# C. Faloutsos

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- Publications
- Influence

On power-law relationships of the Internet topology

- M. Faloutsos, P. Faloutsos, C. Faloutsos
- Computer Science
- SIGCOMM '99
- 30 August 1999

Despite the apparent randomness of the Internet, we discover some surprisingly simple power-laws of the Internet topology. These power-laws hold for three snapshots of the Internet, between November… Expand

Efficient Similarity Search In Sequence Databases

- R. Agrawal, C. Faloutsos, Arun N. Swami
- Mathematics, Computer Science
- FODO
- 13 October 1993

We propose an indexing method for time sequences for processing similarity queries. We use the Discrete Fourier Transform (DFT) to map time sequences to the frequency domain, the crucial observation… Expand

Cost-effective outbreak detection in networks

- J. Leskovec, Andreas Krause, Carlos Guestrin, C. Faloutsos, J. VanBriesen, N. Glance
- Computer Science
- KDD '07
- 12 August 2007

Given a water distribution network, where should we place sensors toquickly detect contaminants? Or, which blogs should we read to avoid missing important stories?.
These seemingly different… Expand

Graph evolution: Densification and shrinking diameters

- J. Leskovec, J. Kleinberg, C. Faloutsos
- Computer Science, Mathematics
- TKDD
- 27 March 2006

How do real graphs evolve over time? What are normal growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties… Expand

Graphs over time: densification laws, shrinking diameters and possible explanations

- J. Leskovec, J. Kleinberg, C. Faloutsos
- Computer Science
- KDD '05
- 21 August 2005

How do real graphs evolve over time? What are "normal" growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying… Expand

Sampling from large graphs

- J. Leskovec, C. Faloutsos
- Computer Science
- KDD '06
- 20 August 2006

Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.), but several of… Expand

R-MAT: A Recursive Model for Graph Mining

- Deepayan Chakrabarti, Y. Zhan, C. Faloutsos
- Computer Science
- SDM
- 2004

How does a ‘normal’ computer (or social) network look like? How can we spot ‘abnormal’ sub-networks in the Internet, or web graph? The answer to such questions is vital for outlier detection… Expand

FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets

- C. Faloutsos, K. Lin
- Computer Science
- SIGMOD '95
- 22 May 1995

A very promising idea for fast searching in traditional and multimedia databases is to map objects into points in k-d space, using k feature-extraction functions, provided by a domain expert [25].… Expand

Kronecker Graphs: An Approach to Modeling Networks

- J. Leskovec, Deepayan Chakrabarti, J. Kleinberg, C. Faloutsos, Zoubin Ghahramani
- Mathematics, Computer Science
- J. Mach. Learn. Res.
- 29 December 2008

How can we generate realistic networks? In addition, how can we do so with a mathematically tractable model that allows for rigorous analysis of network properties? Real networks exhibit a long list… Expand

Fast Random Walk with Restart and Its Applications

- H. Tong, C. Faloutsos, Jia-Yu Pan
- Computer Science
- Sixth International Conference on Data Mining…
- 18 December 2006

How closely related are two nodes in a graph? How to compute this score quickly, on huge, disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance score between two nodes… Expand