'Christos faloutsos' diaporamas de présentation

Graph Mining - surprising patterns in real graphs

1.76k views • 158 slides

Statistical Models for Web Search Click Log Analysis

1.71k views • 151 slides

15-826: Multimedia Databases and Data Mining

15-826: Multimedia Databases and Data Mining. Lecture #26: Graph mining - patterns Christos Faloutsos. Must-read Material. [Graph minining textbook] Deepayan Chakrabarti and Christos Faloutsos Graph Mining: Laws, Tools and Case Studies , Morgan Claypool, 2012 Part I (patterns).

1.47k views • 126 slides

Mining Billion-Node Graphs - Patterns and Algorithms

Mining Billion-Node Graphs - Patterns and Algorithms. Christos Faloutsos CMU. Thank you!. Henry Kautz Melissa Singkhamsack. Resource. Open source system for mining huge graphs: PEGASUS project (PEta GrAph mining System) www.cs.cmu.edu/~pegasus code and papers. Roadmap.

1.39k views • 118 slides

Mining Large Graphs

Mining Large Graphs. Jure Leskovec and Christos Faloutsos Machine Learning Department. Joint work with: Lada Adamic, Deepay Chakrabarti , Natalie Glance, Carlos Guestrin , Bernardo Huberman , Jon Kleinberg, Andreas Krause, Mary McGlohon , Ajit Singh, and Jeanne VanBriesen.

1.16k views • 100 slides

VoG : Summarizing and Understanding Large Graphs

VoG : Summarizing and Understanding Large Graphs. Danai Koutra Jilles Vreeken U Kang Christos Faloutsos. SDM, 24-26 April 2014, Philadelphia, USA. Problem Definition: Graph Summarization. Given : a graph. a succinct summary with possibly overlapping subgraphs. Find : . ?

723 views • 41 slides

The Connectivity and Fault-Tolerance of the Internet Topology

The Connectivity and Fault-Tolerance of the Internet Topology. Christopher R. Palmer (CMU) crpalmer@cs.cmu.edu Georgos Siganos (UC Riverside) Michalis Faloutsos (UC Riverside) Phillip B. Gibbons (Bell-Labs) Christos Faloutsos (CMU). Understanding the Internet.

395 views • 27 slides

Large Graph Mining

398 views • 26 slides

Fast Subsequence Matching in Time-Series Databases

Fast Subsequence Matching in Time-Series Databases. Author: Christos Faloutsos etc. Speaker: Weijun He. What is the problem?. What is Time Series: 1-dimensional data e.g. Daily stock market price, Daily temperature, etc Our goal:

401 views • 25 slides

Big (graph) data analytics

Big (graph) data analytics. Christos Faloutsos CMU. Outline. Problem definition / Motivation Anomaly detection Time series analysis Conclusions. Motivation. Data mining: ~ find patterns (rules, outliers) How do real graphs look like? Anomalies? Time series / Monitoring.

248 views • 24 slides

EVENT DETECTION IN TIME SERIES OF MOBILE COMMUNICATION GRAPHS

EVENT DETECTION IN TIME SERIES OF MOBILE COMMUNICATION GRAPHS. L eman Akoglu Christos Faloutsos. MOTIVATION. Anomaly and event (change-point) detection, is the building block for many applications:. Cyber warfare Network intrusion Epidemic outbreaks

414 views • 20 slides

Visual Mining of Communities in Complex Networks: Bringing Humans Into the Loop Perceptual Science and Technology REU

Visual Mining of Communities in Complex Networks: Bringing Humans Into the Loop Perceptual Science and Technology REU. Jack Murtagh & Florentina Ferati Faculty Mentors: Dr. James Abello & Dr. Tina Eliassi-Rad Graduate mentor: Monica Babes Vroman Aug 3, 2011. Motivation.

262 views • 11 slides

Duration of phonecalls

137 views • 5 slides