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Function module detecting method based on node domination capacity similarity

A technology of functional modules and detection methods, applied in character and pattern recognition, special data processing applications, instruments, etc., can solve the problem that similarity measurement methods cannot be effectively applied to directed and sparse networks

Active Publication Date: 2014-09-03
XIDIAN UNIV
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Problems solved by technology

Therefore, the above existing similarity measurement methods cannot be effectively applied to the detection of functional modules in directed and sparse networks.

Method used

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  • Function module detecting method based on node domination capacity similarity
  • Function module detecting method based on node domination capacity similarity
  • Function module detecting method based on node domination capacity similarity

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Embodiment Construction

[0051] The technical solutions and effects of the present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0052] refer to figure 1 , the realization steps of the present invention are as follows:

[0053] Step 1, network data processing.

[0054] (1a) Input network data and model it with a directed graph G(V, L), where V is a set of nodes and L is a set of edges connecting nodes;

[0055] (1b) Set the initial time t=0, use the Hungarian algorithm to calculate the maximum matching M of the directed graph G(V, L) t , the maximum matching M t is a subset of edges in a directed graph G(V, L), satisfying Maximum matching M t Any two edges in do not share the start endpoint and the end endpoint, and the maximum matching M t Potential|M t The value of | is the largest.

[0056] Step 2, based on the maximum matching M t , to construct the control pattern and observation pattern of the network.

[0057] (2a) Use the ...

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Abstract

The invention discloses a function module detecting method based on node domination capacity similarity. The function module detecting method mainly solves the problem that in the prior art, sparse function modules cannot be mined in directed network data effectively. According to the technical scheme, the function module detecting method comprises the steps that directed control relationships between nodes are analyzed on the basis of network maximum matching; a node control region and an observation region are used for depicting the capacity magnitude of a density-unrelated node directional domination network; node function similarity is measured from the aspect of the control process of a domination system, a maximum matching enumeration method based on the Markov random sampling process is provided, and domination capacity similarity is calculated; the domination capacity similarity is applied to clustering analysis of a directed network to find out control-relationship-related sparse function modules through detection. The function module detecting method based on the node domination capacity similarity has the advantage that detection results are not affected by weight noise of data and can provide tool support for discovery of knowledge in directed sparse network data.

Description

technical field [0001] The invention belongs to the field of data mining, in particular to a method for detecting functional modules based on the similarity of node dominance capabilities in directed complex networks, which can be used for knowledge discovery based on big data and the like. Background technique [0002] Usually, in real network data, individuals belonging to the same module have highly similar functions. For example, in social networks, individuals in the same community usually have the same hobbies; in biological networks, proteins in the same complex participate together to complete A specific biological process; within a technology network, the same set of pages often expresses similar topics. When using the method of hierarchical clustering to discover the functional modules in the network, the first step is to calculate the similarity between each node in the network, and then divide the nodes with high similarity into the same module through the aggreg...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06F19/00
CPCG16B40/00G06F18/295
Inventor 高琳王炳波郭杏莉王玙邓岳
Owner XIDIAN UNIV
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