Feng Wang

TL;DR: This paper proposes a novel influence spread model called Fluidspread, using the fluid dynamics theory to reveal the time-evolving influence spread process, and formulates the Maximizing Positive Influenced Users (MPIU) problem and design the Fluidsspread greedy algorithm to solve it.

TL;DR: An efficient algorithm of trust-based competitive influence maximization through a heuristic pruning method is developed that is more practical than other baselines on real-world social networks.

TL;DR: A novel concept of user gravitational ability which is inspired by Newton's law of universal gravitation is proposed, which can integrate multi-dimensional factors in an appropriate way and validate the effect of time and topic measure factors in the influence evaluation process.

TL;DR: This work modifications Linear Threshold model based on multi-level attitude and users’ interaction, which is proposed modeling the positive and negative attitude towards an entity in the signed social network and the effect of interaction relationship between users and proposes the LT-MLA greedy algorithm to solve the positive influence maximization problem.

TL;DR: This paper proposes an Influence Deep Learning model to learn users' latent feature representation for predicting influence spread, and designs a strategy to incorporate user-specific features and network structure into the graph convolutional neural network to overcome the imbalance problem of labeled training data.