101 citations · 423 across the 19 of their papers we have counts for
19 papers
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
Xingcheng Fu, Jianxin Li, Jia Wu +6
Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. Most existingGNNs embed graph data into Euclidean space and thus are less effective to cap…
HAWK: Rapid Android Malware Detection through Heterogeneous Graph Attention Networks
Yiming Hei, Renyu Yang, Hao Peng +6
Android is undergoing unprecedented malicious threats daily, but the existing methods for malware detection often fail to cope with evolving camouflage in malware. To address this…
Event Extraction by Associating Event Types and Argument Roles
Qian Li, Shu Guo, Jia Wu +5
Event extraction (EE), which acquires structural event knowledge from texts, can be divided into two sub-tasks: event type classification and element extraction (namely identifying…
Transferring Knowledge Distillation for Multilingual Social Event Detection
Jiaqian Ren, Hao Peng, Lei Jiang +6
Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages…
Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
Qian Li, Hao Peng, Jianxin Li +5
Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doi…
Federated Multi-View Learning for Private Medical Data Integration and Analysis
Sicong Che, Hao Peng, Lichao Sun +2
Along with the rapid expansion of information technology and digitalization of health data, there is an increasing concern on maintaining data privacy while garnering the benefits…
HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization
Zhongfen Deng, Hao Peng, Dongxiao He +2
The current state-of-the-art model HiAGM for hierarchical text classification has two limitations. First, it correlates each text sample with all labels in the dataset which contai…
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
Yuwei Cao, Hao Peng, Jia Wu +3
Social events provide valuable insights into group social behaviors and public concerns and therefore have many applications in fields such as product recommendation and crisis man…
FedMood: Federated Learning on Mobile Health Data for Mood Detection
Xiaohang Xu, Hao Peng, Lichao Sun +3
Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical prac…
Hierarchical Bi-Directional Self-Attention Networks for Paper Review Rating Recommendation
Zhongfen Deng, Hao Peng, Congying Xia +3
Review rating prediction of text reviews is a rapidly growing technology with a wide range of applications in natural language processing. However, most existing methods either use…
Kalman Filtering Attention for User Behavior Modeling in CTR Prediction
Hu Liu, Jing Lu, Xiwei Zhao +8
Click-through rate (CTR) prediction is one of the fundamental tasks for e-commerce search engines. As search becomes more personalized, it is necessary to capture the user interest…
KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning
Ye Liu, Yao Wan, Lifang He +2
Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text genera…
Adversarial Directed Graph Embedding
Shijie Zhu, Jianxin Li, Hao Peng +2
Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly…
A Survey on Text Classification: From Shallow to Deep Learning
Qian Li, Hao Peng, Jianxin Li +5
Text classification is the most fundamental and essential task in natural language processing. The last decade has seen a surge of research in this area due to the unprecedented su…
Attentional Graph Convolutional Networks for Knowledge Concept Recommendation in MOOCs in a Heterogeneous View
Shen Wang, Jibing Gong, Jinlong Wang +4
Massive open online courses are becoming a modish way for education, which provides a large-scale and open-access learning opportunity for students to grasp the knowledge. To attra…
Heuristic Semi-Supervised Learning for Graph Generation Inspired by Electoral College
Chen Li, Xutan Peng, Hao Peng +4
Recently, graph-based algorithms have drawn much attention because of their impressive success in semi-supervised setups. For better model performance, previous studies learn to tr…
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding
Yu He, Yangqiu Song, Jianxin Li +3
Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attent…
Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks
Hao Peng, Jianxin Li, Qiran Gong +4
Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text m…
Building Program Vector Representations for Deep Learning
Lili Mou, Ge Li, Yuxuan Liu +4
Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated feature…