Friend relationship mining method combining network topology characteristics and user behavior characteristics

Abstract

The invention discloses a friend relationship mining method combining network topology characteristics and user behavior characteristics. The method comprises the following steps of (1) establishing a friend relationship network diagram and randomly selecting 90% of friend relationship connection edge data as a training set and the rest of 10% as a test set; (2) constructing two weighted undirected graphs of a friend relationship network based on topological similarity; (3) constructing two weighted undirected graphs of a friend relationship network based on user behavior characteristic similarity; and (4) utilizing a community detection algorithm (CNM algorithm) based on weighting modularity to respectively carry out community division on the four weighted undirected graphs, if any two users are classified as one community in a division process of the four communities for at least three or more times, considering that the two users are friends. The method introduces the topology characteristics and the behavior characteristics are introduced into the user friend relationship networks and mines whether the two users are the friends through community division.

Description

technical field [0001] The invention relates to the field of data mining and recommendation systems, in particular to a friend relationship mining method based on social network topology features and user behavior features. Background technique [0002] With the rise of the Internet, especially the mobile Internet, mining valuable information from massive amounts of information and data and presenting it to users has become the core function of major mainstream applications such as e-commerce, social networking, news, audio and video. The recommendation system, especially the personalized recommendation, provides accurate, timely, and even surprising personalized information services for each user. It has great potential value for both users and products that release this function. However, friend prediction has a different sense of surprise for users, because if the product recommends your real friends, and these friends do not exist in your friend list at the beginning, us...

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Problems solved by technology

[0003] In order to overcome the disadvantages that the traditional friendship network in the existing social network can be abstracted as an undirected and unweighted graph, which can only indicate whether there is a friendship relationship between the two, and cannot effectively mine the friendship relationship, the present invention combines network features and The introduction of user behavior characteristics into the traditional friend relationship network not only indicates whether the two are friends, but also indicates the strength of the relationship between the two friends. The similarity between two people is considered from different angles, namely, the topological characteristics of the social network and the user relationship. Behavioral characteristics, use the improved CNM (Clauset-Newman-Moore) algorithm to divide the community, there is a phenomenon of "things of a kind flock together, people are divided into groups" between people, the invention uses the improved CNM in combination with network topology characteristics and user behavior characteristics Algorithm digs out whether two users are friends

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