Original link: tecdat.cn/?p=7285

 

Membership graph model is a generation model that generates networks through community connections. The following diagram depicts an example of a community membership diagram and network (Figure 1).

 

  • Figure 1. Left: Community diagram (circle nodes represent three communities, square nodes represent nodes of the network), right: agM-generated network, community diagram on the left

 

 

When we use a synthetic network that fits the real network, the synthetic network has very similar characteristics to the real network (Figure 2).

 

  • Figure 2. Marginal probability is a function of the number of common community members in an Orkut network.

 

Community testing

 

If the user specified the number of communities to detect, the corresponding number of communities will be found. If the user does not assume a probability, (1 / N ^ 2) is used, where N is the number of nodes in the graph.

 

example

We show some examples of communities detected by the membership graph model and the underlying network.

Figure to create

Examples of how to create and use a directed graph:

G1 = snap.tngraph.new () g1.addNode (1) g1.addNode (5) g1.addNode (32) g1.addedge (1,5) g1.addedge (5,1) G1. AddEdge (5, 32)Copy the code

The code for saving and loading the graph looks like this:

G3 = snap.GenForestFire(1000, 0.35, FOut = snap.tfout ("test.graph") g3.save (FOut) fout.flush () FIn = snap.tfin ("test.graph") G4 = SaveEdgeList(G4, "test.txt", "Save as tab-separated list of edges") G5 = snap.LoadEdgeList(snap.PNGraph, "test.txt", 0, 1)Copy the code

 

 

  • A community in a network of characters in Les Miserables. The edge probability between two nodes that do not share the community is set to 0.01 to detect the more compact community.
  • Communities in the NCAA football team network (best results of 5 trials by setting the edge probability of two nodes that do not share communities to 0.1. The circular area indicates the detected communities, and the node color indicates the NCAA.

 

Download the data

We provide six datasets, each with a network and a set of real communities. Real communities are communities that can be defined and identified from data. The web pages for each dataset describe how we identify the real communities in the dataset.

Data set:

  type Number of nodes The edge community describe
  Undirected, community 3997962 34681189 664414 LiveJournal online social network
  Undirected, community 65608366 1806067135 1620991 Friendster online social network
  Undirected, community 3072441 117185083 15301901 Orkut online social network
  Undirected, community 1134890 2987624 16386 YouTube online social network
  Undirected, community 317080 1049866 13477 DBLP collaboration network
  Undirected, community 334863 925872 271570 Amazon Product Network

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