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README.md
com-amazon.top5000.cmty.txt
com-amazon.ungraph.txt
community-stats.py
components.py
grade-clusters.py
line-sort.py
mace-to-list.py
mlr-to-snap.py
relabel-graph.py
size-stats.py
triangle-clusters.cpp
weighted-edges.py

README.md

Tectonic: Scalable Motif-Aware Community Detection

This repository contains some software used in Scalable Motif-Aware Graph Clustering. You can find the project web page here.

Authors

Usage

Our code takes as input a graph, and the ground-truth communities, check files com-amazon.ungraph.txt and com-amazon.top5000.cmty.txt to see the input format.

  1. Run from a terminal python relabel-graph.py com-amazon.ungraph.txt com-amazon.top5000.cmty.txt amazon.mace amazon.communities.
  2. Download and compile MACE.
  3. Run ./mace C -l 3 -u 3 amazon.mace amazon.triangles
  4. Run python mace-to-list.py amazon.mace amazon.edges
  5. For some stats, run python community-stats.py amazon.edges amazon.communities amazon.edges.stats and python community-stats.py amazon.triangles amazon.communities amazon.triangles.stats
  6. Compile the file triangle_clusters.cpp g++ -std=c++11 -o triangle_clusters triangle-clusters.cpp
  7. Run ./triangle_clusters amazon.triangles numbers_of_nodes (334858 for amazon) threshold_value > amazon_clusters.txt
  8. Quick heuristic evaluation python grade-clusters.py amazon.communities amazon_clusters.txt output.txt