Multi-level Inheritance Influence Calculation and Generalization Based on Knowledge Graph

Abstract

Influence calculation and analysis are widely used in social networks,web page importance evaluation and other fields.There is still a lack of effective and universal solution for the multi-level influence calculation with inheritance chain and time span factors.At the same time,the calculation of maximizing the propagation influence is an NP hard problem,whose approximate algorithm has low accuracy and complicated computation.In order to solve the above problems,this paper proposes a multi-level inheritance influence and generalization algorithm based on knowledge graph to realize the calculation of inheritance influence and inheritance relationship.The algorithm uses the breadth first search hierarchy computing model of knowledge graph,and takes into account the time span constraints to calculate the inheritance influence and inheritance chain.In order to optimize the computational efficiency,the strategy of depth first search and different levels with different weights is further used to only calculate the influence of the top n levels.The above method can not only calculate the inheritance influence and inheritance chain well,but also can be generalized into various communication influence calculation models.On this basis,this paper proposes a local optimal search similarity algorithm to maximize the propagation influence by selecting the nodes with large propagation influence as spare nodes.It achieves competitive results in running speed and the maximum number of propagation nodes.Finally,the effectiveness of the proposed method is verified by a variety of simulation experiments.

Keywords

• calculation of inheritance influence|inheritance chain calculation|knowledge graph|maximization of propagationinfluence