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Spatial-Temporal Contrasting for Fine-Grained Urban Flow Super-Resolution

Xovee Xu, Ting Zhong, Fan Zhou, and Goce Trajcevski

Under review, 2022

Urban flow super-resolution (FSR) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications on reducing electricity, maintenance, and operation costs. Existing models use techniques from image super-resolution and achieve good performance in FSR. However, they often rely on supervised learning with a large amount of training data, and often generalize poorly and face overfitting. We present STCF, a self-supervised framework for data- and parameter-efficient Expand


CCGL: Contrastive Cascade Graph Learning

Xovee Xu, Fan Zhou*, Kunpeng Zhang, and Siyuan Liu

arXiv:2107.12576, 2021

Supervised learning, while prevalent for information cascade modeling, often requires abundant labeled data in training, and the trained model is not easy to generalize across tasks and datasets. Semi-supervised learning facilitates unlabeled data for cascade understanding in pre-training. It often learns fine-grained feature-level representations, which can easily result in overfitting for downstream tasks. Expand

paper, code

CasFlow: Exploring Hierarchical Structures and Propagation Uncertainty for Cascade Prediction

Xovee Xu, Fan Zhou*, Kunpeng Zhang, Siyuan Liu, and Goce Trajcevski

IEEE Transactions on Knowledge and Data Engineering, 2021

Understanding in-network information diffusion is a fundamental problem in many applications and one of the primary challenges is to predict the information cascade size. Most of the existing models rely either on hypothesized point process (e.g., Poisson and Hawkes processes), or simply predict the information propagation via deep neural networks. However, they fail to simultaneously Expand

paper, code

A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances

Fan Zhou, Xovee Xu*, Goce Trajcevski, and Kunpeng Zhang

ACM Computing Surveys, 2021

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better Expand


A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact

Fan Zhou, Xovee Xu, Ce Li, Goce Trajcevski, Ting Zhong, and Kunpeng Zhang

arXiv:2003.12042, 2020

Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluation and identifying emerging research fields. In this work, we propose an approach based on Heterogeneous Dynamical Graph Neural Network (HDGNN) to explicitly model and predict the cumulative impact of papers and authors. Expand

arXiv, code

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