Opinion leader mining method based on PageRank

Abstract

The invention provides an opinion leader mining method based on PageRank, belonging to the data mining field. The invention is based on the core idea of the PageRank algorithm, and is introduced intothe social network user influence analysis. The invention not only considers the topological structure information of the social network user relationship, but also fuses some attribute information ofthe social network user which embodies its influence. At that aspect of social network topology information, the algorithm construct a weighted user relationship network base on the forwarding relationship of users in the event, Event is subject-oriented and a large number of users participate in it. Using event-based forwarding mechanism to build relational network not only has the pertinence and goal of information, but also can obviously reduce the use of social media information data, and this kind of network is dynamic because of the different events. For the social network user attribute information, the algorithm uses the user attribute information which can directly reflect the user influence, and can more accurately depict the user influence.

Description

technical field [0001] The invention belongs to the field of data mining, in particular to a method for mining opinion leaders based on PageRank. Background technique [0002] With the rapid development of Internet-based social media, social networks as a reflection and extension of the real world have become an inseparable part of people's daily life. As the main body of social networks, people actively participate in Internet-based social activities and dominate the interaction behavior of the entire social network. People can not only post new things they encounter anytime and anywhere on social platforms, but also Browse hot events in a timely manner, and participate in the discussion of events to express your views and emotions, and you can also find things you are interested in, and communicate and communicate with your friends and followers in a timely manner. [0003] In such a huge user base, users with different backgrounds and composition structures have differen...

AI Technical Summary

Problems solved by technology

[0004] The traditional influence node analysis method relies on the topological structure of the complex network to measure the key nodes. Several commonly used statistical indicators for the key ranking of undirected network nodes are degree centrality, node betweenness centrality, proximity centrality, k-shell, k-core, eigenvector centrality, etc. These indicators for measuring key nodes have a single applicability and a narrow range, and are difficult to apply to new social networks. New social networks are user-centered and between users The various relationships in the network are constructed by edges, and also include text information generated by users, social behaviors between users, etc. Therefore, the traditional measurement method of node importance is difficult to apply, and new key points suitable for new social networks are needed. Algorithms for Node Metrics

Images