Mining Large, Dynamic Graphs: Patterns, Cascades, Fraud Detection,
Christos Faloutsos, CMU
Given a large graph, like who-likes-whom, or who-calls-whom, what
behavior is normal and what should be surprising,
possibly due to fraudulent activity?
How do graphs evolve over time?
How does influence/news/viruses propagate, over time?
We focus on three topics: (a) anomaly detection in large static graphs
(b) patterns and anomalies in large time-evolving graphs
and (c) cascades and immunization.
For the first, we present a list of static and temporal laws,
including advances patterns like 'eigenspokes';
we show how to use them to spot suspicious activities,
in on-line buyer-and-seller settings, in FaceBook,
in twitter-like networks.
For the second, we show how to handle time-evolving graphs
as tensors, how to handle large tensors in map-reduce environments,
as well as some discoveries such settings.
For the third, we show that for virus propagation,
a single number is enough to characterize the connectivity
of graph, and thus we show how to do efficient immunization for
almost any type of virus (SIS - no immunity; SIR - lifetime immunity;
We conclude with some open research questions for graph mining.
- Slides of presentation in
Last edited: April 4, 2014, by Christos Faloutsos