An Efficient Network-aware Direct Search Method for Influence Maximization
Matteo Bergamaschi, Sara Venturini, Francesco Tudisco, Francesco Rinaldi

TL;DR
This paper introduces NaDS, a network-aware direct search method for influence maximization that leverages network structure to improve scalability and efficiency in large social networks.
Contribution
The paper presents a novel direct search algorithm that incorporates network structure, enhancing scalability for influence maximization in large-scale social networks.
Findings
NaDS outperforms existing methods in large networks.
Exploiting network structure improves computational efficiency.
NaDS achieves comparable or better influence spread results.
Abstract
Influence Maximization (IM) is a pivotal concept in social network analysis, involving the identification of influential nodes within a network to maximize the number of influenced nodes, and has a wide variety of applications that range from viral marketing and information dissemination to public health campaigns. IM can be modeled as a combinatorial optimization problem with a black-box objective function, where the goal is to select seed nodes that maximize the expected influence spread. Direct search methods, which do not require gradient information, are well-suited for such problems. Unlike gradient-based approaches, direct search algorithms, in fact, only evaluate the objective function at a suitably chosen set of trial points around the current solution to guide the search process. However, these methods often suffer from scalability issues due to the high cost of function…
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Complex Network Analysis Techniques · Peer-to-Peer Network Technologies
