Comparing Data Analysis Packages

A succinct comparison of data analysis packages including R, Matlab, SciPy, Excel, SAS, SPSS and Stata, can be found here. I recently tried Stata, but found its language syntax ugly and awkward.

The myth about the Internet

Walter Willinger et al. recently published a paper in which the scale-free network model of the preferential attachment type for Internet is said to be a myth, as it is based on fundamentally flawed traceout data. Furthermore, they criticize the currently popular data-fitting approach to network science and argue that it should be replaced by the reverse-engineering approach.

Large networks are not modular

A pretty striking finding in the WWW'08 paper from Leskovec etc. is that in nearly every network dataset they examined, there are tight but almost trivial communities at very small scales (up to around 100 nodes), while at larger scales, the best possible communities gradually "blend in" with the rest of the network and thus become less "community-like".

Spectral Graph Partitioning

There are a number of methods in the family of Spectral Graph Partitioning, including the traditional min-cut and various balanced cut criteria (such as ratio-cut, average-cut, normalized-cut and minmax-cut). Each method uses a different objective function and consequently a different definition of partition (cluster) indicator vector. The following two tutorials on Spectral Clustering both contain a good summary of these methods.
[1] Spectral Clustering, ICML 2004 Tutorial by Chris Ding
[2] A Tutorial on Spectral Clustering by Ulrike von Luxburg

SIFT and SURF

Scale-Invariant Feature Transform (or SIFT) is one of the most popular algorithms in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999, and it is now a patent of the University of British Columbia.

The SIFT approach, for image feature generation, takes an image and transforms it into a "large collection of local feature vectors". Each of these feature vectors is invariant to any scaling, rotation or translation of the image. This approach shares many features with neuron responses in primate vision. To aid the extraction of these features the SIFT algorithm applies a 4 stage filtering approach: (1) Scale-Space Extrema Detection (2) Keypoint Localistaion (3) Orientation Assignment (4) Keypoint Descriptor.

Speeded Up Robust Features (SURF) is said to have similar performance to SIFT, while at the same time being faster.