Influence maximization method and system for sequential network

A time-series network and influence technology, applied in the field of seed node selection method and system for maximizing influence of time-series network, can solve the problem of not considering the difference of the probability of time-series node propagation, etc.

Pending Publication Date: 2021-09-10
CHANGSHU INSTITUTE OF TECHNOLOGY
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Problems solved by technology

However, the LAIC model does not consider the timing of establishing connections between nodes and the difference in the propagation probability between nodes in different slice networks.

Method used

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  • Influence maximization method and system for sequential network
  • Influence maximization method and system for sequential network
  • Influence maximization method and system for sequential network

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Experimental program
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Embodiment 1

[0060] like figure 1 As shown, a time series network-oriented influence maximization method includes the following steps:

[0061] S01: Divide the number of layers of the sequential network and model the sequential network;

[0062] S02: Calculate the propagation probability between nodes based on the eigenvector centrality of the nodes in the network;

[0063] S03: Define a new time series centrality measure based on the local information, propagation probability and time characteristics of nodes in the time series network to calculate the influence of nodes;

[0064] S04: Build a propagation model, combine heuristic algorithms and greedy algorithms to select seed nodes with maximum influence.

[0065] Influence maximization is to find k users as seed nodes in a large-scale social network, so that information can influence as many other users as possible in the network through these k users under a specific dissemination model.

[0066] In a preferred embodiment, in step S...

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Abstract

The invention discloses an influence maximization method and system for a sequential network, which comprises the following steps of: dividing the number of layers of a sequential network, and modeling the sequential network; calculating the propagation probability between nodes based on the feature vector centrality of the nodes in the network; defining a new time sequence centrality measure based on local information, propagation probability and time features of nodes in the time sequence network so as to calculate the influence of the nodes; constructing a propagation model, and combining a heuristic algorithm and a greedy algorithm to select a seed node with the maximum influence. According to the invention, the time sequence of establishing the relation between nodes is considered, and the propagation probability between nodes is calculated on the basis of the feature vector centrality, so that the difference of the propagation probability between nodes in different slice networks can be reflected; meanwhile, on the basis of the node degree, the actual propagation process of the nodes is considered, and a seed node set is selected based on a greedy strategy; the method and the system have high accuracy and high efficiency.

Description

technical field [0001] The invention relates to a method for maximizing the influence of a time series network, in particular to a method and a system for selecting a seed node for maximizing the influence of a time series network combined with a heuristic and a greedy strategy. Background technique [0002] Online social networks play an important role in people's lives. People can express ideas, share information and influence each other through social networks. Influence maximization seed node selection has important applications in advertising, public opinion monitoring and recommendation systems. For example, some corporate brands choose celebrity users in social networks (Weibo, WeChat, FaceBook, Twitter, etc.) Many potential customers while contributing to the greatest boost in brand influence. [0003] After retrieving domestic and foreign literature, it is found that most of the current methods for selecting seed nodes for maximizing influence are based on static...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06Q50/00
CPCG06F30/27G06Q50/01
Inventor 姜久雷方辉凌坤李盛庆
Owner CHANGSHU INSTITUTE OF TECHNOLOGY
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