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algomolbiol

Algorithms for Molecular Biology

faster

Graphs are frequently used to model data. An example is a social network graph: people are represented by vertices and their mutual relationships are encoded in edges. One then can extract useful information about the encoded data by measuring various combinatorial quantities; traditional examples include shortest paths or graph cuts. On the other hand, graph themselves can be viewed as a resistive electrical networks. Electrical measures, and in particular the effective resistances between vertices, often capture information that is not readily available through combinatorial measures. However, computing effective resistances is a challenging computational task especially for very large graphs. In this report we discuss a MATLAB implementation of a near-linear time algorithm for the computation of effective resistance, discovered by Spielman and Srivastava. We explore the trade-offs between running time and approximation quality, and we propose a space-efficient variant of the method.

pkt2flow

A simple utility to classify packets into flows. It's so simple that only one task is aimed to finish. For Deep Packet Inspection or flow classification, it's so common to analyze the feature of one specific flow. I have make the attempt to use made-ready tools like tcpflows, tcpslice, tcpsplit, but all these tools try to either decrease the trace volume (under requirement) or resemble the packets into flow payloads (over requirement). I have not found a simple tool to classify the packets into flows without further processing. This is why this program is born.

pychameleon

CHAMELEON clustering for Python 2.7.

rgarcialebron.github.io

wilneliaac.github.io