[Παλιό] - ομιλίες Χ. Φαλούτσου 19, 20/12/2007 @AUEB
Posted: Sun Dec 16, 2007 1:50 pm
Σας πρoσκαλούμε στις δύο ομιλίες που θα δώσει, τη Τετάρτη 19/12/2007 στις 14:00 (Δεριγνύ 21) και τη Πέμπτη 20/12/2007 στις 16:00 (Αίθουσα Ε606, Κτήριο Ευελπίδων, Map: http://mm.aueb.gr/contact.html ), όπως φαίνεται αναλυτικά πιο κάτω ο Καθ. Χρήστος Φαλούτσος, Carnegie Mellon University στη Σειρά Ομιλιών του Τμήματος Πληροφορικής του Ο.Π.Α.
Η πρώτη ομιλία θα είναι κατάλληλη και για τελειόφοιτους προπτυχιακούς φοιτητές.
Biographical note
Christos Faloutsos is a Professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award in ICDM 2006, eleven "best paper" awards, and several teaching awards. He has served as a member of the executive committee of SIGKDD; he has published over 170 refereed articles, 11 book chapters and one monograph. He holds five patents and he has given over 20 tutorials and 10 invited distinguished lectures.
His research interests include data mining for streams and graphs, fractals, database performance, and indexing for multimedia and bio-informatics data.
===================================================================
Τετάρτη 19/12/2007, 14:00
Αίθουσα Δ21, Πτέρυγα Δεριγνύ, Οικονομικό Πανεπιστήμιο Αθηνών
Data Mining using Fractals and Power Laws
Prof. Christos Faloutsos (CMU)
ABSTRACT
What patterns can we find in a bursty web traffic? On the web or on the internet graph itself? How about the distributions of galaxies in the sky, or the distribution of a company's customers in geographical space? How long should we expect a nearest-neighbor search to take, when there are 100 attributes per patient or customer record? The traditional assumptions (uniformity, independence, Poisson arrivals, Gaussian distributions), often fail miserably. Should we give up trying to find patterns in such settings?
Self-similarity, fractals and power laws are extremely successful in describing real datasets (coast-lines, rivers basins, stock-prices, brain-surfaces, communication-line noise, to name a few). We show some old and new successes, involving modeling of graph topologies (internet, web and social networks); modeling galaxy and video data; dimensionality reduction; and more.
===================================================================
Πέμπτη 20/12/2007 στις 16:00
Αίθουσα Ε606, Κτήριο Ευελπίδων, Οικονομικό Πανεπιστήμιο Αθηνών
Graph Mining: Laws, Generators and Tools
Prof. Christos Faloutsos (CMU)
ABSTRACT:
How do graphs look like? How do they evolve over time? How can we generate realistic-looking graphs? We review some static and temporal 'laws', and we describe the "Kronecker'' graph generator, which naturally matches all of the known properties of real graphs. Moreover, we present tools for discovering anomalies and patterns in two types of graphs, static and time-evolving. For the former, we present the 'CenterPiece' subgraphs (CePS), which expects $q$ query nodes (eg., suspicious people) and finds the node that is best connected to all $q$ of them (eg., the master mind of a criminal group). We also show how to compute CenterPiece subgraphs efficiently. For the time evolving graphs, we present tensor-based methods, and apply them on real data, like the DBLP author-paper dataset, where they are able to find natural research communities, and track their evolution.
Finally, we also briefly mention some results on influence and virus propagation on real graphs.
===================================================================
Η πρώτη ομιλία θα είναι κατάλληλη και για τελειόφοιτους προπτυχιακούς φοιτητές.
Biographical note
Christos Faloutsos is a Professor at Carnegie Mellon University. He has received the Presidential Young Investigator Award by the National Science Foundation (1989), the Research Contributions Award in ICDM 2006, eleven "best paper" awards, and several teaching awards. He has served as a member of the executive committee of SIGKDD; he has published over 170 refereed articles, 11 book chapters and one monograph. He holds five patents and he has given over 20 tutorials and 10 invited distinguished lectures.
His research interests include data mining for streams and graphs, fractals, database performance, and indexing for multimedia and bio-informatics data.
===================================================================
Τετάρτη 19/12/2007, 14:00
Αίθουσα Δ21, Πτέρυγα Δεριγνύ, Οικονομικό Πανεπιστήμιο Αθηνών
Data Mining using Fractals and Power Laws
Prof. Christos Faloutsos (CMU)
ABSTRACT
What patterns can we find in a bursty web traffic? On the web or on the internet graph itself? How about the distributions of galaxies in the sky, or the distribution of a company's customers in geographical space? How long should we expect a nearest-neighbor search to take, when there are 100 attributes per patient or customer record? The traditional assumptions (uniformity, independence, Poisson arrivals, Gaussian distributions), often fail miserably. Should we give up trying to find patterns in such settings?
Self-similarity, fractals and power laws are extremely successful in describing real datasets (coast-lines, rivers basins, stock-prices, brain-surfaces, communication-line noise, to name a few). We show some old and new successes, involving modeling of graph topologies (internet, web and social networks); modeling galaxy and video data; dimensionality reduction; and more.
===================================================================
Πέμπτη 20/12/2007 στις 16:00
Αίθουσα Ε606, Κτήριο Ευελπίδων, Οικονομικό Πανεπιστήμιο Αθηνών
Graph Mining: Laws, Generators and Tools
Prof. Christos Faloutsos (CMU)
ABSTRACT:
How do graphs look like? How do they evolve over time? How can we generate realistic-looking graphs? We review some static and temporal 'laws', and we describe the "Kronecker'' graph generator, which naturally matches all of the known properties of real graphs. Moreover, we present tools for discovering anomalies and patterns in two types of graphs, static and time-evolving. For the former, we present the 'CenterPiece' subgraphs (CePS), which expects $q$ query nodes (eg., suspicious people) and finds the node that is best connected to all $q$ of them (eg., the master mind of a criminal group). We also show how to compute CenterPiece subgraphs efficiently. For the time evolving graphs, we present tensor-based methods, and apply them on real data, like the DBLP author-paper dataset, where they are able to find natural research communities, and track their evolution.
Finally, we also briefly mention some results on influence and virus propagation on real graphs.
===================================================================