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Group discovery method based on path backtracking graph embedding

A discovery method and graph embedding technology, applied in special data processing applications, other database retrieval, other database indexing, etc., can solve the problems of high computational time complexity and difficult application, and meet the requirements of simple data source, strong applicability, achieve simple effects

Inactive Publication Date: 2019-10-18
XI AN JIAOTONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, when mining and discovering groups in the network through the graph neural network method, not only the computational time complexity is high, but also knowledge other than graph topology information is required, such as node names, gender and other attributes, making this method difficult to use in practical situations. application

Method used

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  • Group discovery method based on path backtracking graph embedding
  • Group discovery method based on path backtracking graph embedding
  • Group discovery method based on path backtracking graph embedding

Examples

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Embodiment 1

[0071] see figure 2 , an artificially constructed complex network is represented by a topological graph G=(V, E), where V represents the node set of the topological graph, and E represents the set of topological graph edges. The artificially constructed complex network has 7 nodes and 8 edges, and the network graph has an adjacency matrix with a size of 7×7. A random walk with a length of 49 steps is performed on the topological map, and the process is as follows:

[0072] Step 1-1: Start to select a node with equal probability among all nodes {1, 2, 3, 4, 5, 6, 7} in the topological graph G as the starting point of the random walk, where each point The probability of being selected is 1 / 7. After random selection, the starting node of the random walk is 1, and the node sequence V is traversed. S ={1};

[0073] Step 1-2: Take 1 as the starting node, select the target node in its neighbor node [2, 3] with medium probability, the probability of each neighbor node being select...

Embodiment 2

[0093] see Figure 4 , Zachary Karate club real member relationship diagram, adopt the method of the present invention to process Zacharykarate club relationship diagram, the processing result sees Figure 5 , the triangle shape in the figure represents Figure 4 The light gray nodes in , and the round nodes represent Figure 4 The dark gray nodes in . The nodes of the two shapes can be divided into two types through clustering, and the interface is as follows Figure 5 As shown by the straight line in the middle, the group discovery results are in good agreement with the real situation.

[0094] In the process of the whole group discovery, the present invention only needs to know the network topology information, and does not need any other information, so that the algorithm has strong universality; The implementation of the method is simple, the complexity is low, and there is no need for huge computing overhead; the node representation method generated by graph embeddin...

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Abstract

The invention discloses a group discovery method based on path backtracking graph embedding, which comprises the following steps: establishing a topological graph for representing a network, selectinga node from the topological graph as an initial node, carrying out random walk to obtain a traversal node sequence, and sequentially cutting off the traversal node sequence into a plurality of traversal node sub-sequences with preset lengths; traversing nodes in the node subsequence according to each traversal node; counting backtracking to obtain the frequency of occurrence of each edge among the nodes to serve as the weight value of the edge, obtaining an edge weight matrix, expressing the nodes by adopting a randomly constructed graph embedding vector, optimizing the graph embedding vectorthrough the edge weight matrix, obtaining a graph embedding expression vector, and performing dimensionality reduction and clustering to form nodes contained in each category, namely the same group.The method has the characteristics of low calculation complexity and simple required data source, and can effectively reduce the calculation resource overhead in group discovery. The method does not need any priori knowledge, completely depends on a network topology structure, and is high in applicability to a real complex network.

Description

technical field [0001] The invention belongs to the field of data mining, and relates to a group discovery method based on path backtracking graph embedding. Background technique [0002] In the field of data mining, topological network is a very important processing object, which is composed of a large number of nodes and the connection relationship between nodes. In real life, data in many fields is stored in such a graph network structure, such as social networks (text social networks and image social networks, etc.), industrial networks (electricity and industrial interconnection equipment, etc.), biological networks (protein structure Wait). Due to its non-Euclidean structure, the topological graph contains very rich information, and it also makes it more complicated to perform information extraction and other operations on this type of object. An important feature of a topological graph network is the group structure presented in the network. A large number of empir...

Claims

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Application Information

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IPC IPC(8): G06F16/901G06F16/906
CPCG06F16/9024G06F16/906
Inventor 沈超李其睿刘晓明刘笑子管晓宏
Owner XI AN JIAOTONG UNIV
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