STWalk: Learning Trajectory Representations in Temporal Graphs

Recommended citation: Pandhre, Supriya, et al. "STwalk: learning trajectory representations in temporal graphs." Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. ACM, 2018. https://arxiv.org/pdf/1711.04150.pdf)

STWalk: Learning Trajectory Representations in Temporal Graphs

STWalk analyzes the temporal behavior of nodes in dynamic graphs by doing random walks on graph at a current & past time-steps. STWalk outperforms baseline algorithms on 3 real-world datasets. Implemented in Python, TensorFlow-Keras.

Cite

@inproceedings{pandhre2018stwalk,
  title={STwalk: learning trajectory representations in temporal graphs},
  author={Pandhre, Supriya and Mittal, Himangi and Gupta, Manish and Balasubramanian, Vineeth N},
  booktitle={Proceedings of the ACM India Joint International Conference on Data Science and Management of Data},
  pages={210--219},
  year={2018},
  organization={ACM}
}