Search Results for author: Jia Wu

Found 63 papers, 34 papers with code

Exploring Sparsity in Graph Transformers

no code implementations9 Dec 2023 Chuang Liu, Yibing Zhan, Xueqi Ma, Liang Ding, Dapeng Tao, Jia Wu, Wenbin Hu, Bo Du

Graph Transformers (GTs) have achieved impressive results on various graph-related tasks.

Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes

no code implementations21 Nov 2023 Chuang Liu, Wenhang Yu, Kuang Gao, Xueqi Ma, Yibing Zhan, Jia Wu, Bo Du, Wenbin Hu

Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning.

Graph Representation Learning

Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection

1 code implementation30 Oct 2023 Jiaqian Ren, Hao Peng, Lei Jiang, Zhiwei Liu, Jia Wu, Zhengtao Yu, Philip S. Yu

While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance.

Contrastive Learning Event Detection

Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model

1 code implementation3 Aug 2023 Fu Lin, Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Zitong Wang, Haonan Gong

Then, two competing student models trained by normal and abnormal graphs respectively fit graph representations of the teacher model in terms of node-level and graph-level representation perspectives.

Anomaly Detection

Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification

1 code implementation11 Apr 2023 Xingcheng Fu, Yuecen Wei, Qingyun Sun, Haonan Yuan, Jia Wu, Hao Peng, JianXin Li

We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments.

Graph Representation Learning Node Classification

KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks

no code implementations22 Feb 2023 Zhizhi Yu, Di Jin, Cuiying Huo, Zhiqiang Wang, Xiulong Liu, Heng Qi, Jia Wu, Lingfei Wu

Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes.

Heterogeneous Social Event Detection via Hyperbolic Graph Representations

1 code implementation20 Feb 2023 Zitai Qiu, Jia Wu, Jian Yang, Xing Su, Charu C. Aggarwal

This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space.

Contrastive Learning Event Detection

A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT

no code implementations18 Feb 2023 Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.

Graph Learning Language Modelling +1

A Comprehensive Survey on Graph Summarization with Graph Neural Networks

no code implementations13 Feb 2023 Nasrin Shabani, Jia Wu, Amin Beheshti, Quan Z. Sheng, Jin Foo, Venus Haghighi, Ambreen Hanif, Maryam Shahabikargar

Hence, this paper presents a comprehensive survey of progress in deep learning summarization techniques that rely on graph neural networks (GNNs).

Graph Attention

A Comprehensive Survey on Automatic Knowledge Graph Construction

no code implementations10 Feb 2023 Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, Xindong Wu

A knowledge graph is built in three steps: knowledge acquisition, knowledge refinement, and knowledge evolution.

graph construction

Unbiased and Efficient Self-Supervised Incremental Contrastive Learning

1 code implementation28 Jan 2023 Cheng Ji, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Qingyun Sun, Phillip S. Yu

Contrastive Learning (CL) has been proved to be a powerful self-supervised approach for a wide range of domains, including computer vision and graph representation learning.

Contrastive Learning Graph Representation Learning +1

State of the Art and Potentialities of Graph-level Learning

no code implementations14 Jan 2023 Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, Pietro Liò

Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures.

Graph Learning

Mining User-aware Multi-relations for Fake News Detection in Large Scale Online Social Networks

1 code implementation21 Dec 2022 Xing Su, Jian Yang, Jia Wu, Yuchen Zhang

In this paper, we construct a dual-layer graph (i. e., the news layer and the user layer) to extract multiple relations of news and users in social networks to derive rich information for detecting fake news.

Fake News Detection

Brain Tumor Synthetic Data Generation with Adaptive StyleGANs

1 code implementation4 Dec 2022 Usama Tariq, Rizwan Qureshi, Anas Zafar, Danyal Aftab, Jia Wu, Tanvir Alam, Zubair Shah, Hazrat Ali

Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues.

Synthetic Data Generation Transfer Learning

DAGAD: Data Augmentation for Graph Anomaly Detection

1 code implementation18 Oct 2022 Fanzhen Liu, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu C. Aggarwal

To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes.

Data Augmentation Graph Anomaly Detection

Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation

1 code implementation2 Oct 2022 Yuecen Wei, Xingcheng Fu, Qingyun Sun, Hao Peng, Jia Wu, Jinyan Wang, Xianxian Li

To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology.

Privacy Preserving

Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees

1 code implementation4 Sep 2022 Jiaqian Ren, Lei Jiang, Hao Peng, Lingjuan Lyu, Zhiwei Liu, Chaochao Chen, Jia Wu, Xu Bai, Philip S. Yu

Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.

Attribute Link Prediction +2

Automating DBSCAN via Deep Reinforcement Learning

2 code implementations9 Aug 2022 Ruitong Zhang, Hao Peng, Yingtong Dou, Jia Wu, Qingyun Sun, Jingyi Zhang, Philip S. Yu

DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality.

