A New Algorithm and Its Application in Detecting Community of the Bipartite Complex Network
Algorithm 1
Detecting community from the bipartite network based on generalized suffix tree.
Input: The matrix of relation of a bipartite graph .
Output: The initial communities.
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Begin
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// Step 1: Get the adjacent node sequences.
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fordo
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Calculate the adjacent node sequences .
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Let be the set of all adjacent node sequences, that is, .
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end for
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Step 2: Integrate the adjacent node sequences into a linked list to facilitate the establishment of a generalized suffix tree.
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Create a node class with two attributes, and .
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fordo
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Convert to .
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end for
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Create a suffix linked list according to the node linked list:
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fordo
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Convert each to .
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end for
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Step 3: Establish a generalized suffix tree according to the linked list of adjacent node sequences.
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Create a class with two attributes, and . Since the number of child nodes of the generalized suffix tree is uncertain, is an array of , which stores all its child nodes.
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node is null node.
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Insert all the linked lists of adjacent node sequence into the generalized suffix tree, the process is:
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fordo
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whiledo
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ifthen
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Insert the linked lists from to the node.
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else
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; .
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end if
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end while
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end for
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Step 4: Get the bipartite cliques through the generalized suffix tree.
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fordo
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if is a leaf node, that is, , or is a branch node, that is, then
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Create the bipartite clique .
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end if
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end for
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// Step 5: Adjust the bipartite cliques.
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// Step 6: Merging the bipartite cliques to form the initial communities.
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while true do
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Calculate the tightnesses of bipartite cliques: .
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ifthen
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Merge the bipartite cliques.
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end if
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end while
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Step 7: Adjust the isolated edges and divide them into communities.
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Taking the isolated edge as bipartite cliques, and calculate the tightness .
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Divide the isolated edges into the communities with the highest tightness.
