Jiawei Zhang - IEEE Xplore Author Profile

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Author details

Jiawei Zhang

Publications

54

Citations

1,679

Publications by Year

20132024

Co-Authors:

Charu C. AggarwalJiyang BaiLiefeng BoJiannong CaoYi Chang

Show All Co-Authors (91)

Jiawei Zhang

Affiliation

Department of Computer Science

IFM Laboratory

University of California at Davis, Davis, CA, USA

Publication Topics

Attention Mechanism,
Graph Neural Networks,
Neural Network,
Real-world Datasets,
Representation Learning,
User Representation,
Alignment Information,
Alignment Method,
Alignment Performance,
Alignment Problem,
Alignment Results,
Attention Layer

Biography

Jiawei Zhang received the bachelor's degree in computer science from Nanjing University, China, in 2012, and the PhD degree in computer science from the University of Illinois at Chicago, in 2017. He is currently an Assistant Professor with the Department of Computer Science, University of California, Davis. He founded IFM Lab in 2017, and has been working as the director since then. IFM Lab is a research oriented academic laboratory, providing the latest information on fusion learning and data mining research works and application tools to both academia and industry.(Based on document published on 10 August 2022).

Publications

54

Citations

1,679

Publications by Year

20132024

Co-Authors:

Charu C. Aggarwal
Jiyang Bai
Liefeng Bo
Jiannong Cao
Yi Chang

Show All Co-Authors (91)

Author's Published Works

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Showing 1-25 of 54 results

Conferences (45)

Journals (9)

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Single Year
Jiawei Zhang(54)
Philip S. Yu(38)
Yuxiang Ren(9)
Chenwei Zhang(5)
Lin Meng(5)
University of Illinois at Chicago, Chicago, IL, USA(17)
Institute for Data Science, Tsinghua University, Beijing, China(9)
Department of Computer Science, IFM Lab, Florida State University, FL, USA(8)
Department of Computer Science, Florida State University, FL, USA(6)
Department of Computer Science, University of Illinois at Chicago, IL, USA(5)
IEEE Transactions on Knowledge and Data Engineering(4)
2019 IEEE International Conference on Big Data (Big Data)(3)
2020 IEEE International Conference on Data Mining (ICDM)(3)
2021 International Joint Conference on Neural Networks (IJCNN)(3)
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)(2)
IEEE(54)
Atlanta, GA, USA(4)
Los Angeles, CA, USA(4)
Shenzhen, China(4)
San Francisco, CA, USA(3)
Sorrento, Italy(3)
Social Networks(23)
Heterogeneous Network(15)
Network Alignment(15)
Objective Function(14)
Link Prediction(13)

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Results

Context-Aware Attentive Multilevel Feature Fusion for Named Entity Recognition

Zhiwei Yang;Jing Ma;Hechang Chen;Jiawei Zhang;Yi Chang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 1 | Journal Article |

Cited by: Papers (16)

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In the era of information explosion, named entity recognition (NER) has attracted widespread attention in the field of natural language processing, as it is fundamental to information extraction. Recently, methods of NER based on representation learning, e.g., character embedding and word embedding, have demonstrated promising recognition results. However, existing models only consider partial fea...Show More

Context-Aware Attentive Multilevel Feature Fusion for Named Entity Recognition

Zhiwei Yang;Jing Ma;Hechang Chen;Jiawei Zhang;Yi Chang

IEEE Transactions on Neural Networks and Learning Systems

Year: 2024 | Volume: 35, Issue: 1 | Journal Article |

Location-Adaptive Generative Graph Augmentation for Fraud Detection

Lin Meng;Xiaonan Zhang;Jiawei Zhang;Philip S. Yu

2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI)

Year: 2023 | Conference Paper |

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Class imbalance is a well-recognized challenge in GNN-based fraud detection. Traditional methods like re-sampling and re-weighting address this issue by balancing class distribution. However, node class balancing with simple re-sampling or re-weighting may significantly distort the data distributions and eventually lead to the ineffective performance of GNNs. In this paper, we propose a novel appr...Show More

