#python scirpts ##Utilities
- util_helper.py, util codes is put in this file
- yche_exp.py, some experimental codes is put in this file
##Demo Codes:
| content | detail |
|---|---|
| gexpmq.py | A demonstration of the Gauss-Seidel coordinate relaxation scheme |
| hr_relax.py | A demonstration of a relaxation method for computing a heat-kernel based community |
##Heat Kernel Growth(With Relax)
-
Code: algo_hr_grow.py, code is refactored by Yulin CHE to make it more easier to understand
-
Authors:
by Kyle Kloster and David F. Gleich supported by NSF award CCF-1149756.
- Description:
This demo shows our algorithm running on the Twitter graph. Our other codes are available from David's website:
https://www.cs.purdue.edu/homes/dgleich/codes/hkgrow/
To use this demo, you need pylibbvg graph by David Gleich, Wei-Yen Day, and Yongyang Yu (heavily based on webgraph by Sebastiano Vigna -- he did all the fundamental work!) as well as the symmetrized version of the twitter graph.
If you are on a mac or linux, run
pip install pylibbvg wget https://www.cs.purdue.edu/homes/dgleich/data/twitter-2010-symm.graph wget https://www.cs.purdue.edu/homes/dgleich/data/twitter-2010-symm.offsets wget https://www.cs.purdue.edu/homes/dgleich/data/twitter-2010-symm.properties
Then, as long as these files are in your directory, our code will run!
You need about 4-5GB of space and memory to run this demo.
##Personalized Page Rank
-
Code: algo_ppr.py, code is refactored by Yulin CHE to make it more easier to understand
-
Bug, should update first
for neighbor_v in self.graph[v]:
if neighbor_v not in r_dict:
r_dict[neighbor_v] = 0.
if r_dict[neighbor_v] < len(self.graph[neighbor_v]) * self.tolerance <= r_dict[neighbor_v] + mass:
task_queue.append(neighbor_v)
r_dict[neighbor_v] += mass