Priori knowledge based microblog user group division method

Abstract

The invention relates to a priori knowledge based microblog user group division method. The method specifically comprises the steps of reading social network data; constructing a social network graph taking social network users as nodes and user relationships as edges; constructing a user similarity matrix; when labels of user nodes are initialized, giving the same labels to the nodes with high similarity, and updating the labels of the user nodes by adopting a label propagation algorithm; in a label propagation process, when a plurality of labels with highest frequencies exist in neighbor nodes of the updated nodes, randomly selecting a label with highest frequency to the label of the corresponding node; and after multi-step iterative updating, ensuring that the closely connected nodes have the same specific label values. According to the social network group division method provided by an embodiment of the invention, user groups are divided by improving the label propagation algorithm according to edge clustering coefficient attributes of a user relationship graph, and a division result has relatively high application values for network public opinion monitoring, commercial user mining and the like.

Description

technical field [0001] The invention relates to the technical field of social networks, in particular to a method for dividing microblog user groups based on prior knowledge. Background technique [0002] How to dig out information with practical benefits from social networks has become a research hotspot in complex networks, which is of great significance both in theory and in social practical value. Network communities are usually composed of network nodes with similar functions or properties, and nodes in the same community in complex networks have similar characteristics or similar interests. Microblog is a typical complex network. A community in a microblog network is a collection of users who follow the same topic or have similar interests. By mining the community structure in the microblog network, users with the same or similar hobbies can be quickly and accurately found, and the topics they participate in are discovered. These have good application value in the fie...

AI Technical Summary

Problems solved by technology

Typical representative algorithms include Newman fast algorithm, GN algorithm, etc. The disadvantage is that the algorithm has high complexity and cannot define when to stop

[0004] It can be seen that the above classic algorithms have many limitations, the division results are not ideal, and the complexity is high, it is difficult to meet the requirements of large-scale real network community discovery

In 2007, Raghavan et al. proposed the Label propagation algorithm (Label propagation Algorithm, LPA), which effectively solved the problem of high complexity and unconvergence.

[0005] However, although the label propagation algorithm is simple and efficient, the randomness of the label propagation in the algorithm leads to poor accuracy of the algorithm, unstable division results, strong randomness, and robustness to be improved

In summary, there is a lot of room for improvement in the accuracy and time complexity of existing community discovery methods.

Images