  Influence maximization (IM) aims to identify a small number of influential
individuals to maximize the information spread and finds applications in
various fields. It was first introduced in the context of viral marketing,
where a company pays a few influencers to promote the product. However, apart
from the cost factor, the capacity of individuals to consume content poses
challenges for implementing IM in real-world scenarios. For example, players on
online gaming platforms can only interact with a limited number of friends. In
addition, we observe that in these scenarios, (i) the initial adopters of
promotion are likely to be the friends of influencers rather than the
influencers themselves, and (ii) existing IM solutions produce sub-par results
with high computational demands. Motivated by these observations, we propose a
new IM variant called capacity constrained influence maximization (CIM), which
aims to select a limited number of influential friends for each initial adopter
such that the promotion can reach more users. To solve CIM effectively, we
design two greedy algorithms, MG-Greedy and RR-Greedy, ensuring the
$1/2$-approximation ratio. To improve the efficiency, we devise the scalable
implementation named RR-OPIM+ with $(1/2-\epsilon)$-approximation and
near-linear running time. We extensively evaluate the performance of 9
approaches on 6 real-world networks, and our solutions outperform all
competitors in terms of result quality and running time. Additionally, we
deploy RR-OPIM+ to online game scenarios, which improves the baseline
considerably.
