Implementation and data of the experiments in the following paper:
@inproceedings{fakhraei2015collective,
author = {Fakhraei, Shobeir and Foulds, James and Shashanka, Madhusudana and Getoor, Lise},
title = {Collective Spammer Detection in Evolving Multi-Relational Social Networks},
booktitle = {Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
series = {KDD '15},
year = {2015},
isbn = {978-1-4503-3664-2},
location = {Sydney, NSW, Australia},
pages = {1769--1778},
doi = {10.1145/2783258.2788606},
publisher = {ACM},
}
Folders:
- code_psl: PSL code for collective spammer detection.
- code_python: Python code (and iPython notebook versions) for computing graph and sequence features and classification, and exporting data for PSL.
- data: Placeholder for dataset please download the dataset from https://linqs-data.soe.ucsc.edu/public/social_spammer/
- output: Placeholder for the features and predictions.
Dependencies:
- Graphlab Create: https://dato.com/products/create/
- NumPy: http://www.numpy.org/
- scikit-learn: http://scikit-learn.org/stable/
A copy of the paper can be found here:
https://dl.acm.org/citation.cfm?id=2788606 or https://www.cs.umd.edu/~shobeir/papers/fakhraei_kdd_2015.pdf