Network negative information impact minimization method based on topic model

A technology of negative information and topic model, applied in the field of network technology and information, it can solve problems such as not considering network processing

Active Publication Date: 2016-04-06
INST OF INFORMATION ENG CAS
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  • Network negative information impact minimization method based on topic model
  • Network negative information impact minimization method based on topic model
  • Network negative information impact minimization method based on topic model

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[0024] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described below through specific embodiments and accompanying drawings.

[0025] The object of the present invention is to cut a certain number of nodes for an already infected social network from the perspective of the topic model, so as to minimize the final infection area. In more detail, when the infection starts from a part of the initial nodes and spreads under a widely used basic probability model - IC model (Independent Cascade, independent cascade model), we consider finding a set containing k points, when these k points are removed, the expected infection area will be the smallest, and k is a given positive integer. We call this combinatorial optimization problem the negative impact minimization problem. For this problem, we propose a topic model-based influence minimization method to efficiently find an o...

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Abstract

The invention relates to a network negative information impact minimization method based on a topic model. The network negative information impact minimization method comprises the following steps: 1) adopting a directed graph to express the propagation of information in a social network, and independently calculating the probability distribution of the negative information and the probability distribution of historical information on each edge through the topic model; 2) independently calculating a distance, i.e., KL (Kullback-Leibler) divergence d(w,i), between the probability distribution of the negative information and the probability distribution of historical information on each edge, wherein d expresses a calculation result of the KL divergence, w expresses the topic distribution of the historical information, and i expresses the topic distribution of the negative information; 3) calculating tb(w)=b(w)/d(w,i) and to(w)=o(w)/d(w,i), wherein b(w) and o(w) are independently the calculation results of a centrality algorithm and an out-degree in-degree algorithm and are sorted from big to small, and the first k nodes are removed to minimize the propagation range of the negative information. The method disclosed by the invention can effectively control the social network of which the malicious information is burst, and greatly reduces an impact range of the negative information.

Description

technical field [0001] The invention belongs to the fields of network technology and information technology, and in particular relates to a method for minimizing the influence of negative network information based on a topic model. Background technique [0002] Over the past few decades, online social networks have provided convenient platforms for information dissemination and marketing activities, allowing ideas and behaviors to flow to each other in cascades of social relationships. Judging from the functions of social networks, it can not only spread positive news, such as: innovative ideas, hot topics, etc., but also spread negative news, such as: malicious rumors, false information, etc. Taking rumors as an example, even if there are only a few infected people at the beginning, the number of infected people will eventually be large due to the triggering of a series of cascading structures in the network. Therefore, how to design an effective method to reduce the scope...

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Application Information

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IPC IPC(8): G06F17/30G06Q50/00
CPCG06F16/9535G06Q50/01
Inventor 姚启鹏周川张鹏胡玥郭莉
Owner INST OF INFORMATION ENG CAS
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