Clustering Computational Efficiency +3

A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions

1 code implementation15 Jun 2022 Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester

Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.

Clustering Deep Clustering +1

Graph-level Neural Networks: Current Progress and Future Directions

no code implementations31 May 2022 Ge Zhang, Jia Wu, Jian Yang, Shan Xue, Wenbin Hu, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu Aggarwal

To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.

Evidential Temporal-aware Graph-based Social Event Detection via Dempster-Shafer Theory

no code implementations24 May 2022 Jiaqian Ren, Lei Jiang, Hao Peng, Zhiwei Liu, Jia Wu, Philip S. Yu

To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula.

Event Detection

Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities

1 code implementation15 Apr 2022 Chuang Liu, Yibing Zhan, Jia Wu, Chang Li, Bo Du, Wenbin Hu, Tongliang Liu, DaCheng Tao

Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation.

Graph Classification Graph Generation

Deep reinforcement learning guided graph neural networks for brain network analysis

no code implementations18 Mar 2022 Xusheng Zhao, Jia Wu, Hao Peng, Amin Beheshti, Jessica J. M. Monaghan, David Mcalpine, Heivet Hernandez-Perez, Mark Dras, Qiong Dai, Yangyang Li, Philip S. Yu, Lifang He

Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.

reinforcement-learning Reinforcement Learning (RL) +1

Curvature Graph Generative Adversarial Networks

1 code implementation3 Mar 2022 JianXin Li, Xingcheng Fu, Qingyun Sun, Cheng Ji, Jiajun Tan, Jia Wu, Hao Peng

In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named \textbf{\modelname}, which is the first GAN-based graph representation method in the Riemannian geometric manifold.

Generative Adversarial Network

Single-stage Rotate Object Detector via Two Points with Solar Corona Heatmap

no code implementations14 Feb 2022 Beihang Song, Jing Li, Shan Xue, Jun Chang, Jia Wu, Jun Wan, Tianpeng Liu

In this study, we developed a single-stage rotating object detector via two points with a solar corona heatmap (ROTP) to detect oriented objects.

Object object-detection +2

Graph Structure Learning with Variational Information Bottleneck

1 code implementation16 Dec 2021 Qingyun Sun, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, Philip S. Yu

Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.

Graph structure learning

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

1 code implementation15 Oct 2021 Xingcheng Fu, JianXin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu

Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.

Graph Learning Multi-agent Reinforcement Learning +1

DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control

no code implementations12 Sep 2021 Libing Wu, Min Wang, Dan Wu, Jia Wu

Then, to efficiently utilize the historical state information of the intersection, we design a sequence model with the temporal convolutional network (TCN) to capture the historical information and further merge it with the spatial information to improve its performance.

Graph Attention

Event Extraction by Associating Event Types and Argument Roles

no code implementations23 Aug 2021 Qian Li, Shu Guo, Jia Wu, JianXin Li, Jiawei Sheng, Lihong Wang, Xiaohan Dong, Hao Peng

It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles.

Event Extraction Graph Attention +2

Transferring Knowledge Distillation for Multilingual Social Event Detection

1 code implementation6 Aug 2021 Jiaqian Ren, Hao Peng, Lei Jiang, Jia Wu, Yongxin Tong, Lihong Wang, Xu Bai, Bo wang, Qiang Yang

Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.

Cross-Lingual Word Embeddings Event Detection +2

A Survey on Deep Learning Event Extraction: Approaches and Applications

no code implementations5 Jul 2021 Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu

Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.

Event Extraction

Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms

1 code implementation30 Jun 2021 Pingjun Chen, Muhammad Aminu, Siba El Hussein, Joseph D. Khoury, Jia Wu

In the end, we built global graphs to abstract spatial interaction patterns and extract features for disease diagnosis.

Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations

1 code implementation23 Jun 2021 Qian Li, Hao Peng, JianXin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang

Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.

Event Extraction Incremental Learning +3

Solving Stochastic Optimization with Expectation Constraints Efficiently by a Stochastic Augmented Lagrangian-Type Algorithm

no code implementations22 Jun 2021 Liwei Zhang, Yule Zhang, Jia Wu, Xiantao Xiao

We present a computable stochastic approximation type algorithm, namely the stochastic linearized proximal method of multipliers, to solve this convex stochastic optimization problem.

Stochastic Optimization

A Comprehensive Survey on Graph Anomaly Detection with Deep Learning

1 code implementation14 Jun 2021 Xiaoxiao Ma, Jia Wu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z. Sheng, Hui Xiong, Leman Akoglu

In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.

Graph Anomaly Detection

dFDA-VeD: A Dynamic Future Demand Aware Vehicle Dispatching System

no code implementations10 Jun 2021 Yang Guo, Tarique Anwar, Jian Yang, Jia Wu

As the process should be socially and economically profitable, the task of vehicle dispatching is highly challenging, specially due to the time-varying travel demands and traffic conditions.

