No description, website, or topics provided.
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
HyperCommunityDensity
README.md

README.md

Multi-partite community detection

It discovers overlapping communities in hyper-network or multi-partite networks. A hyper-network is made of hyper-edges, where each hyper-edge contains 3 nodes, and in addition each node is of a different type. For instance, the nodes could be users, named entities and hashtags in Twitter. Each community will contain nodes from all types or partites. The algorithm is a based on a simple community detection method.

how to run it

Compile it with java 1.8, and then run it as: javaClass -parameter -inputHyperNetwork -resultingCommunities The parameter is related to the strictness of the community detection. The higher the number the more compact are the communities. Also, it is possible that some nodes will not be included in any community, they could be considered as noise.

example

Let net be a hyper-network of 6 hyper-edges and three partites (denoted as columns) net=
1 2 3
5 4 3
5 6 7
9 8 3
11 10 7
13 12 7

java javaClass ---6 ---net ---results

results ={ "1":{"1":[1],"9":[1],"11":[2],"5":[1,2],"13":[2]}, "2":{"2":[1],"4":[1],"6":[2],"8":[1],"10":[2],"12":[2]}, "3":{"3":[1],"7":[2]} }

  • 1, 2, 3 denote the three partites
  • the number in the brackets denote the community
  • the number in quotes denotes the node, for instance node 5 belongs to communities 1 and 2