CoreScope: Graph Mining Using k-Core Analysis - Patterns, Anomalies and Algorithms

CoreScope is a set of algorithms based on the empirical patterns related to k-coresxs in real-world graphs. CoreScope consists of the following algorithms:

• Core-A: anomaly detection algorithm based on Mirror Pattern
• Truss-A: anomaly detection algorithm based on Truss Mirror Pattern
• Core-D: streaming algorithm for degeneracy based on Core-Triangle Pattern
• Core-S: influential spreader detection method based on Structured Core Pattern

Datasets

The download links for the datasets used in the paper are here

Building and Running CoreScope

Please see User Guide

Running Demo

For demo, please type 'make'

Reference

If you use this code as part of any published research, please acknowledge the following papers.

@inproceedings{shin2016corescope,
  author    = {Kijung Shin and Tina Eliassi-Rad and Christos Faloutsos},
  title     = {CoreScope: Graph Mining Using k-Core Analysis - Patterns, Anomalies and Algorithms},
  booktitle = {ICDM},
  pages     = {469--478},
  year      = {2016}
}

@article{shin2018pattern,
  author={Kijung Shin and Tina Eliassi-Rad and Christos Faloutsos},
  title={Patterns and Anomalies in k-Cores of Real-World Graphs with Applications},
  journal={Knowledge and Information Systems},
  volume={54},
  number={3},
  pages={677--710},
  year={2018},
  issn={0219-3116},
  doi={10.1007/s10115-017-1077-6},
  publisher={Springer}
}