Deep Structural Point Process for Learning Temporal Interaction Networks
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；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）；Xiaomi AI Lab, Xiaomi Inc.（小米公司小米人工智能实验室）
Jiangxia Cao, Xixun Lin, Xin Cong, Shu Guo, Hengzhu Tang, Tingwen Liu, Bin Wang
This work investigates the problem of learning temporal interaction networks. A temporal interaction network consists of a series of chronological interactions between users and items. Previous methods tackle this problem by using different variants of recurrent neural networks to model sequential interactions, which fail to consider the structural information of temporal interaction networks and inevitably lead to sub-optimal results. To this end, we propose a novel Deep Structural Point Process termed as DSPP for learning temporal interaction networks. DSPP simultaneously incorporates the topological structure and long-range dependency structure into our intensity function to enhance model expressiveness. To be specific, by using the topological structure as a strong prior, we first design a topological fusion encoder to obtain node embeddings. An attentive shift encoder is then developed to learn the long-range dependency structure between users and items in continuous time. The proposed two modules enable our model to capture the user-item correlation and dynamic influence in temporal interaction networks. DSPP is evaluated on three real-world datasets for both tasks of item prediction and time prediction. Extensive experiments demonstrate that our model achieves consistent and significant improvements over state-of-the-art baselines.
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
School of Computer, National University of Defense Technology（国防科技大学计算机学院）；Medical Big Data Research Center, Chinese PLA General Hospital（中国人民解放军总医院医学大数据研究中心）；College of Intelligence Science and Technology, National University of Defense Technology（国防科技大学智能科学与技术学院）
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Academia Sinica（中央研究院）；University of Surrey（萨里大学）；Microsoft Research（微软研究院）
ISI Foundation（ISI基金会）；Alma Mater Studiorum University of Bologna（博洛尼亚大学）；Central European University（中欧大学）；Univ de Lyon（里昂大学）；ENS de Lyon（里昂高等师范学院）；INRIA（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；IXXI（里昂复杂系统研究所）
