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A network characterization method based on cross-double-layer network random walk

A random walk, two-layer network technology, applied in data exchange networks, digital transmission systems, electrical components, etc., can solve the problems of not being able to characterize adjacency and structural similarity at the same time, and not being able to handle non-connected networks well. achieve effective representation

Active Publication Date: 2019-05-10
HANGZHOU DIANZI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The technical problem to be solved by the present invention is: the current network characterization technology cannot characterize adjacency and structural similarity at the same time, and cannot handle disconnected networks well

Method used

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  • A network characterization method based on cross-double-layer network random walk
  • A network characterization method based on cross-double-layer network random walk
  • A network characterization method based on cross-double-layer network random walk

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Embodiment 1

[0026] A network representation method based on random walks across two-layer networks, such as figure 1 As shown, it is a block diagram of a network characterization method in Embodiment 1. This embodiment includes the following steps: A) Establish a network topology structure according to the relationship between entities in the real system, and obtain the network adjacency matrix W={w ij}, i, j∈[1, n], n is the number of nodes in the network topology; B) Obtain the role sequence of the nodes in the induced subgraph whose size does not exceed a given value k, record it as a representation vector, and establish the relationship between nodes The role similarity matrix S={s ij}, i, j∈[1,n]; C) According to the one-to-one correspondence between the nodes in the network neighbor matrix W and the role similarity matrix, establish a two-layer hybrid network; D) start from each node in turn, and perform h times Random walk across the double-layer mixed network, a total of h groups...

Embodiment 2

[0049] In this embodiment, the word skipping model is used for the two-layer hybrid network obtained in step C of the embodiment, and node features are extracted to form a node representation, and then form a network representation. The skip-gram model realizes node representation. The process is to predict words that may co-occur with it by giving a central word and training it through a simple neural network with only one hidden layer. In this embodiment, a central node, which is the starting point of the random walk, is used to predict the probability of another node that may appear in its random walk sampling sequence. If the probability of two nodes appearing in the same random walk sampling sequence at the same time is higher, the role similarity of the two nodes is higher.

[0050] For any sampling sequence L i , given a central node i to generate a background node i k The conditional probability of can be obtained by performing a softmax operation on the vector inner...

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Abstract

The invention relates to the technical field of network characterization, in particular to a network characterization method based on cross-double-layer network random walk, which comprises the following steps of: A) establishing a network topological structure; B) obtaining a role similarity matrix; C) establishing a double-layer hybrid network; D) obtaining a random walk sequence; And E) obtaining the representation of the network by using the continuous bag-of-words model. The method for establishing the role similarity matrix S comprises the following steps of: B1) listing all subgraphs with sizes smaller than or equal to a given size k; B2) enumerating a non-isomorphic track, and recording the number as m; B3) expressing the condition that each node participates in m roles by using avector with the length of m; And B4) taking the similarity of the role representation vectors of every two nodes as the similarity of the two nodes and a role similarity matrix S. The method has the advantages that by means of the random walk model and the continuous bag-of-words model, representation of network adjacency and structural similarity is fused at the same time, and effective representation of non-connected network nodes with similar roles can be achieved.

Description

technical field [0001] The invention relates to the technical field of network characterization, in particular to a network characterization method based on random walk across a double-layer network. Background technique [0002] In the era of big data, not only the scale of data has exploded over time, but also the forms of data are diversified, and there are complex correlations between data. The imbalance between computing power and data supply required to analyze linked big data makes the processing of linked big data face severe challenges. "Network" has become the most natural and direct way to express linked data because of its powerful and flexible representation capabilities. Due to the high-dimensional nature of the network, when the network scale is large, the traditional representation method based on network topology usually inevitably has problems such as high computational complexity and inability to effectively perform parallel operations, resulting in long ...

Claims

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Application Information

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IPC IPC(8): H04L12/24
Inventor 史本云钟佳楠邱洪君韩腾海张新波
Owner HANGZHOU DIANZI UNIV
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