476 citations · 930 across the 34 of their papers we have counts for
35 papers
Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
Jingyun Zhang, Hao Peng, Jianxin Li +2
Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated p…
Graph Structure Learning with Variational Information Bottleneck
Qingyun Sun, Jianxin Li, Hao Peng +4
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications. Most empirical studies of GNNs directly take the observed graph as input, assuming th…
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…
Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions
Hui Yin, Xiangyu Song, Shuiqiao Yang +1
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world. Even worse,…
Distributed Optimization of Graph Convolutional Network using Subgraph Variance
Taige Zhao, Xiangyu Song, Jianxin Li +2
In recent years, Graph Convolutional Networks (GCNs) have achieved great success in learning from graph-structured data. With the growing tendency of graph nodes and edges, GCN tra…
Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works
Jinghui Si, Xutan Peng, Chen Li +2
Event Extraction bridges the gap between text and event signals. Based on the assumption of trigger-argument dependency, existing approaches have achieved state-of-the-art performa…
Representation Learning for Short Text Clustering
Hui Yin, Xiangyu Song, Shuiqiao Yang +2
Effective representation learning is critical for short text clustering due to the sparse, high-dimensional and noise attributes of short text corpus. Existing pre-trained models (…
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…
RoSearch: Search for Robust Student Architectures When Distilling Pre-trained Language Models
Xin Guo, Jianlei Yang, Haoyi Zhou +2
Pre-trained language models achieve outstanding performance in NLP tasks. Various knowledge distillation methods have been proposed to reduce the heavy computation and storage requ…
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…
A Robust and Generalized Framework for Adversarial Graph Embedding
Jianxin Li, Xingcheng Fu, Hao Peng +5
Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-qua…
CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline Summarization
Qianren Mao, Jiazheng Wang, Zheng Wang +3
Timeline summarization (TLS) involves creating summaries of long-running events using dated summaries from numerous news articles. However, limited data availability has significan…
Noised Consistency Training for Text Summarization
Junnan Liu, Qianren Mao, Bang Liu +3
Neural abstractive summarization methods often require large quantities of labeled training data. However, labeling large amounts of summarization data is often prohibitive due to…
Differentially Private Federated Knowledge Graphs Embedding
Hao Peng, Haoran Li, Yangqiu Song +2
Knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-o…
Incremental Graph Computation: Anchored Vertex Tracking in Dynamic Social Networks
Taotao Cai, Shuiqiao Yang, Jianxin Li +5
User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. When a user chooses…
Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
Gongxu Luo, Jianxin Li, Jianlin Su +5
Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate…
Higher-Order Attribute-Enhancing Heterogeneous Graph Neural Networks
Jianxin Li, Hao Peng, Yuwei Cao +4
Graph neural networks (GNNs) have been widely used in deep learning on graphs. They can learn effective node representations that achieve superior performances in graph analysis ta…
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…
Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks
Hao Peng, Jianxin Li, Yangqiu Song +4
Events are happening in real-world and real-time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings or sports…
Dynamic Network Embedding Survey
Guotong Xue, Ming Zhong, Jianxin Li +3
Since many real world networks are evolving over time, such as social networks and user-item networks, there are increasing research efforts on dynamic network embedding in recent…
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism
Qingyun Sun, Jianxin Li, Hao Peng +4
Graph representation learning has attracted increasing research attention. However, most existing studies fuse all structural features and node attributes to provide an overarching…
Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
Haoyi Zhou, Shanghang Zhang, Jieqi Peng +4
Many real-world applications require the prediction of long sequence time-series, such as electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a h…
Top-k Socio-Spatial Co-engaged Location Selection for Social Users
Nur Al Hasan Haldar, Jianxin Li, Mohammed Eunus Ali +3
With the advent of location-based social networks, users can tag their daily activities in different locations through check-ins. These check-in locations signify user preferences…
Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market
Hao Peng, Jianxin Li, Zheng Wang +5
We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and…