Abstract
Network representation learning (RL) aims
to transform the nodes in a network into low-dimensional vector spaces while
preserving the inherent properties of the network. Though network RL has been intensively
studied, most existing works focus on either network structure or node
attribute information. In this paper, we propose a novel framework, named ANRL,
to incorporate both the network structure and node attribute information in a principled
way. Specifically, we propose a neighbor enhancement autoencoder to model the
node attribute information, which reconstructs its target neighbors instead of
itself. To capture the network structure, attribute-aware skip-gram model is designed
based on the attribute encoder to formulate the correlations between each node and its
direct or indirect neighbors. We conduct extensive experiments on six
real-world networks, including two social networks, two citation networks and
two user behavior networks. The results empirically show that ANRL can achieve
relatively significant gains in node classification and link prediction tasks.
Keywords
- Machine Learning: Neural Networks
- Machine Learning: Unsupervised Learning
- Machine Learning: Deep Learning
- Natural Language Processing: Embeddings
- Machine Learning Applications: Networks
