Abstract
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.
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Acknowledgments
This work is supported by 973 Program with Grant No. 2014CB340400. Yan Zhang is supported by NSFC with Grant Nos. 61532001 and 61370054, and MOE-RCOE with Grant No. 2016ZD201. We thank the anonymous reviewers for their comments.
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Chen, W., Wang, J., Jiang, Z., Zhang, Y., Li, X. (2017). Hierarchical Mixed Neural Network for Joint Representation Learning of Social-Attribute Network. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_19
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