A link prediction algorithm based on ant colony optimization

Bolun Chen,Ling Chen

doi:10.1007/s10489-014-0558-5

Abstract

The problem of link prediction has attracted considerable recent attention from various domains such as sociology, anthropology, information science, and computer sciences. In this paper, we propose a link prediction algorithm based on ant colony optimization. By exploiting the swarm intelligence, the algorithm employs artificial ants to travel on a logical graph. Pheromone and heuristic information are assigned in the edges of the logical graph. Each ant chooses its path according to the value of the pheromone and heuristic information on the edges. The paths the ants traveled are evaluated, and the pheromone information on each edge is updated according to the quality of the path it located. The pheromone on each edge is used as the final score of the similarity between the nodes. Experimental results on a number of real networks show that the algorithm improves the prediction accuracy while maintaining low time complexity. We also extend the method to solve the link prediction problem in networks with node attributes, and the extended method also can detect the missing or incomplete attributes of data. Our experimental results show that it can obtain higher quality results on the networks with node attributes than other algorithms.

Highlights

Many social, biological, and information systems in the real world, from the nervous system to the ecosystem, from road traffic to the Internet, from the ant colony structure to human social relations, can be naturally described as networks, where vertices represent entities and links denote relations or interactions between vertices
We focus on the accuracy of the results and the algorithms’ computing time
All experiments have been conducted on Microsoft Windows 7 operating system, and the results are visualized on Matlab 6.0

Summary

Introduction

Biological, and information systems in the real world, from the nervous system to the ecosystem, from road traffic to the Internet, from the ant colony structure to human social relations, can be naturally described as networks, where vertices represent entities and links denote relations or interactions between vertices. Due to the high experimental costs of revealing the hidden interaction relationships in these networks, the results of link prediction can direct the experiment designing so as to reduce the cost and improve the success rate of experiment. Predicting the loss and suspicious links of diseases-gene networks can help to explore the mechanism of diseases, predict and evaluate their treatment. It can find new drug targets and open up new ways for drug development [4]

Objectives

Methods

Results