Design And Implementation Of Information Diffusion Analysis System In Social Network Hotspot

As the media of information diffusion, social network platform contains a great amount of information of users’ action and relationship. These information reflect the personal preference of users which have significant meaning to commercial advertisement, political election and public opinion guidance. At present, the research of this field mainly focus on the user level by studying the user’s feature and content’s feature. Users’ complicated relationships determine they own community characteristic. Therefore, this paper researches the discipline of hotspot diffusion from two aspects which are user relation and community structure. And the specific researches are as follows:Firstly, for the problem of the unstable results of community detection, this paper proposes a community detection algorithm based on the label propagation algorithm. The proposed algorithm adopts the diffusion strategy that the labels of large degree nodes are fixed, and the update strategy is selecting the nodes by their weights. We also add a convergence condition. The experimental results show that the standard deviation of the proposed method is the original 25%. Meanwhile, the modularity compared to the original algorithm is improved by 18%. The stability and performance of the community detection are both improved. And the larger datasets are, and the better result is.Then, for the problem of the users’ influence metrics is too simple, this paper design a method to measure user influence based on the community structure and the Page Rank algorithm. We obtain the users’ global and local influence by analyzing the importance of users and communities in the social network. In this case, we can measure the user influence in a more comprehensive way. And then, we propose a collaborative filtering algorithm based on social network information. Using the users’ relationship and the similarity of users’ label to predict the labels preferred by new users, and recommend the corresponding community to them. The experimental results show that the recommend precision of proposed algorithm is superior to the original algorithm by 30%. Therefore, our recommendation can achieve a higher accuracy.Finally, we design a prototype system to analyze the discipline of hotspot diffusion based on the above. According to researching the data of users and communities in diffusion, the system obtains the community property of diffusion path and the user property of diffusion groups and mining the users and communities which motivate the diffusion. As a result, we can learn the discipline of information diffusion and achieve control and guidance on the use of information diffusion.