Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Beyond Adaptive Submodularity: Adaptive Influence Maximization with
  Intermediary Constraints   We consider a brand with a given budget that wants to promote a product over
multiple rounds of influencer marketing. In each round, it commissions an
influencer to promote the product over a social network, and then observes the
subsequent diffusion of the product before adaptively choosing the next
influencer to commission. This process terminates when the budget is exhausted.
We assume that the diffusion process follows the popular Independent Cascade
model. We also consider an online learning setting, where the brand initially
does not know the diffusion parameters associated with the model, and has to
gradually learn the parameters over time.
  Unlike in existing models, the rounds in our model are correlated through an
intermediary constraint: each user can be commissioned for an unlimited number
of times. However, each user will spread influence without commission at most
once. Due to this added constraint, the order in which the influencers are
chosen can change the influence spread, making obsolete existing analysis
techniques that based on the notion of adaptive submodularity. We devise a
sample path analysis to prove that a greedy policy that knows the diffusion
parameters achieves at least $1-1/e - \epsilon$ times the expected reward of
the optimal policy.
  In the online-learning setting, we are the first to consider a truly adaptive
decision making framework, rather than assuming independent epochs, and
adaptivity only within epochs. Under mild assumptions, we derive a regret bound
for our algorithm. In our numerical experiments, we simulate information
diffusions on four Twitter sub-networks, and compare our UCB-based learning
algorithms with several baseline adaptive seeding strategies. Our learning
algorithm consistently outperforms the baselines and achieves rewards close to
the greedy policy that knows the true diffusion parameters.

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The categories of this paper are: q-alg
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
17
The categories of this paper are: q-fin
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The categories of this paper are: q-bio
19
The categories of this paper are: eess