Label Propagation on K-partite Graphs with Heterophily

@article{Deng2017LabelPO,
  title={Label Propagation on K-partite Graphs with Heterophily},
  author={Dingxiong Deng and Fan Bai and Yiqi Tang and Shuigeng Zhou and C. Shahabi and L. Zhu},
  journal={ArXiv},
  year={2017},
  volume={abs/1701.06075}
}
  • Dingxiong Deng, Fan Bai, +3 authors L. Zhu
  • Published 2017
  • Computer Science, Mathematics
  • ArXiv
  • In this paper, for the first time, we study label propagation in heterogeneous graphs under heterophily assumption. Homophily label propagation (i.e., two connected nodes share similar labels) in homogeneous graph (with same types of vertices and relations) has been extensively studied before. Unfortunately, real-life networks are heterogeneous, they contain different types of vertices (e.g., users, images, texts) and relations (e.g., friendships, co-tagging) and allow for each node to… CONTINUE READING
    1 Citations
    Are you on the right track? Learning career tracks for job movement analysis
    • 2
    • PDF

    References

    SHOWING 1-10 OF 50 REFERENCES
    Label Propagation on K-partite Graphs
    • C. Ding, Tao Li, Dingding Wang
    • Computer Science
    • 2009 International Conference on Machine Learning and Applications
    • 2009
    • 6
    • Highly Influential
    • PDF
    Joint Inference of Multiple Label Types in Large Networks
    • 28
    • PDF
    Balanced label propagation for partitioning massive graphs
    • 144
    • PDF
    OMNI-Prop: Seamless Node Classification on Arbitrary Label Correlation
    • 14
    Linearized and Single-Pass Belief Propagation
    • 44
    • Highly Influential
    • PDF
    Unsupervised learning on k-partite graphs
    • 149
    • PDF
    Learning latent representations of nodes for classifying in heterogeneous social networks
    • 78
    • PDF
    Large graph mining: patterns, cascades, fraud detection, and algorithms
    • 7
    • PDF
    New Regularized Algorithms for Transductive Learning
    • 216
    • PDF
    Semi-supervised learning using randomized mincuts
    • 242
    • PDF