
<center>
<h3>WWW 2014 <br>
Keynote talk:
<i>Mining Large, Dynamic Graphs: Patterns, Cascades, Fraud Detection,
and Algorithms.
</i> <br>
Christos Faloutsos, CMU <br>
</h3>
</center>
<h3> ABSTRACT </h3>
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. <br>
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.
<br>
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.
<br>
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;
etc)
<br>
We conclude with some open research questions for graph mining.

<h3> FOILS </h3>
<ul>
<li> Slides of presentation in
    <a href="FOILS/faloutsos_www_2014.pdf">pdf</a>
    </li>
</ul>

<hr><i>
Last edited: April 4, 2014, by Christos Faloutsos
</i>