Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

A real-time tracking method of dynamic network pagerank value

A real-time tracking and dynamic network technology, applied in data exchange network, network data retrieval, network data query, etc., can solve the problems of difficult PageRank tracking, rapid accumulation of errors, and low algorithm efficiency in dynamic networks

Active Publication Date: 2021-08-03
WUHAN UNIV
View PDF6 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

First of all, these algorithms will accumulate errors when incrementally calculating the PageRank value, and the error accumulates quickly, so the PageRank value has to be recalculated after running for a period of time
Secondly, these algorithms require a large amount of storage or complex calculations to incrementally calculate the PageRank value, which makes the efficiency of the algorithm very low, and it is difficult to achieve real-time PageRank tracking for dynamic networks

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A real-time tracking method of dynamic network pagerank value

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0034] Embodiment 1: Add a new edge e(u, w) to the network, where both nodes u and w already exist in the network.

[0035] Step 1. For the initial network, each node in the network is used as the starting point, and the reset probability ∈ is used to simulate R random walks. In the embodiment, ∈=0.15, R=8;

[0036] Step 2, for each random walk path obtained in the previous step, assign a unique id, and store it in a hash table in memory with the id as the key value, so that the access to each random walk path The time is O(1), and the id in the implementation is a positive integer starting from 1, and increments by 1 every time it is generated;

[0037] Step 3, for each node u in the network, save S(u), a set that saves the ids of all random walk paths passing through node u; V(u), a positive integer value, saves all random walk paths The number of visits to node u; the specific operation method is as follows,

[0038] Every time a random walk path is added to the hash tabl...

Embodiment 2

[0044] Embodiment 2: Add a new edge e(u, w) to the network, where node u is a node newly added to the network.

[0045] Step 1. For the initial network, each node in the network is used as the starting point, and the reset probability ∈ is used to simulate R random walks. In the embodiment, ∈=0.15, R=8;

[0046]Step 2, for each random walk path obtained in the previous step, assign a unique id, and store it in a hash table in memory with the id as the key value, so that the access to each random walk path The time is O(1), and the id in the implementation is a positive integer starting from 1, and increments by 1 every time it is generated;

[0047] Step 3, for each node u in the network, save S(u), a set that saves the ids of all random walk paths passing through node u; V(u), a positive integer value, saves all random walk paths The number of visits to node u; the specific operation method is as follows,

[0048] Every time a random walk path is added to the hash table, al...

Embodiment 3

[0055] Embodiment 3: Delete edge e(u, w) from the network, where nodes u and w still exist in the network.

[0056] Step 1. For the initial network, each node in the network is used as the starting point, and the reset probability ∈ is used to simulate R random walks. In the embodiment, ∈=0.15, R=8;

[0057] Step 2, for each random walk path obtained in the previous step, assign a unique id, and store it in a hash table in memory with the id as the key value, so that the access to each random walk path The time is O(1), and the id in the implementation is a positive integer starting from 1, and increments by 1 every time it is generated;

[0058] Step 3, for each node u in the network, save S(u), a set that saves the ids of all random walk paths passing through node u; V(u), a positive integer value, saves all random walk paths The number of visits to node u; the specific operation method is as follows,

[0059] Every time a random walk path is added to the hash table, all n...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a real-time tracking method for the PageRank value of a dynamic network. For the situation that the random walk will visit the nodes and edges in the network again, the present invention introduces a revisit probability model. When updating the PageRank value, first calculate the exact number of random walk paths that need to be adjusted according to the revisit probability model, then delete and re-simulate the corresponding number of random walk paths from the saved historical paths to obtain the updated PageRank value . Every time a new edge is added or removed from the network, the existing PageRank value is incrementally updated in real time, so as to achieve the purpose of tracking the PageRank value of the dynamic network. The method proposed by the invention is suitable for large-scale dynamic networks, significantly improves the tracking efficiency of the PageRank value of the dynamic network, and does not accumulate errors in the long-term tracking process.

Description

technical field [0001] The invention relates to the field of node influence calculation in network science and technology, and is suitable for real-time tracking of PageRank values ​​of large-scale dynamic networks. Background technique [0002] Networks are a powerful representation of collections of objects and the relationships between objects. With the rapid development of the Internet, the World Wide Web, and social networks, the research and analysis of network data has become more and more important. Among them, the concept of centrality, especially the PageRank centrality measure, has also received great attention in the field of network research. In 1998, Google's search engine first used the PageRank algorithm to rank web pages on the World Wide Web, thereby providing users with better search results. The PageRank algorithm measures the popularity of a page only from the topological network structure of the World Wide Web, without considering the specific content...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): H04L12/24G06F16/9538G06F16/953
CPCG06F16/953G06F16/9538H04L41/14
Inventor 胡瑞敏詹泽行潘翔李登实胡文怡王晓晨
Owner WUHAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com