embeddings of new coming nodes can be inferred from their node attributes
through the learned mapping function. Experiments on various types of networks
confirm that attri2vec is superior to state-of-the-art baselines for node
classification, node clustering, as well as out-of-sample link prediction
tasks. The source code of this paper is available at
https://github.com/daokunzhang/attri2vec.

Social and Information Networks;Machine Learning