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cs.SI2023
BOPIM: Bayesian Optimization for influence maximization on temporal networks
Eric Yanchenko
The goal of influence maximization (IM) is to select a small set of seed nodes which maximizes the spread of influence on a network. In this work, we propose BOPIM, a Bayesian Opti…
cs.SI2023
Influence maximization on temporal networks: a review
Eric Yanchenko, Tsuyoshi Murata, Petter Holme
Influence maximization (IM) is an important topic in network science where a small seed set is chosen to maximize the spread of influence on a network. Recently, this problem has a…
cs.SI2023
Link prediction for ex ante influence maximization on temporal networks
Eric Yanchenko, Tsuyoshi Murata, Petter Holme
Influence maximization (IM) is the task of finding the most important nodes in order to maximize the spread of influence or information on a network. This task is typically studied…