Large-scale Graph Analytics: Patterns, Anomalies, and Tools
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  • Large-scale Graph Analytics: Patterns, Anomalies, and Tools

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Sabancı University
Faculty of Engineering and Natural Sciences
CS Seminar
Wednesday, 23 May 2012 at 14:40
FENS L045
Large-scale Graph Analytics: Patterns, Anomalies, and
Tools
Leman Akoglu
Carnegie Mellon University

Given large collections of data, how can we extract useful knowledge from it? How can we
find regularities, anomalies, and events efficiently? With the advance of science and
technology, we are witnessing an explosion in both the amount and complexity of data,
constantly being generated. Today, interpreting this huge volume of data and extracting
descriptive, predictive, and commercially or medically useful information from it is a big
challenge.
In this talk, I will describe how we exploit graph mining techniques for large data analytics
with applications on diverse real graphs ranging from social and information graphs like
Twitter and YouTube to communication graphs. Our contributions follow three main tracks:
(1) graph pattern mining and generators, focusing on identifying regularities in the
formation and evolution of real graphs, and building generative models that can mimic the
properties that real graphs exhibit; (2) graph anomaly detection, exploiting the discovered
patterns as well as various compression techniques to spot irregularities and
discontinuities in complex graphs that grow over time; and (3) scalable graph mining
algorithms, focusing on developing a breadth of fast algorithms and tools that provide the
means for massive graph analytics, including anomaly detection, nearest-neighbor search,
community detection, as well as visualization and sensemaking.

BIO
Leman Akoglu is a Ph.D. candidate in the Computer Science Department at Carnegie
Mellon University, advised by Christos Faloutsos. She received her B.S. at Bilkent
University in 2007. She won 2 best paper awards and published several refereed articles
in major data mining venues. Her research focuses on large-scale data analytics, with an
emphasis on anomaly and event detection in large, time-varying graphs using scalable
algorithms and tools.