  Influence maximization aims to find a subset of seeds that maximize the
influence spread under a given budget. In this paper, we mainly address the
data-driven version of this problem, where the diffusion model is not given but
needs to be inferred from the history cascades. Several previous works have
addressed this topic in a statistical way and provided efficient algorithms
with theoretical guarantee. However, in their settings, though the diffusion
parameters are inferred, they still need users to preset the diffusion model,
which can be an intractable problem in real-world practices. In this paper, we
reformulate the problem on the attributed network and leverage the node
attributes to estimate the closeness between the connected nodes. Specifically,
we propose a machine learning-based framework, named DSCom, to address this
problem in a heuristic way. Under this framework, we first infer the users'
relationship from the diffusion dataset through attention mechanism and then
leverage spectral clustering to overcome the influence overlap problem in the
lack of exact diffusion formula. Compared to the previous theoretical works, we
carefully designed empirical experiments with parameterized diffusion models
based on real-world social networks, which prove the efficiency and
effectiveness of our algorithm.
