Homophily, Structure, and Content Augmented Network Representation Learning

@article{Zhang2016HomophilySA,
  title={Homophily, Structure, and Content Augmented Network Representation Learning},
  author={Daokun Zhang and Jie Yin and Xingquan Zhu and Chengqi Zhang},
  journal={2016 IEEE 16th International Conference on Data Mining (ICDM)},
  year={2016},
  pages={609-618}
}
Advances in social networking and communication technologies have witnessed an increasing number of applications where data is not only characterized by rich content information, but also connected with complex relationships representing social roles and dependencies between individuals. To enable knowledge discovery from such networked data, network representation learning (NRL) aims to learn vector representations for network nodes, such that off-the-shelf machine learning algorithms can be… CONTINUE READING
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