Mining plain and knowledge graphs
Christos Faloutsos, CMU
KGE-KDD 2020
Abstract
Given a large graph, like who-calls-whom, or who-likes-whom,
what behavior is normal and what should be surprising,
possibly due to fraudulent activity? How do graphs evolve over time?
We focus on these topics:
(a) anomaly detection in large static graphs and
(b) patterns and anomalies in knowledge graphs and time-evolving graphs.
For the first, we present a list of patterns,
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
and knowledge graphs,
as tensors, as well as some surprising discoveries such settings.
Bio
here
Material