Hyperparametric Robust and Dynamic Influence Maximization

TL;DR

This paper addresses robust influence maximization in dynamic networks with changing nodes and edges, proposing algorithms with guarantees that optimize influence spread under uncertainty.

Contribution

It introduces a novel approach combining multiplicative weight updates and greedy algorithms for influence maximization in dynamic, uncertain networks.

Findings

Proposed algorithms outperform baselines in influence spread.

Methods are computationally efficient and scalable.

Experimental results validate theoretical guarantees.

Abstract

We study the problem of robust influence maximization in dynamic diffusion networks. In line with recent works, we consider the scenario where the network can undergo insertion and removal of nodes and edges, in discrete time steps, and the influence weights are determined by the features of the corresponding nodes and a global hyperparameter. Given this, our goal is to find, at every time step, the seed set maximizing the worst-case influence spread across all possible values of the hyperparameter. We propose an approximate solution using multiplicative weight updates and a greedy algorithm, with provable quality guarantees. Our experiments validate the effectiveness and efficiency of the proposed methods.