62 citations · 439 across the 23 of their papers we have counts for
23 papers
Mitigating the Performance Sacrifice in DP-Satisfied Federated Settings through Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Muyang Li +2
Currently, graph learning models are indispensable tools to help researchers explore graph-structured data. In academia, using sufficient training data to optimize a graph model on…
Generating Counterfactual Hard Negative Samples for Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Sixiao Zhang +4
Graph contrastive learning has emerged as a powerful tool for unsupervised graph representation learning. The key to the success of graph contrastive learning is to acquire high-qu…
Dual Space Graph Contrastive Learning
Haoran Yang, Hongxu Chen, Shirui Pan +3
Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive l…
DialMed: A Dataset for Dialogue-based Medication Recommendation
Zhenfeng He, Yuqiang Han, Zhenqiu Ouyang +4
Medication recommendation is a crucial task for intelligent healthcare systems. Previous studies mainly recommend medications with electronic health records (EHRs). However, some d…
Graph Masked Autoencoders with Transformers
Sixiao Zhang, Hongxu Chen, Haoran Yang +3
Recently, transformers have shown promising performance in learning graph representations. However, there are still some challenges when applying transformers to real-world scenari…
Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
Sixiao Zhang, Hongxu Chen, Xiangguo Sun +2
Graph contrastive learning is the state-of-the-art unsupervised graph representation learning framework and has shown comparable performance with supervised approaches. However, ev…
Towards Unsupervised Deep Graph Structure Learning
Yixin Liu, Yu Zheng, Daokun Zhang +3
In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated whe…
GenLabel: Mixup Relabeling using Generative Models
Jy-yong Sohn, Liang Shang, Hongxu Chen +3
Mixup is a data augmentation method that generates new data points by mixing a pair of input data. While mixup generally improves the prediction performance, it sometimes degrades…
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation
Yicong Li, Hongxu Chen, Yile Li +3
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs. Most existing expl…
Improving Fairness via Federated Learning
Yuchen Zeng, Hongxu Chen, Kangwook Lee
Recently, lots of algorithms have been proposed for learning a fair classifier from decentralized data. However, many theoretical and algorithmic questions remain open. First, is f…
Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation
Haoran Yang, Hongxu Chen, Lin Li +2
User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via le…
Click-Through Rate Prediction with Multi-Modal Hypergraphs
Li He, Hongxu Chen, Dingxian Wang +3
Advertising is critical to many online e-commerce platforms such as e-Bay and Amazon. One of the important signals that these platforms rely upon is the click-through rate (CTR) pr…
TagPick: A System for Bridging Micro-Video Hashtags and E-commerce Categories
Li He, Dingxian Wang, Hanzhang Wang +2
Hashtag, a product of user tagging behavior, which can well describe the semantics of the user-generated content personally over social network applications, e.g., the recently pop…
Is Attention Better Than Matrix Decomposition?
Zhengyang Geng, Meng-Hao Guo, Hongxu Chen +3
As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global correlation discovery. However, is hand-crafted…
Hyperbolic Hypergraphs for Sequential Recommendation
Yicong Li, Hongxu Chen, Xiangguo Sun +5
Hypergraphs have been becoming a popular choice to model complex, non-pairwise, and higher-order interactions for recommender system. However, compared with traditional graph-based…
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems
Sixiao Zhang, Hongxu Chen, Xiao Ming +3
Hyperbolic space and hyperbolic embeddings are becoming a popular research field for recommender systems. However, it is not clear under what circumstances the hyperbolic space sho…
Hyperbolic Neural Collaborative Recommender
Anchen Li, Bo Yang, Hongxu Chen +1
This paper explores the use of hyperbolic geometry and deep learning techniques for recommendation. We present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic…
FENet: A Frequency Extraction Network for Obstructive Sleep Apnea Detection
Guanhua Ye, Hongzhi Yin, Tong Chen +3
Obstructive Sleep Apnea (OSA) is a highly prevalent but inconspicuous disease that seriously jeopardizes the health of human beings. Polysomnography (PSG), the gold standard of det…
Temporal Meta-path Guided Explainable Recommendation
Hongxu Chen, Yicong Li, Xiangguo Sun +2
This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowled…
Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph
Yadan Luo, Zi Huang, Hongxu Chen +2
Signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) given their existing positive and negative interactions…
Heterogeneous Hypergraph Embedding for Graph Classification
Xiangguo Sun, Hongzhi Yin, Bo Liu +4
Recently, graph neural networks have been widely used for network embedding because of their prominent performance in pairwise relationship learning. In the real world, a more natu…
Multi-level Graph Convolutional Networks for Cross-platform Anchor Link Prediction
Hongxu Chen, Hongzhi Yin, Xiangguo Sun +3
Cross-platform account matching plays a significant role in social network analytics, and is beneficial for a wide range of applications. However, existing methods either heavily r…
When Point Process Meets RNNs: Predicting Fine-Grained User Interests with Mutual Behavioral Infectivity
Tong Chen, Lin Wu, Yang Wang +3
Predicting fine-grained interests of users with temporal behavior is important to personalization and information filtering applications. However, existing interest prediction meth…