Link prediction system in social networks based on meta-heuristic algorithm

Karimzadgan Moghadam, Davood; Ahmadi Shakib, Seyyede Masoume; Sanaei, Mohammad Reza

doi:10.22034/apj.2025.723052

Link prediction system in social networks based on meta-heuristic algorithm

Document Type : Original Research Article

Authors

Davood Karimzadgan Moghadam ¹

Seyyede Masoume Ahmadi Shakib ²

Mohammad Reza Sanaei ²

¹ Associate Professor and Faculty Member of Payam Noor University, Tehran, Iran.

² Department of Management and Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran

10.22034/apj.2025.723052

Abstract

Social networks are primarily represented and analyzed in the form of graphs with a large number of vertices and edges, structured as an adjacency matrix. The edges indicate relationships between individuals and act as connections between the vertices. The structural characteristics of each network are determined by the features of the edges and vertices within it. In this research, conducted on various types of social network data from the Stanford University database, a preprocessing method was employed using a competitive colonial algorithm for feature selection with the highest merit (lowest cost). To evaluate the impact of feature selection on the final output, experiments were conducted both with and without feature selection operations using various algorithms commonly used in this field. Valid metrics such as accuracy, precision, sensitivity, and recall were independently measured on the output results with an average of 10 program executions. The comparison of results between scenarios with and without feature selection showed a significant impact on all metrics of the final outcome. Many features in the datasets were either unused or contained minimal information. Not removing these features not only increased the computational burden but also affected the accuracy of the output results due to time-consuming execution.

Keywords

Link prediction

meta-heuristic algorithms

data preprocessing

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