  Influence maximization (IM) is formulated as selecting a set of initial users
from a social network to maximize the expected number of influenced users.
Researchers have made great progress in designing various traditional methods,
and their theoretical design and performance gain are close to a limit. In the
past few years, learning-based IM methods have emerged to achieve stronger
generalization ability to unknown graphs than traditional ones. However, the
development of learning-based IM methods is still limited by fundamental
obstacles, including 1) the difficulty of effectively solving the objective
function; 2) the difficulty of characterizing the diversified underlying
diffusion patterns; and 3) the difficulty of adapting the solution under
various node-centrality-constrained IM variants. To cope with the above
challenges, we design a novel framework DeepIM to generatively characterize the
latent representation of seed sets, and we propose to learn the diversified
information diffusion pattern in a data-driven and end-to-end manner. Finally,
we design a novel objective function to infer optimal seed sets under flexible
node-centrality-based budget constraints. Extensive analyses are conducted over
both synthetic and real-world datasets to demonstrate the overall performance
of DeepIM. The code and data are available at:
https://github.com/triplej0079/DeepIM.
