In this work, we presents three empirical patterns related to k-cores in real-world graphs and their applications to anomaly detection, streaming algorithm design, and influential spreaders identification.
CoreScope: Graph Mining Using k-Core Analysis - Patterns, Anomalies and Algorithms.
IEEE International Conference on Data Mining (ICDM) 2016, Barcelona, Spain
[PDF] [Supplementary Document] [BIBTEX]
Patterns and Anomalies in k-Cores of Real-World Graphs with Applications .
Knowledge and Information Systems
CoreScope v2.0 [Github Repository]
- Core-A: an anomaly detection algorithm based on coreness
- Truss-A: an anomaly detection algorithm based on trussness
- Core-D: a streaming algorithm for degeneracy
- Core-S: an influential spreader detection method based on the structure of degeneracy-cores