Most existing network representation learning (NRL) methods are designed for homogeneous network, which only consider topological properties of networks. However, in real-world networks, text or categorical attributes are usually associated with nodes, providing another description for networks in a different perspective. In this paper, we present a joint learning approach which learns the representations of nodes and attributes in the same low-dimensional vector space simultaneously. Particularly, we show that more discriminative node representations can be acquired by leveraging attribute features. The experiments conducted on three social-attribute network datasets demonstrate that our model outperforms several state-of-the-art baselines significantly for node classification task and network visualization task.
CITATION STYLE
Chen, W., Wang, J., Jiang, Z., Zhang, Y., & Li, X. (2017). Hierarchical mixed neural network for joint representation learning of social-attribute network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10234 LNAI, pp. 238–250). Springer Verlag. https://doi.org/10.1007/978-3-319-57454-7_19
Mendeley helps you to discover research relevant for your work.