On Budgeted Influence Maximization in Social Networks

TL;DR

This paper introduces a new approximation algorithm for the budgeted influence maximization problem in social networks, achieving near-optimal influence spread within a given budget, and proposes efficient heuristics for influence computation.

Contribution

It presents a seed selection algorithm with a (1 - 1/sqrt(e)) approximation ratio and links influence spread calculation to Bayesian network probabilities for efficiency.

Findings

The algorithm outperforms existing methods on large-scale networks.

Experiments confirm the algorithm's effectiveness with moderate computational costs.

Synthetic data analysis reveals how network structure impacts algorithm performance.

Abstract

Given a budget and arbitrary cost for selecting each node, the budgeted influence maximization (BIM) problem concerns selecting a set of seed nodes to disseminate some information that maximizes the total number of nodes influenced (termed as influence spread) in social networks at a total cost no more than the budget. Our proposed seed selection algorithm for the BIM problem guarantees an approximation ratio of (1 - 1/sqrt(e)). The seed selection algorithm needs to calculate the influence spread of candidate seed sets, which is known to be #P-complex. Identifying the linkage between the computation of marginal probabilities in Bayesian networks and the influence spread, we devise efficient heuristic algorithms for the latter problem. Experiments using both large-scale social networks and synthetically generated networks demonstrate superior performance of the proposed algorithm with moderate computation costs. Moreover, synthetic datasets allow us to vary the network parameters and gain important insights on the impact of graph structures on the performance of different algorithms.