How to define G=graph(s,t,weights,NodeTable)

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phdcomputer Eng 2018-4-24

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评论： Walter Roberson 2018-5-16

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My method is to create a social network based on a dataset in Matlab. I attached my excel dataset to this question. Each node represents a stakeholder, the edge shows communication among two stakeholders and its weight indicates the total instance of communication between them. I think the following method is appropriate to my subject: G=graph(s,t, weights, NodeTable); How can I define the above variables with this regards that my dataset includes letters? I will be grateful to have any similar examples or related link in this field.

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Walter Roberson 2018-4-24

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在 MATLAB Online 中打开

>> S = {'harold', 'harold', 'lucy'}, T = {'sally', 'maude', 'maude'}
S =
  1×3 cell array
    {'harold'}    {'harold'}    {'lucy'}
T =
  1×3 cell array
    {'sally'}    {'maude'}    {'maude'}
>> G = graph(S,T)
G = 
  graph with properties:
      Edges: [3×1 table]
      Nodes: [4×1 table]
  >> plot(G)

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Walter Roberson 2018-5-7

The first output of unique() is the list of unique values. The third output of unique() is the same length as the input, and for each original input value, gives the index at which the value appears in the list of unique values. So after that unique() call, if you were to do nodenames(nodeidx) then you would have recovered the original input. The alphabetically first name would appear first in the list of unique node names, and every place that nodeidx comes out as 1 is a place in the original input that had that alphabetically-first node name.

nument = height(data) is the same nument = size(data,1) -- it is the number of rows in the table.

numnodes = size(nodenames,1) is the number of unique nodes.

Now, I constructed the input to unique as [data.Assignee; data.Reporter] . That is size(data,1) entries of Assignee followed by the same number of entries of Reporter. So when we look at nodeidx the first size(data,1) entries are related to the Assignee information and the rest are related to the Reporter information.

We can now count the number of times an Assignee was matched with a Reporter by constructing a 2D array in which the first index corresponds to who the item was assigned to, and the second item corresponds to the reporter. When can then go through the input pairs of Assignee and Reporter and add 1 to the corresponding entry in the 2d table, and keep doing that. At the end, the numbers in the table would correspond to the number of times a particular Assignee was associated with a particular Reporter. That in turn is the same as the edge count between the Assignee and Reporter nodes.

This function is implemented by using accumarray(), which is designed for that kind of counting. For the first index (rows) we use the index number into the list of unique names that the Assignee had, and for the second index (columns) we use the index number into the list of unique names that the Reporter had.

Afterwards, adj (the output of accumarray) will be 0 where two nodes did not have the Assignee / Reporter connection, and would be a non-zero count of the number of times that connection occurred in the input otherwise. It is, in other words, and adjacency matrix containing appropriate weights.

We then use the adjacency matrix and the list of node names to create a directed graph.

phdcomputer Eng 2018-5-16

In your opinion which method is better for my graph: installing the community detection toolbox or writing codes for graph clustering?

Walter Roberson 2018-5-16

I do not know anything about that toolbox. Typically using an existing toolbox is easier than writing your own code.

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