Social network influence maximization method for user behaviors and psychology
A social network and psychological technology, applied in biological models, instruments, computing models, etc., can solve problems such as the inability to apply large-scale social networks, increased unreliability of results, and low algorithm efficiency, so as to speed up the convergence speed, The effect of shortening the gap and improving the accuracy of the algorithm
Pending Publication Date: 2020-02-07
HARBIN ENG UNIV
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Therefore, such algorithms are inefficient and cannot be applied to large-scale social networks
Although the heuristic method is better than the greedy algorithm in efficiency, the time complexity is still high
Secondly, this type of method does not well combine the advanced results of the influence measurement method, and still uses an older measurement method. The resolution problem and the influence overlap problem have not been well handled, which increases the unreliability of the results.
[0005] Therefore, how to quickly discover scattered users with strong spreading ability and maximize influence while ensuring accuracy is still a difficult and challenging task.
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[0052] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing:
[0053] Such as figure 1 , a social network influence maximization method based on user behavior and psychology, including the following steps:
[0054] Step 1: Input social network G=(N,E), particle swarm size n, seed node size k, maximum iteration number g max , inertia weight w, learning factors c1, c2 and user behavior data, where N is the node set of the network, and E is the edge set;
[0055] Step 2: Use the last two user behavior data to calculate the activity time interval of each user, identify the set ST of inactive users, and those whose activity time interval is greater than t days are inactive users;
[0056] Step 3: Use the heuristic algorithm based on the IC sorting method, set the sampling space as N-ST, and initialize the particle swarm;
[0057] Step 4: Construct a target optimization function based on the second-degree theory...
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The invention discloses a social network influence maximization method for user behaviors and psychology, and belongs to the field of data mining and the field of social network science. According tothe method, in a social network, a heuristic algorithm based on an IC method is firstly adopted, an inactive user group is recognized through user activity time and deleted from a sampling space, andinitialization of a particle swarm is completed. The difference between the initial state and the seed user set can be shortened, algorithm convergence is accelerated, and the accuracy is improved. Atarget optimization function is constructed according to a second-degree theory and cun-footage effect. The influence of the users is estimated, the influence overlapping influence between the candidate users is reduced, and the accuracy is improved. Finally, a local optimization algorithm is created in combination with an objective function and an IC method, and speed, position and accelerationof the particle swarm are converged to obtain a seed user set, thereby realizing influence maximization.
Description
technical field [0001] The invention belongs to the fields of data mining and social network science, and specifically relates to a method for maximizing social network influence of user behavior and psychology. Background technique [0002] With the rise and development of social networking platforms, people browse information, express opinions, and spread new ideas and ideas all the time. Influential users can promote the dissemination of information through their own actions. [0003] Influence maximization is to identify a group of influential users to maximize the dissemination of information as much as possible. Good guidance and control of this type of users is of great significance to viral marketing and advertisement release. Theoretical research on the problem could also help control infectious disease outbreaks and prevent power grid breakdowns and internet failures. [0004] Existing methods mainly study the problem of influence maximization from two aspects. ...
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CPCG06N3/006
Inventor 杨静张薇
Owner HARBIN ENG UNIV