416 citations · 850 across the 17 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…