Identifying Key Nodes for the Influence Spread using a Machine Learning Approach

TL;DR

This paper presents an enhanced machine learning framework for identifying influential nodes in networks, improving label generation with 'Smart Bins' and enabling prediction of influence and spreading characteristics across diverse network types.

Contribution

It introduces a novel label refinement method using 'Smart Bins' and demonstrates the ability of ML models to predict influence and other spreading features beyond traditional centrality measures.

Findings

'Smart Bins' improve label accuracy for training

ML models can predict influence and spreading characteristics

Framework generalizes well across different network types

Abstract

The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional, centrality-based methods in accuracy and consistency, but this approach still requires further refinement. What information about the influencers can be extracted from the network? How can we precisely obtain the labels required for training? Can these models generalize well? In this paper, we answer these questions by presenting an enhanced machine learning-based framework for the influence spread problem. We focus on identifying key nodes for the Independent Cascade model, which is a popular reference method. Our main contribution is an improved process of obtaining the labels required for training by introducing 'Smart Bins' and proving their advantage over known methods. Next, we show that our methodology allows ML models to not only predict the influence of a given node, but to also determine other characteristics of the spreading process-which is another novelty to the relevant literature. Finally, we extensively test our framework and its ability to generalize beyond complex networks of different types and sizes, gaining important insight into the properties of these methods.