Online influence maximization (OIM) is a popular problem in social networks to learn
influence propagation model parameters and maximize the influence spread at the same …

We investigate the online bandit learning of the monotone multi-linear DR-submodular
functions, designing the algorithm $\mathtt {BanditMLSM} $ that attains $ O (T^{2/3}\log T) …

We address the problem of influence maximization when the social network is accompanied
by diffusion cascades. In the literature, such information is used to compute influence …

We study the online influence maximization (OIM) problem in social networks, where in
multiple rounds the learner repeatedly chooses seed nodes to generate cascades, observes …

Given a graph whose edges are assigned positive-type and negative-type weights, the
problem of correlation clustering aims at grouping the graph vertices so as to minimize (resp …

Seeding the most influential individuals based on the contact structure can substantially
enhance the extent of a spread over the social network. Most of the influence maximization …

Motivated by scenarios of information diffusion and advertising in social media, we study an
influence maximization problem in which little is assumed to be known about the diffusion …

To maximize the impact of an advertisement campaign on social networks, the real-time
bidding (RTB) systems aim at targeting the most influential users of this network. Influence …

This paper is concerned with online targeted advertising on social networks. The main
technical task we address is to estimate the activation probability for user pairs, which …

Explainable recommendation systems (ERSs) have attracted increasing attention from
researchers, which generate high-quality recommendations with intuitive explanations to …