156 citations · 1k across the 58 of their papers we have counts for
58 papers
Concept Discovery for Fast Adapatation
Shengyu Feng, Hanghang Tong
The advances in deep learning have enabled machine learning methods to outperform human beings in various areas, but it remains a great challenge for a well-trained model to quickl…
GENIUS: A Novel Solution for Subteam Replacement with Clustering-based Graph Neural Network
Chuxuan Hu, Qinghai Zhou, Hanghang Tong
Subteam replacement is defined as finding the optimal candidate set of people who can best function as an unavailable subset of members (i.e., subteam) for certain reasons (e.g., c…
Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs
Haipeng Luo, Hanghang Tong, Mengxiao Zhang +1
We study high-probability regret bounds for adversarial -armed bandits with time-varying feedback graphs over rounds. For general strongly observable graphs, we develop an a…
Improved Algorithms for Neural Active Learning
Yikun Ban, Yuheng Zhang, Hanghang Tong +2
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. In particular, we introduce tw…
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…
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
Dongqi Fu, Jingrui He, Hanghang Tong +1
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial cap…
Geometric Matrix Completion via Sylvester Multi-Graph Neural Network
Boxin Du, Changhe Yuan, Fei Wang +1
Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exist…
Schema-Guided Event Graph Completion
Hongwei Wang, Zixuan Zhang, Sha Li +5
We tackle a new task, event graph completion, which aims to predict missing event nodes for event graphs. Existing link prediction or graph completion methods have difficulty deali…
CoNSoLe: Convex Neural Symbolic Learning
Haoran Li, Yang Weng, Hanghang Tong
Learning the underlying equation from data is a fundamental problem in many disciplines. Recent advances rely on Neural Networks (NNs) but do not provide theoretical guarantees in…
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…
SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation
Zhenning Zhang, Boxin Du, Hanghang Tong
Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users. Although Graph Neural Networ…
Trustworthy Graph Neural Networks: Aspects, Methods and Trends
He Zhang, Bang Wu, Xingliang Yuan +3
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation syst…
Optimal Propagation for Graph Neural Networks
Beidi Zhao, Boxin Du, Zhe Xu +2
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input. However, the initial input graph…
Detecting Topology Attacks against Graph Neural Networks
Senrong Xu, Yuan Yao, Liangyue Li +3
Graph neural networks (GNNs) have been widely used in many real applications, and recent studies have revealed their vulnerabilities against topology attacks. To address this issue…
DISCO: Comprehensive and Explainable Disinformation Detection
Dongqi Fu, Yikun Ban, Hanghang Tong +2
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous iss…
RawlsGCN: Towards Rawlsian Difference Principle on Graph Convolutional Network
Jian Kang, Yan Zhu, Yinglong Xia +2
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications. Despite the successes of GCN deployment, GCN often exhibits performance disparity with respec…
Data Augmentation for Deep Graph Learning: A Survey
Kaize Ding, Zhe Xu, Hanghang Tong +1
Graph neural networks, a powerful deep learning tool to model graph-structured data, have demonstrated remarkable performance on numerous graph learning tasks. To address the data…
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…
Backdoor Attack through Frequency Domain
Tong Wang, Yuan Yao, Feng Xu +3
Backdoor attacks have been shown to be a serious threat against deep learning systems such as biometric authentication and autonomous driving. An effective backdoor attack could en…
Fair Regression under Sample Selection Bias
Wei Du, Xintao Wu, Hanghang Tong
Recent research on fair regression focused on developing new fairness notions and approximation methods as target variables and even the sensitive attribute are continuous in the r…
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…
Deep Active Learning by Leveraging Training Dynamics
Haonan Wang, Wei Huang, Ziwei Wu +3
Active learning theories and methods have been extensively studied in classical statistical learning settings. However, deep active learning, i.e., active learning with deep learni…
Unsupervised Belief Representation Learning with Information-Theoretic Variational Graph Auto-Encoders
Jinning Li, Huajie Shao, Dachun Sun +6
This paper develops a novel unsupervised algorithm for belief representation learning in polarized networks that (i) uncovers the latent dimensions of the underlying belief space a…