# Efficient Information Flow Maximization in Probabilistic Graphs

## TL;DR

This paper introduces a novel approach for maximizing information flow in uncertain networks by combining specialized data structures and heuristics, effectively addressing NP-hard subproblems with high-quality solutions and low computational cost.

## Contribution

It presents the F-tree data structure for efficient expected information flow computation and heuristics for optimal edge selection in probabilistic graphs.

## Key findings

- F-tree enables efficient analysis of independent graph components.
- Heuristics achieve high information flow with low runtime.
- Proposed methods outperform baseline approaches in quality and speed.

## Abstract

Reliable propagation of information through large networks, e.g., communication networks, social networks or sensor networks is very important in many applications concerning marketing, social networks, and wireless sensor networks. However, social ties of friendship may be obsolete, and communication links may fail, inducing the notion of uncertainty in such networks. In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network. For the problem of finding the optimal edges, we propose a series of heuristics that exploit properties of this data structure. Our evaluation shows that these heuristics lead to high quality solutions, thus yielding high information flow, while maintaining low running time.