Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time
arxiv(2024)
摘要
The Influence Maximization (IM) problem seeks to discover the set of nodes in
a graph that can spread the information propagation at most. This problem is
known to be NP-hard, and it is usually studied by maximizing the influence
(spread) and, optionally, optimizing a second objective, such as minimizing the
seed set size or maximizing the influence fairness. However, in many practical
scenarios multiple aspects of the IM problem must be optimized at the same
time. In this work, we propose a first case study where several IM-specific
objective functions, namely budget, fairness, communities, and time, are
optimized on top of the maximization of influence and minimization of the seed
set size. To this aim, we introduce MOEIM (Many-Objective Evolutionary
Algorithm for Influence Maximization) a Multi-Objective Evolutionary Algorithm
(MOEA) based on NSGA-II incorporating graph-aware operators and a smart
initialization. We compare MOEIM in two experimental settings, including a
total of nine graph datasets, two heuristic methods, a related MOEA, and a
state-of-the-art Deep Learning approach. The experiments show that MOEIM
overall outperforms the competitors in most of the tested many-objective
settings. To conclude, we also investigate the correlation between the
objectives, leading to novel insights into the topic. The codebase is available
at https://github.com/eliacunegatti/MOEIM.
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