Signed Graph Representation Learning: A Survey
<|reference_start|>Signed Graph Representation Learning: A Survey: With the prevalence of social media, the connectedness between people has been greatly enhanced. Real-world relations between users on social media are often not limited to expressing positive ties such as friendship, trust, and agreement, but they also reflect negative ties such as enmity, mistrust, and disagreement, which can be well modelled by signed graphs. Signed Graph Representation Learning (SGRL) is an effective approach to analyze the complex patterns in real-world signed graphs with the co-existence of positive and negative links. In recent years, SGRL has witnesses fruitful results. SGRL tries to allocate low-dimensional representations to nodes and edges which could preserve the graph structure, attribute and some collective properties, e.g., balance theory and status theory. To the best of knowledge, there is no survey paper about SGRL up to now. In this paper, we present a broad review of SGRL methods and discuss some future research directions.<|reference_end|>
@article{zhang2024signed,
    title={Signed Graph Representation Learning: A Survey},
    author={Zeyu Zhang, Peiyao Zhao, Xin Li, Jiamou Liu, Xinrui Zhang, Junjie
  Huang and Xiaofeng Zhu},
    journal={arXiv preprint arXiv:2402.15980},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15980},
    primaryClass={cs.SI}
}