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CRank: Prioritizing network communities

Author: Marinka Zitnik (

Project website


This repository contains code necessary to run the CRank algorithm. CRank is an automatic unsupervised method for prioritizing network communities and identifying the most promising ones for further experimentation.

CRank can be used with any community detection method and scales to large networks. It uses only information provided by the network structure and does not require any additional external metadata or labels. However, when available, CRank can incorporate domain-specific metadata and labels to further boost performance. See our paper for details on the algorithm.

Usage: Zachary's Karate Club

Zachary's Karate Club network is a well known network, which consists of 34 nodes and 78 edges. It shows the relationships between Zachary Karate Club members. Each node represents a member of the karate club and each edge represents a ties between two members of the club.

A community detection method of user's choice takes as input the network and outputs a grouping of nodes into five communities, Cmt1, Cmt2, Cmt3, Cmt4, and Cmt5, as highlighted in the figure. Each community is given by a set of its member nodes. For example, community Cmt1 contains nodes 5, 6, 7, 11, and 17. Notice that communities Cmt2 and Cmt4 split the friendship network among the Karate Club members into two widely known factions, whereas communities Cmt1, Cmt3 and Cmt5 represent less meaningful groups of the Karate Club members.

We now prioritize the detected communities using CRank. CRank takes as input the Zachary's Karate Club network given by its edge list (switch -i:) and node-community affiliations (switch -c:). As a result, CRank prioritizes the communities by ranking them by their aggregated prioritization score and saves the resulting prioritization to a file (switch -o:).

$ ./crank -i:karate.txt -c:karate_communities.txt -o:karate_prioritization.txt


CRank assigns a score to each community and uses that score to determine the rank of a community in the final prioritization. The resulting prioritization of the five communities detected in the Zachary's network is shown in the figure below.

Communities Cmt4 and Cmt2 are placed at the top of the ranking, indicating that Cmt4 and Cmt2 are most promising communities for follow-up investigation. In contrast. Cmt5 is ranked last, 5 out of 5, suggesting that Cmt5 is the least promising community. Indeed, Cmt4 and Cmt2 correspond to two groups of people into which the karate club split after an argument between two teachers, a fact that is well known in the literature but that was not used for prioritization.

Notice that CRank allows the input to consist of only network and community affiliation data, given by switches -i: and -c:, respectively. As a result, CRank can be used with non-statistical community detection methods.

See the project website for more examples of usage.


If you find CRank useful for your research, please consider citing this paper:

  title     = {Prioritizing Network Communities},
  author    = {Zitnik, Marinka and Sosic, Rok and Leskovec, Jure},
  journal   = {Nature Communications},
  volume    = {9},
  number    = {1},
  pages     = {2544},
  year      = {2018}


Please send any questions you might have about the code and/or the algorithm to

This code implements several different variants, including the option to include user-specific community metrics and labels. Many prioritization variants are possible and what works best might depend on a concrete use case.


CRank code is tested under Mac OS X, Linux and Windows systems.

This is a C++ implementation. To compile the code, do:

$ cd crank
$ make all

CRank relies on SNAP, a general-purpose network analysis and graph mining library.

The code also includes, a simple Python implementation of CRank's rank aggregation algorithm.


Decagon is licensed under the MIT License.