'Faloutsos' diaporamas de présentation

Understanding and Managing Cascades on Large Graphs

Understanding and Managing Cascades on Large Graphs. B. Aditya Prakash Virginia Tech. Christos Faloutsos Carnegie Mellon University. Sept 28, Tutorial, ECML-PKDD 2012, Bristol. Networks are everywhere!. Facebook Network [2010]. Gene Regulatory Network [ Decourty 2008].

1.74k views • 156 slides

Data Mining Tools

Data Mining Tools. A crash course C. Faloutsos. Subset of:. www.cs.cmu.edu/~christos/TALKS/ SIGMETRICS03-tut/. High-level Outline. [I - Traditional Data Mining tools classification, CART trees; clustering II - Time series: analysis and forecasting ARIMA; Fourier, Wavelets]

1.77k views • 133 slides

Data Mining using Fractals and Power laws

Data Mining using Fractals and Power laws. Christos Faloutsos Carnegie Mellon University. Thank you!. Dr. Xueqi Cheng Dr. Liu Yue. Thanks to. Deepayan Chakrabarti (CMU/Yahoo) Michalis Faloutsos (UCR) George Siganos (UCR). Overview. Goals/ motivation: find patterns in large datasets:

1.41k views • 118 slides

Anomaly Detection and Virus Propagation in Large Graphs

Anomaly Detection and Virus Propagation in Large Graphs. Christos Faloutsos CMU. Thank you!. Dr. Ching-Hao (Eric) Mao Prof. Kenneth Pao. Outline. Part 1: anomaly detection OddBall (anomaly detection) Belief Propagation Conclusions Part 2: influence propagation.

1.44k views • 102 slides

Propagation on Large Networks

1.08k views • 101 slides

An introduction to large sparse graphs

An introduction to large sparse graphs. Fan Chung Graham UC San Diego. Yahoo IM graph Reid Andersen 2005. edge. vertex. A graph G = ( V, E ). Vertices cities. Edges flights. Graph models. _____________________________. Vertices cities people.

1.07k views • 90 slides

Propagation on Large Networks

955 views • 87 slides

Mining Large Graphs: Spectral Methods, Tensors and Influence propagation

1.02k views • 82 slides

Fan Chung Graham University of California, San Diego

New Directions in Graph Theory. for network sciences. Fan Chung Graham University of California, San Diego. A graph G = ( V, E ). edge. vertex. Vertices cities people authors telephones web pages genes. Edges flights pairs of friends coauthorship

986 views • 76 slides

Weighted Graphs and Disconnected Components Patterns and a Generator

778 views • 64 slides

Weighted Graphs and Disconnected Components Patterns and a Generator

837 views • 64 slides

Epidemic Spreading in Real Networks: an Eigenvalue Viewpoint

Epidemic Spreading in Real Networks: an Eigenvalue Viewpoint. Yang Wang Deepayan Chakrabarti Chenxi Wang Christos Faloutsos. Introduction. Computer viruses are a prevalent threat Existing defense mechanisms (eg., scanning) focus on local behaviors only

651 views • 38 slides

Ten Years in the Evolution of the Internet Ecosystem

Ten Years in the Evolution of the Internet Ecosystem. Amogh Dhamdhere Constantine Dovrolis College of Computing Georgia Tech. Motivation. How did the Internet AS ecosystem grow during the last decade? Is growth more important than rewiring?

559 views • 35 slides

Measuring and Analyzing Networks

446 views • 30 slides

Large Graph Mining

399 views • 26 slides

Human-computer interface with Kinect

Human-computer interface with Kinect. by Alexander Marinov. Institute of Information and Communication Technologies. My professional work. My scientific work. Motivation.

292 views • 19 slides

SteerBench: a benchmark suite for evaluating steering behaviors

312 views • 18 slides