depytrace 0.0.2

High conductance rooted trees in relation graphs.

depytrace

This project provides an algorithm for fast extraction of high-conductance trees (called core traces) rooted on designated graph nodes.

License: Apache Software License
Author: Emmanouil (Manios) Krasanakis
Dependencies: networkx (required) pcst_fast (optional)

Roadmap

:x: Method ensemble.
:x: Application for github project understanding.

Quickstart

Install the library in your environment, for example per pip install depytrace. Then, create a networkx graph, select a root node and run the snippet:

import depytrace as dp

graph, root = ...
trace = dp.Core()(graph, root)
print(dp.conductance(graph, trace))

Features

• Near-linear running time with respect to the number of edges (doubling the edges, approximately doubles running time).
• Provable 1+A factor approximations of maximum conductance, where A tend to be small.
• Extensibility to future breakthroughs on Steiner problems.

Customization

Core trace extraction is actually an NP-complete problem. For this reason, solutions provided by this library are approximate and trade-off tightness for speed.

In particular, solutions are found with an algorithm called trace contraction, which internally relies on iteratively solving an NP-complete problems called ELOD maximization. In turn, the latter can be translated to rooted Steiner tree problems. To accommodate future theoretical breakthroughs, the library allows setting up custom ELOD solvers, where tighter solvers translate to tighter theoretical bounds of the trace contraction algorithm.

The dafault ELOD maximizer is a heuristic written in native Python and is chosen thanks to its cross-platform support. For systems that integrate the gcc compiler (e.g. Linux, Windows with mingw installed) you can also use a maximizer provided by the library that adjusts and depends on the pcst_fast Steiner tree solver. To use the related maximizer, just install the pcst_fast library (if gcc is properly set up, this should be as simple as pip install pcst_fast) and run the snipper:

import depytrace as dp

graph, root = ...
trace = dp.Core(dp.cleverRPCST)(graph, root)
print(dp.conductance(graph, trace))