TellTail: Fast Scoring and Detection of Dense Subgraphs


  • Bryan Hooi National University of Singapore
  • Kijung Shin KAIST
  • Hemank Lamba Carnegie Mellon University
  • Christos Faloutsos Carnegie Mellon University



Suppose you visit an e-commerce site, and see that 50 users each reviewed almost all of the same 500 products several times each: would you get suspicious? Similarly, given a Twitter follow graph, how can we design principled measures for identifying surprisingly dense subgraphs? Dense subgraphs often indicate interesting structure, such as network attacks in network traffic graphs. However, most existing dense subgraph measures either do not model normal variation, or model it using an Erdős-Renyi assumption - but this assumption has been discredited decades ago. What is the right assumption then? We propose a novel application of extreme value theory to the dense subgraph problem, which allows us to propose measures and algorithms which evaluate the surprisingness of a subgraph probabilistically, without requiring restrictive assumptions (e.g. Erdős-Renyi). We then improve the practicality of our approach by incorporating empirical observations about dense subgraph patterns in real graphs, and by proposing a fast pruning-based search algorithm. Our approach (a) provides theoretical guarantees of consistency, (b) scales quasi-linearly, and (c) outperforms baselines in synthetic and ground truth settings.




How to Cite

Hooi, B., Shin, K., Lamba, H., & Faloutsos, C. (2020). TellTail: Fast Scoring and Detection of Dense Subgraphs. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4150-4157.



AAAI Technical Track: Machine Learning