1 research outputs found
Continuous Activity Maximization in Online Social Networks
Activity maximization is a task of seeking a small subset of users in a given
social network that makes the expected total activity benefit maximized. This
is a generalization of many real applications. In this paper, we extend
activity maximization problem to that under the general marketing strategy
, which is a -dimensional vector from a lattice space and has
probability to activate a node as a seed. Based on that, we
propose the continuous activity maximization (CAM) problem, where the domain is
continuous and the seed set we select conforms to a certain probability
distribution. It is a new topic to study the problem about information
diffusion under the lattice constraint, thus, we address the problem
systematically here. First, we analyze the hardness of CAM and how to compute
the objective function of CAM accurately and effectively. We prove this
objective function is monotone, but not DR-submodular and not DR-supermodular.
Then, we develop a monotone and DR-submodular lower bound and upper bound of
CAM, and apply sampling techniques to design three unbiased estimators for CAM,
its lower bound and upper bound. Next, adapted from IMM algorithm and sandwich
approximation framework, we obtain a data-dependent approximation ratio. This
process can be considered as a general method to solve those maximization
problem on lattice but not DR-submodular. Last, we conduct experiments on three
real-world datasets to evaluate the correctness and effectiveness of our
proposed algorithms.Comment: in IEEE Transactions on Network Science and Engineerin