416 citations · 850 across the 17 of their papers we have counts for
18 papers
PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment
Qi Yu, Zhichen Zeng, Yuchen Yan +5
Network alignment (NA) aims to identify node correspondence across different networks and serves as a critical cornerstone behind various downstream multi-network learning tasks. D…
Heterogeneous Contrastive Learning for Foundation Models and Beyond
Lecheng Zheng, Baoyu Jing, Zihao Li +2
In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data. Many existing f…
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization
Haohui Wang, Baoyu Jing, Kaize Ding +6
In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitig…
STERLING: Synergistic Representation Learning on Bipartite Graphs
Baoyu Jing, Yuchen Yan, Kaize Ding +4
A fundamental challenge of bipartite graph representation learning is how to extract informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to address…
Retrieval Based Time Series Forecasting
Baoyu Jing, Si Zhang, Yada Zhu +4
Time series data appears in a variety of applications such as smart transportation and environmental monitoring. One of the fundamental problems for time series analysis is time se…
ARIEL: Adversarial Graph Contrastive Learning
Shengyu Feng, Baoyu Jing, Yada Zhu +1
Contrastive learning is an effective unsupervised method in graph representation learning, and the key component of contrastive learning lies in the construction of positive and ne…
COIN: Co-Cluster Infomax for Bipartite Graphs
Baoyu Jing, Yuchen Yan, Yada Zhu +1
Bipartite graphs are powerful data structures to model interactions between two types of nodes, which have been used in a variety of applications, such as recommender systems, info…
Adversarial Graph Contrastive Learning with Information Regularization
Shengyu Feng, Baoyu Jing, Yada Zhu +1
Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from…
Graph Communal Contrastive Learning
Bolian Li, Baoyu Jing, Hanghang Tong
Graph representation learning is crucial for many real-world applications (e.g. social relation analysis). A fundamental problem for graph representation learning is how to effecti…
Multiplex Graph Neural Network for Extractive Text Summarization
Baoyu Jing, Zeyu You, Tao Yang +2
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary. To extract a good summary from a long text document, senten…
X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning
Baoyu Jing, Shengyu Feng, Yuejia Xiang +3
Graphs are powerful representations for relations among objects, which have attracted plenty of attention. A fundamental challenge for graph learning is how to train an effective G…
HDMI: High-order Deep Multiplex Infomax
Baoyu Jing, Chanyoung Park, Hanghang Tong
Networks have been widely used to represent the relations between objects such as academic networks and social networks, and learning embedding for networks has thus garnered plent…
Network of Tensor Time Series
Baoyu Jing, Hanghang Tong, Yada Zhu
Co-evolving time series appears in a multitude of applications such as environmental monitoring, financial analysis, and smart transportation. This paper aims to address the follow…
SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation
Jieli Zhou, Baoyu Jing, Zeya Wang
Due to the shortage of COVID-19 viral testing kits and the long waiting time, radiology imaging is used to complement the screening process and triage patients into different risk…
Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports
Baoyu Jing, Zeya Wang, Eric Xing
Chest X-Ray (CXR) images are commonly used for clinical screening and diagnosis. Automatically writing reports for these images can considerably lighten the workload of radiologist…
Adversarial Domain Adaptation Being Aware of Class Relationships
Zeya Wang, Baoyu Jing, Yang Ni +3
Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain ada…
Cross-Domain Labeled LDA for Cross-Domain Text Classification
Baoyu Jing, Chenwei Lu, Deqing Wang +2
Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the fe…
On the Automatic Generation of Medical Imaging Reports
Baoyu Jing, Pengtao Xie, Eric Xing
Medical imaging is widely used in clinical practice for diagnosis and treatment. Report-writing can be error-prone for unexperienced physicians, and time- consuming and tedious for…