Research On Influence Maximization Of Social Networks

With the development of Internet technology,the Internet is flooded in every aspect of people's daily life.People are satisfied with the convenience brought about by the Internet in the information age,and are even more looking forward to using network technology to better enhance the quality of life.The quiet rise of online social networks gradually turns people's social life from offline to online,changing the way people live today and enriching people's lives.A variety of online social tools emerge in an endless stream,further enriching the online social experience of people.In addition to providing social convenience for people,online social networks also have great commercial value.The use of social network marketing is one of them,and the most representative of online marketing is the "word of mouth" marketing model.What leads to the “word of mouth” marketing model is the issue of maximizing the influence of social networks.A large number of scholars have devoted themselves to the study of the issue of maximizing the influence of social networks.The major research tasks of this article include:Using the objectively existing community structure in social networks,the key nodes of social network influence propagation are discovered,and a key node is used to improve an influence maximization algorithm based on a linear threshold model.LDAG algorithm is an efficient heuristic influence maximization algorithm that uses the construction of local directed acyclic graphs for network nodes to compute the propagation of influence in the network.However,the LDAG algorithm constructs a directed acyclic graph for each node in the network,which makes the calculation of the algorithm large.This paper uses the method of constructing a directed acyclic graph for key nodes to ensure the accuracy of the algorithm while simplifying the time complexity of the LDAG algorithm.Finally,the effectiveness of the proposed algorithm is verified by experiments.At the same time,the selection strategies for key nodes of different social networks are discussed in the experiment to further improve the efficiency of the algorithm.This paper proposes an algorithm that uses clustering to improve the efficiency of greedy algorithms.In the linear threshold mode,there are edge-to-weighted edges between social network nodes.In this paper,a new user relationship calculation method is proposed by using the weights of edges between nodes,and this method is used to improve the k-means clustering algorithm.The result of the clustering algorithm uses the greedy algorithm to mine the most influential seed nodes in the social network.The algorithm has greatly improved the time complexity of the algorithm compared to the greedy algorithm by the hybrid clustering algorithm and the greedy algorithm.It is verified by experiment that the algorithm is obviously improved in time and close to the greedy algorithm in accuracy.In this paper,the problem of influence maximization is not confined to the algorithmic innovation of the problem itself.Instead,it adopts a method of merging with other directions of the social network to make targeted improvements to the characteristics of the existing algorithm,making the algorithm calculate the influence.Maximized seed nodes have better results.