Maximizing Activity in Ising Networks via the TAP Approximation

TL;DR

This paper develops approximate gradient algorithms using TAP and Plefka expansions to optimize influence in Ising networks, addressing both continuous and discrete influence settings with theoretical guarantees.

Contribution

It introduces novel TAP-based gradient ascent methods for Ising model influence maximization and provides submodularity conditions for greedy algorithms in the discrete case.

Findings

TAP-based algorithms outperform naive mean field in influence optimization.

Submodularity conditions enable efficient greedy algorithms with guarantees.

Modeling stochastic fluctuations improves influence maximization results.

Abstract

A wide array of complex biological, social, and physical systems have recently been shown to be quantitatively described by Ising models, which lie at the intersection of statistical physics and machine learning. Here, we study the fundamental question of how to optimize the state of a networked Ising system given a budget of external influence. In the continuous setting where one can tune the influence applied to each node, we propose a series of approximate gradient ascent algorithms based on the Plefka expansion, which generalizes the na\"{i}ve mean field and TAP approximations. In the discrete setting where one chooses a small set of influential nodes, the problem is equivalent to the famous influence maximization problem in social networks with an additional stochastic noise term. In this case, we provide sufficient conditions for when the objective is submodular, allowing a greedy algorithm to achieve an approximation ratio of $1-1/e$. Additionally, we compare the Ising-based algorithms with traditional influence maximization algorithms, demonstrating the practical importance of accurately modeling stochastic fluctuations in the system.