RCELF: A Residual-based Approach for Influence Maximization Problem

TL;DR

This paper introduces RCELF, a residual-based method for the Influence Maximization Problem that outperforms existing approaches in efficiency and result quality, with theoretical guarantees and extensive real-world testing.

Contribution

It proposes a novel residual-based approach (RCELF) that overcomes limitations of current methods, offering improved efficiency and theoretical guarantees for influence maximization.

Findings

RCELF outperforms state-of-the-art methods in experiments

It provides theoretical guarantees for influence spread

Demonstrates high efficiency on real datasets

Abstract

Influence Maximization Problem (IMP) is selecting a seed set of nodes in the social network to spread the influence as widely as possible. It has many applications in multiple domains, e.g., viral marketing is frequently used for new products or activities advertisements. While it is a classic and well-studied problem in computer science, unfortunately, all those proposed techniques are compromising among time efficiency, memory consumption, and result quality. In this paper, we conduct comprehensive experimental studies on the state-of-the-art IMP approximate approaches to reveal the underlying trade-off strategies. Interestingly, we find that even the state-of-the-art approaches are impractical when the propagation probability of the network have been taken into consideration. With the findings of existing approaches, we propose a novel residual-based approach (i.e., RCELF) for IMP, which i) overcomes the deficiencies of existing approximate approaches, and ii) provides theoretical guaranteed results with high efficiency in both time- and space- perspectives. We demonstrate the superiority of our proposal by extensive experimental evaluation on real datasets.