217 citations · 2.1k across the 107 of their papers we have counts for
27 papers · 1 filter
Pruning Before Training May Improve Generalization, Provably
Hongru Yang, Yingbin Liang, Xiaojie Guo +2
It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only retain the testing performance…
T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
Cuiying Huo, Di Jin, Yawen Li +3
Graph Neural Networks (GNNs) have been a prevailing technique for tackling various analysis tasks on graph data. A key premise for the remarkable performance of GNNs relies on comp…
HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOps
Jiajia Li, Feng Tan, Cheng He +4
Modern IT system operation demands the integration of system software and hardware metrics. As a result, it generates a massive amount of data, which can be potentially used to mak…
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
Jiayin Jin, Zeru Zhang, Yang Zhou +1
Only recently, researchers attempt to provide classification algorithms with provable group fairness guarantees. Most of these algorithms suffer from harassment caused by the requi…
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Dong Chen, Lingfei Wu, Siliang Tang +3
Recent years have seen a surge of interest in meta-learning techniques for tackling the few-shot learning (FSL) problem. However, the meta-learner is prone to overfitting since the…
TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature
Cuiying Huo, Di Jin, Chundong Liang +3
Trust evaluation is critical for many applications such as cyber security, social communication and recommender systems. Users and trust relationships among them can be seen as a g…
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
Cuiying Huo, Dongxiao He, Yawen Li +5
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. Most existing HGNN-based approaches are supervised or s…
Compact Graph Structure Learning via Mutual Information Compression
Nian Liu, Xiao Wang, Lingfei Wu +3
Graph Structure Learning (GSL) recently has attracted considerable attentions in its capacity of optimizing graph structure as well as learning suitable parameters of Graph Neural…
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does…
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training
Xiao Qin, Nasrullah Sheikh, Berthold Reinwald +1
This paper describes an end-to-end solution for the relationship prediction task in heterogeneous, multi-relational graphs. We particularly address two building blocks in the pipel…
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Hanlu Wu, Tengfei Ma, Lingfei Wu +1
Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-ex…
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
Xiang Ling, Lingfei Wu, Saizhuo Wang +5
While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph…
Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement
Xiaojie Guo, Liang Zhao, Zhao Qin +3
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning. However, learning the dis…
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
Yu Chen, Lingfei Wu, Mohammed J. Zaki
In this paper, we propose an end-to-end graph learning framework, namely Iterative Deep Graph Learning (IDGL), for jointly and iteratively learning graph structure and graph embedd…
Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks
Zhiqian Chen, Fanglan Chen, Lei Zhang +7
Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing. As an…
Deep Iterative and Adaptive Learning for Graph Neural Networks
Yu Chen, Lingfei Wu, Mohammed J. Zaki
In this paper, we propose an end-to-end graph learning framework, namely Deep Iterative and Adaptive Learning for Graph Neural Networks (DIAL-GNN), for jointly learning the graph s…
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
Lingfei Wu, Ian En-Hsu Yen, Siyu Huo +5
Analysis of large-scale sequential data has been one of the most crucial tasks in areas such as bioinformatics, text, and audio mining. Existing string kernels, however, either (i)…
KerGM: Kernelized Graph Matching
Zhen Zhang, Yijian Xiang, Lingfei Wu +2
Graph matching plays a central role in such fields as computer vision, pattern recognition, and bioinformatics. Graph matching problems can be cast as two types of quadratic assign…
Scalable Global Alignment Graph Kernel Using Random Features: From Node Embedding to Graph Embedding
Lingfei Wu, Ian En-Hsu Yen, Zhen Zhang +5
Graph kernels are widely used for measuring the similarity between graphs. Many existing graph kernels, which focus on local patterns within graphs rather than their global propert…
Multi-stage Deep Classifier Cascades for Open World Recognition
Xiaojie Guo, Amir Alipour-Fanid, Lingfei Wu +4
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem is far more…
DynGraph2Seq: Dynamic-Graph-to-Sequence Interpretable Learning for Health Stage Prediction in Online Health Forums
Yuyang Gao, Lingfei Wu, Houman Homayoun +1
Online health communities such as the online breast cancer forum enable patients (i.e., users) to interact and help each other within various subforums, which are subsections of th…
Attacking Graph Convolutional Networks via Rewiring
Yao Ma, Suhang Wang, Tyler Derr +2
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification. Recent researches show that graph neural…
Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classification
Qi Lei, Lingfei Wu, Pin-Yu Chen +3
Adversarial examples are carefully constructed modifications to an input that completely change the output of a classifier but are imperceptible to humans. Despite these successful…
Revisiting Random Binning Features: Fast Convergence and Strong Parallelizability
Lingfei Wu, Ian E. H. Yen, Jie Chen +1
Kernel method has been developed as one of the standard approaches for nonlinear learning, which however, does not scale to large data set due to its quadratic complexity in the nu…