Learning Parameters for Balanced Index Influence Maximization

TL;DR

This paper introduces a machine learning approach to optimize the parameters of the Balance Index algorithm for influence maximization in social networks, improving the selection of influential nodes.

Contribution

It proposes a supervised learning method to tune BI algorithm parameters based on graph features, enhancing influence maximization performance on real-world networks.

Findings

Machine learning effectively tunes BI parameters for different networks.

The approach outperforms existing heuristics in influence spread.

Parameter tuning improves influence maximization accuracy.

Abstract

Influence maximization is the task of finding the smallest set of nodes whose activation in a social network can trigger an activation cascade that reaches the targeted network coverage, where threshold rules determine the outcome of influence. This problem is NP-hard and it has generated a significant amount of recent research on finding efficient heuristics. We focus on a {\it Balance Index} algorithm that relies on three parameters to tune its performance to the given network structure. We propose using a supervised machine-learning approach for such tuning. We select the most influential graph features for the parameter tuning. Then, using random-walk-based graph-sampling, we create small snapshots from the given synthetic and large-scale real-world networks. Using exhaustive search, we find for these snapshots the high accuracy values of BI parameters to use as a ground truth. Then, we train our machine-learning model on the snapshots and apply this model to the real-word network to find the best BI parameters. We apply these parameters to the sampled real-world network to measure the quality of the sets of initiators found this way. We use various real-world networks to validate our approach against other heuristic.