Influence Maximization over Markovian Graphs: A Stochastic Optimization Approach

TL;DR

This paper develops stochastic optimization algorithms to estimate optimal influence sampling distributions over evolving Markovian graphs, accounting for unknown or noisy transition probabilities, with proven convergence and numerical validation.

Contribution

It introduces novel recursive algorithms for influence maximization on Markovian graphs, handling both known and unknown transition probabilities with convergence guarantees.

Findings

Algorithms converge under specified conditions.

Numerical results demonstrate effectiveness.

Methods adapt to noisy and perfect observations.

Abstract

This paper considers the problem of randomized influence maximization over a Markovian graph process: given a fixed set of nodes whose connectivity graph is evolving as a Markov chain, estimate the probability distribution (over this fixed set of nodes) that samples a node which will initiate the largest information cascade (in expectation). Further, it is assumed that the sampling process affects the evolution of the graph i.e. the sampling distribution and the transition probability matrix are functionally dependent. In this setup, recursive stochastic optimization algorithms are presented to estimate the optimal sampling distribution for two cases: 1) transition probabilities of the graph are unknown but, the graph can be observed perfectly 2) transition probabilities of the graph are known but, the graph is observed in noise. These algorithms consist of a neighborhood size estimation algorithm combined with a variance reduction method, a Bayesian filter and a stochastic gradient algorithm. Convergence of the algorithms are established theoretically and, numerical results are provided to illustrate how the algorithms work.