Large-scale network disintegration method based on deep reinforcement learning, storage device and storage medium

Abstract

The invention provides a large-scale network disintegration method based on deep reinforcement learning, a storage device and a computer readable storage medium, and the method comprises the followingsteps: training a network representation learning model which is a neural network model mapped from nodes of the network to corresponding feature vectors of the network; training a network disintegration model according to the network representation learning model and a reinforcement learning algorithm, the network disintegration model being a neural network model fitting a reinforcement learningQ value function; and performing network disruption on the target network through the network disruption model. According to the method, a conventional network disintegration problem and a generalized network disintegration problem can be systematically solved. The method has the advantages that the method is simple, the expansibility is high, the required priori knowledge is less, the network disintegration strategy can be efficiently learned only by taking the network as input and defining corresponding reward functions according to different problems, the problem solving scale can be expanded to more than ten thousands of nodes, and the application scene is very wide.

Description

technical field [0001] The present invention relates to the fields of artificial intelligence and network science, in particular to a large-scale network collapse method based on deep reinforcement learning, a storage device and a storage medium. Background technique [0002] Social networks and other various networks are playing an increasingly important role in the fields of social economy and natural science. These interacting individuals and their relationships are collectively referred to as networks or graphs in the field of network science. Network collapse or optimal percolation of complex networks is a classic problem in the field of network science, and has always been the research focus of network science researchers. The goal of this type of problem is to find an optimal node removal sequence from the original network, which can reduce the size of the largest connected piece in the remaining network at the fastest speed with the smallest cost. In general, it can...

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Problems solved by technology

[0014] However, the above-mentioned technologies can only solve the problem of network collapse in a specific scenario. If the scenario is changed (for example, another evaluation index or the problem of collapse with a strike cost), then these technologies will be invalid. Moreover, even For a specific scenario, the solution cannot achieve a good balance between solution efficiency and solution quality

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