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Online influence maximization method independent of network structure

A technology with maximum influence and network structure, applied in the field of information dissemination, can solve problems such as difficult access to learning seed nodes for structural information, insufficient information utilization, etc., to achieve the effect of reducing influence and difficulty

Inactive Publication Date: 2021-10-29
江苏乐筑网络科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

Among them, it is often necessary to know the exact structure of the entire network or the local structure around the seed nodes in advance. However, structural information is not easy to obtain in reality and focuses on learning the characteristics of the seed nodes, resulting in insufficient information utilization.

Method used

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  • Online influence maximization method independent of network structure
  • Online influence maximization method independent of network structure
  • Online influence maximization method independent of network structure

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Embodiment Construction

[0019] In order to facilitate the understanding of the present invention, the present invention will be understood in connection with the accompanying drawings and examples, and the embodiments described herein are not intended to illustrate and explain the present invention. this invention.

[0020] In the present embodiment, we use the artificial network generated by the NetSCI network and the LFR algorithm. The network structure is shown in Table 1, where NetSCI is a partner network representing the research work of scientists, where node represents scientists, while representing two There is a cooperative relationship between a scientist. The propagation data generation process of each network is as follows: assumes that the effect between each node in the network is the same, during each propagation process, randomly select 15% of the node from the test network as an initial "infection" point, record these initial Infection points, then propagate simulation according to the I...

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Abstract

The invention discloses an online influence maximization method independent of a network structure. The method comprises the following steps: firstly, randomly selecting a seed node from a node set which is not selected as a seed node; using the current seed node for executing an influence propagation process on a real network for b times, and calculating the influence accessibility of the seed node; inferring an activation probability between the nodes according to all historical infection state results; simulating an influence propagation process b times on the inference network, and estimating the influence accessibility of each node which is not selected as a seed node; identifying the first k nodes with the maximum influence according to the obtained influence accessibility by using a greedy algorithm; and finally, if the node with the maximum influence is not selected yet, selecting the node as a new seed node for repetition, otherwise, randomly selecting and repeating the process. Influence reachability estimation can be carried out under the condition that a network structure is unknown, meanwhile, only of seed and non-seed nodes is updated, and a group of k nodes with the most influence are identified more accurately.

Description

Technical field [0001] The present invention belongs to the field of information propagation, and more particularly to an online impact maximization method that does not depend on the network structure. Background technique [0002] Information dissemination is a basic process of social networks, which is one of the basic issues of social network analysis. It is designed to determine a group of most influential K nodes that affect the maximum number of nodes in the network. The impact has wide application in terms of viral marketing, epidemic prevention. [0003] In the study of the impact of maximizing problems, most of them focus on offline impact, that is, the probability of activation between nodes is known in advance, however, in real social networks, this information is not easy, so Activating feedback to network nodes and network interaction is gradually approaching the top of the previous K influential nodes to maximize the most significant approach. Among them, there is ...

Claims

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Application Information

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IPC IPC(8): G06Q50/00G06N5/00
CPCG06Q50/01G06N5/01
Inventor 张风格
Owner 江苏乐筑网络科技有限公司
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