A Comprehensive Survey on Community Detection with Deep Learning

no code implementations26 May 2021 Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu

A community reveals the features and connections of its members that are different from those in other communities in a network.

Clustering Community Detection +3

Task-adaptive Neural Process for User Cold-Start Recommendation

1 code implementation26 Feb 2021 Xixun Lin, Jia Wu, Chuan Zhou, Shirui Pan, Yanan Cao, Bin Wang

In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP).

Meta-Learning Recommendation Systems

Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs

2 code implementations21 Jan 2021 Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, JianXin Li, Philip S. Yu

The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns.

Event Detection Feature Engineering +4

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

no code implementations3 Jan 2021 Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang

We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.

Community Detection

Graph Stochastic Neural Networks for Semi-supervised Learning

1 code implementation NeurIPS 2020 Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, Jilong Wang

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification.

Classification General Classification +2

Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications

no code implementations20 Nov 2020 Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang

In this paper, to fill this gap, we summarize the open problems for privacy preserving KG in data isolation setting and propose possible solutions for them.

Privacy Preserving

Graph Geometry Interaction Learning

1 code implementation NeurIPS 2020 Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, Bin Wang

To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph.

Link Prediction Node Classification

A Deep Framework for Cross-Domain and Cross-System Recommendations

no code implementations14 Sep 2020 Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, Jia Wu

Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy.

Recommendation Systems

Opinion Maximization in Social Trust Networks

1 code implementation19 Jun 2020 Pinghua Xu, Wenbin Hu, Jia Wu, Weiwei Liu

However, the practical significance of the existing studies on this subject is limited for two reasons.

Social and Information Networks Computer Science and Game Theory J.4

Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter

1 code implementation10 Jun 2020 Qi Huang, Junshuai Yu, Jia Wu, Bin Wang

A meta-path based heterogeneous graph attention network framework is proposed to capture the global semantic relations of text contents, together with the global structure information of source tweet propagations for rumor detection.

Graph Attention

Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification

no code implementations25 May 2020 Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng

Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between different nodes.

Classification General Classification +2

Deep Learning for Community Detection: Progress, Challenges and Opportunities

1 code implementation17 May 2020 Fanzhen Liu, Shan Xue, Jia Wu, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Jian Yang, Philip S. Yu

As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics.

Clustering Community Detection +1

Deep Active Learning for Anchor User Prediction

1 code implementation18 Jun 2019 Anfeng Cheng, Chuan Zhou, Hong Yang, Jia Wu, Lei LI, Jianlong Tan, Li Guo

Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction.

Active Learning

Deep segmentation networks predict survival of non-small cell lung cancer

1 code implementation26 Mar 2019 Stephen Baek, Yusen He, Bryan G. Allen, John M. Buatti, Brian J. Smith, Ling Tong, Zhiyu Sun, Jia Wu, Maximilian Diehn, Billy W. Loo, Kristin A. Plichta, Steven N. Seyedin, Maggie Gannon, Katherine R. Cabel, Yusung Kim, Xiaodong Wu

Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value.

Segmentation Tumor Segmentation

TLR: Transfer Latent Representation for Unsupervised Domain Adaptation

no code implementations19 Aug 2018 Pan Xiao, Bo Du, Jia Wu, Lefei Zhang, Ruimin Hu, Xuelong. Li

Many classic methods solve the domain adaptation problem by establishing a common latent space, which may cause the loss of many important properties across both domains.

Unsupervised Domain Adaptation

Brain EEG Time Series Selection: A Novel Graph-Based Approach for Classification

no code implementations14 Jan 2018 Chenglong Dai, Jia Wu, Dechang Pi, Lin Cui

Brain Electroencephalography (EEG) classification is widely applied to analyze cerebral diseases in recent years.

Classification EEG +4

Dynamic Island Model based on Spectral Clustering in Genetic Algorithm

no code implementations5 Jan 2018 Qinxue Meng, Jia Wu, John Ellisy, Paul J. Kennedy

One is that after a certain number of generations, different islands may retain quite similar, converged sub-populations thereby losing diversity and decreasing efficiency.


Subpopulation Diversity Based Selecting Migration Moment in Distributed Evolutionary Algorithms

no code implementations5 Jan 2017 Cheng-Jun Li, Jia Wu

In this paper, a scheme of setting the success rate of migration based on subpopulation diversity at each interval is proposed.

Evolutionary Algorithms Traveling Salesman Problem

Temporal Feature Selection on Networked Time Series

no code implementations20 Dec 2016 Haishuai Wang, Jia Wu, Peng Zhang, Chengqi Zhang

For example, social network users are considered to be social sensors that continuously generate social signals (tweets) represented as a time series.

feature selection Time Series +2

PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning

no code implementations12 Dec 2016 Dongkuan Xu, Jia Wu, Wei zhang, Yingjie Tian

To the end, we propose a positive instance detection via graph updating for multiple instance learning, called PIGMIL, to detect TPI accurately.

Multiple Instance Learning

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