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Network representation learning method and device based on edge random walk

A random walk and network representation technology, which is applied in the field of random walk network representation learning, can solve problems such as unsuitable representation, lack of semantic information, and inability to adjust network representation.

Active Publication Date: 2021-06-01
BEIJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
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Problems solved by technology

[0004] Early network representation learning was mainly aimed at static homogeneous networks, which can effectively represent the network topology, but lacks detailed semantic information; moreover, the amount of network data is increasing day by day, and the scale and shape of the network change over time. There are significant changes, and the existing network representation learning mainly for static isomorphic networks also lacks time information, and cannot properly adjust the network representation for changes in the network due to the passage of time; therefore, the existing network Representation learning methods cannot interpret the rich content contained in the network, and cannot perform more appropriate and realistic representations for networks that change over time

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  • Network representation learning method and device based on edge random walk
  • Network representation learning method and device based on edge random walk
  • Network representation learning method and device based on edge random walk

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Embodiment Construction

[0057] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0058] Embodiments of the present invention are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals designate the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0059] Those skilled in the art will understand that unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that when an element is referred to as being "connected" or "coupled" to another el...

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Abstract

The invention discloses a network representation learning method and device based on edge random walk. The method includes: calculating the relationship between edges and edges in the network according to the topic vector of each node in the network and the associated time stamp of the edge. Similarity: Calculate the edge-to-edge transition probability according to the calculated edge-to-edge similarity; based on the guidance of the meta-path, perform a random walk according to the calculated transition probability to generate a node sequence; perform node representation according to the obtained node sequence Learning to obtain low-dimensional representations of nodes. The present invention can annotate semantic information and time information to obtain richer network content, thereby helping to mine potential information in the real world more realistically and effectively; and can update the network that changes over time. It is an appropriate and realistic representation.

Description

technical field [0001] The present invention relates to the technical field of network representation learning, in particular to a network representation learning method and device based on edge random walk. Background technique [0002] Many applications in real life can be abstracted as networks, and networks can be represented by graphs. Therefore, most studies use some methods of graph research to help analyze networks, so as to solve the needs and problems of various practical scenarios. Graphs are an important data representation and are widely used in related fields such as computer science and biology. Real-life applications such as social networks, road networks, academic networks, biological protein networks, and communication networks can all be modeled as graphs. By modeling the interaction behavior between entities as a graph, researchers are able to understand various networks in a systematic way. Effective graph analysis can give users a deeper understanding...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/901G06K9/62
Inventor 卢美莲叶丹娜
Owner BEIJING UNIV OF POSTS & TELECOMM
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