Social network model construction module of company image improvement system

Abstract

The invention discloses a social network model construction module of a company image improvement system. The social network model construction module comprises the following five sub-modules: construction of a complex social network user model, construction of an inter-user relationship module, construction of a multi-source heterogeneous complex social network topological graph, identification of key nodes, discovering and dividing of communities, wherein the construction of the complex social network user model comprises user data extraction and user attribute feature definition; the construction of the inter-user relationship module comprises user relationship extraction and potential relationship prediction; and the identification of the key nodes comprises user node importance indexes and event propagation node importance indexes. According to the invention, related data on social media is collected efficiently; the complex social network user model is constructed on the basis ofacquired data; meanwhile, a specific relationship among users is modeled; a one-way edge model among the users is constructed; a complex social network topological structure model is comprehensivelyobtained; and the complex social network topological structure model is taken as an object.

Description

technical field [0001] The invention relates to the field of network science and technology, in particular to a social network model building block of a company image promotion system. Background technique [0002] 1. Construct complex social network user model / multi-source heterogeneous complex social network topology map: generally use supervised learning model to predict it with non-missing data. However, the latent attributes of users are usually ignored, which leads to the problem of decreased prediction accuracy due to insufficient model information. When manually writing rules to model and extract the relationship between users (such as the following relationship between users on Sina Weibo), it is difficult to manually construct rules for complex or potential user relationships. Extract and predict the relationship; [0003] 2. Key node identification: The method based on network topology is generally simple in calculation and low in complexity, but this type of me...

AI Technical Summary

Problems solved by technology

However, the potential attributes of users are usually ignored, which leads to the problem that the prediction accuracy rate decreases due to insufficient model information.

When manually writing rules to model and extract the relationship between users (such as the following relationship between users on Sina Weibo), it is difficult to manually construct rules for complex or potential user relationships. relationship extraction and prediction;

[0003] 2. Key node identification: methods based on network topology are generally simple to calculate and low in complexity, but such methods cannot accurately evaluate the influence of nodes and are not adaptable to different networks.

The community discovery algorithm optimized based on a single evaluation index has great limitations

However, the workload of the network community discovery algorithm based on multi-objective particle swarm optimization will be relatively large.

Images