Cost-Effective Seed Selection in Online Social Networks

TL;DR

This paper introduces new algorithms for cost-effective seed selection in online social networks, achieving better theoretical guarantees and experimental performance, especially in large-scale networks.

Contribution

It presents the first bi-criteria approximation algorithms with polynomial time and logarithmic bounds for heterogeneous costs, and improved algorithms for uniform costs.

Findings

Algorithms outperform previous methods in cost and speed.

Scales efficiently to billion-scale networks.

Achieves significant improvements in both theoretical bounds and practical performance.

Abstract

We study the min-cost seed selection problem in online social networks, where the goal is to select a set of seed nodes with the minimum total cost such that the expected number of influenced nodes in the network exceeds a predefined threshold. We propose several algorithms that outperform the previous studies both on the theoretical approximation ratios and on the experimental performance. Under the case where the nodes have heterogeneous costs, our algorithms are the first bi- criteria approximation algorithms with polynomial running time and provable logarithmic performance bounds using a general contagion model. Under the case where the users have uniform costs, our algorithms achieve logarithmic approximation ratio and provable time complexity which is smaller than that of existing algorithms in orders of magnitude. We conduct extensive experiments using real social networks. The experimental results show that, our algorithms significantly outperform the existing algorithms both on the total cost and on the running time, and also scale well to billion-scale networks.