Complement Coupling Network for Multiple Activated Users Prediction in Social Cascade.

Even though conventional methods have contributed a lot to understanding information diffusion to a certain extent, they are limited by the neglect of considering survivors (i.e., non-participants). As the counterpart to participants, survivors exert analogously a vital role in cascade analysis, which represent the inaccessible scope of the message. To characterize participants and survivors simultaneously and assemble them into the macro-level cascade representation, we propose an end-to-end model, named complement coupling network, which utilizes multi-gating mechanism to coalesce inhomogeneous input. We first design a novel strategy for sampling survivors, which extracts a sequence of emblematic survivors corresponding to the sequence of observed participants. Afterwards, the complement gate is designed to weigh the contributions of the participant and survivor to the cascade at each timestamp. The reset and output gates are reformed to update the cell state and output the cascade snapshot, respectively. Furthermore, an attention mechanism keyed by the source node is introduced to assemble all snapshots within the observation window for predicting multiple subsequent activated users. Extensive experiments on two real-world datasets demonstrate that the proposed model significantly outperforms state-of-the-art approaches.