Time plays an essential role in the diffusion of information, influence and disease over networks. In many cases we only observe when a node copies information, makes a decision or becomes infected -- but the connectivity, transmission rates between nodes and transmission sources are unknown. Inferring the underlying dynamics is of outstanding interest since it enables forecasting, influencing and retarding infections, broadly construed. To this end, we model diffusion processes as discrete networks of continuous temporal processes occurring at different rates. Given cascade data -- observed infection times of nodes -- we infer the edges of the global diffusion network and estimate the transmission rates of each edge that best explain the observed data. The optimization problem is convex. The model naturally (without heuristics) imposes sparse solutions and requires no parameter tuning. The problem decouples into a collection of independent smaller problems, thus scaling easily to networks on the order of hundreds of thousands of nodes. Experiments on real and synthetic data show that our algorithm both recovers the edges of diffusion networks and accurately estimates their transmission rates from cascade data.
Uncovering Social Network Activity Using Joint User and Topic Interaction
Université Paris Saclay（巴黎萨克雷大学）；Université Paris Cité（巴黎城市大学）；ENS Paris Saclay（巴黎萨克雷高等师范学校）；CNRS（国家科学研究中心）；SSA；INSERM（法国国家健康与医学研究中心）；Centre Borelli（博雷利中心）；Centre d’Analyse et de Mathématique Sociales（社会分析与数学研究中心）；EHESS（高等社会科学研究院）；Laboratoire de Physique de l’École Normale Supérieure（高等师范学校物理实验室）；College of Computing, UM6P（计算学院，UM6P）
Inferring Multiplex Diffusion Network via Multivariate Marked Hawkes Process
Beihang University（北京航空航天大学）；University of Ottawa（渥太华大学）；National Computer Network Emergency Response Technical Team/Coordination Center of China（国家计算机网络应急技术处理协调中心）