Location-Adaptive Generative Graph Augmentation for Fraud Detection

Lin Meng;Xiaonan Zhang;Jiawei Zhang;Philip S. Yu

2023 IEEE 5th International Conference on Cognitive Machine Intelligence (CogMI)

Year: 2023 | Conference Paper |

When Behavior Analysis Meets Social Network Alignment

Zhongbao Zhang;Fuxin Ren;Jiawei Zhang;Sen Su;Yang Yan;Qian Wei;Li Sun;Guozhen Zhu;Congying Guo

IEEE Transactions on Knowledge and Data Engineering

Year: 2023 | Volume: 35, Issue: 7 | Journal Article |

Cited by: Papers (4)

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Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from poor learning performance. In fact, we observe that social network alignment and user behavior analysis can benefit from each other. Motivated by such an observation, w...Show More

When Behavior Analysis Meets Social Network Alignment

Zhongbao Zhang;Fuxin Ren;Jiawei Zhang;Sen Su;Yang Yan;Qian Wei;Li Sun;Guozhen Zhu;Congying Guo

IEEE Transactions on Knowledge and Data Engineering

Year: 2023 | Volume: 35, Issue: 7 | Journal Article |

Few-Shot Radiology Report Generation via Knowledge Transfer and Multi-modal Alignment

Xing Jia;Yun Xiong;Jiawei Zhang;Yao Zhang;Yangyong Zhu;Philip S. Yu

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2022 | Conference Paper |

Cited by: Papers (1)

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Automatic radiology report generation aims at generating informative text from the given medical image, which could assist diagnosis and lighten the workload of radiologists. While some models have been proposed to study on this task, few of them paid attention to the radiology report generation for rare diseases, except for RareGen which solved this problem by enhancing the semantic representatio...Show More

Few-Shot Radiology Report Generation via Knowledge Transfer and Multi-modal Alignment

Xing Jia;Yun Xiong;Jiawei Zhang;Yao Zhang;Yangyong Zhu;Philip S. Yu

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2022 | Conference Paper |

Stage Evolving Graph Neural Network based Dynamic Recommendation with Life Cycles

Lin Meng;Haoran Yang;Jiawei Zhang

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

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Existing methods of dynamic recommender systems usually build neural networks or collaborative filtering models according to the absolute timeline, which fails to consider the preferences of users or characteristics of products in their life cycles. With the rise of graph neural networks, researchers have recently proposed methods based on graph neural networks to solve the recommendation problem....Show More

Stage Evolving Graph Neural Network based Dynamic Recommendation with Life Cycles

Lin Meng;Haoran Yang;Jiawei Zhang

2022 International Joint Conference on Neural Networks (IJCNN)

Year: 2022 | Conference Paper |

Radiology Report Generation for Rare Diseases via Few-shot Transformer

Xing Jia;Yun Xiong;Jiawei Zhang;Yao Zhang;Blackley Suzanne;Yangyong Zhu;Chunlei Tang

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2021 | Conference Paper |

Cited by: Papers (6)

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Reliable automatic radiology report generation is highly desired to reduce the labor-intensive and error-prone workload for healthcare workers. While some multi-modal learning models have been proposed to study on this task, few of them paid attention to the radiology report generation for rare diseases, except for RareGen which solved this problem by enhancing the semantic representations of rare...Show More

Radiology Report Generation for Rare Diseases via Few-shot Transformer

Xing Jia;Yun Xiong;Jiawei Zhang;Yao Zhang;Blackley Suzanne;Yangyong Zhu;Chunlei Tang

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2021 | Conference Paper |

Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention

Yuxiang Ren;Jiawei Zhang

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

Cited by: Papers (11)

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The viral spread of fake news has caused great social harm, making fake news detection an urgent task. Current fake news detection methods rely heavily on text information by learning the extracted news content or writing style of internal knowledge. However, deliberate rumors can mask writing style, bypassing language models and invalidating simple text-based models. In fact, news articles and ot...Show More

Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention

Yuxiang Ren;Jiawei Zhang

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

Ripple Walk Training: A Subgraph-based Training Framework for Large and Deep Graph Neural Network

Jiyang Bai;Yuxiang Ren;Jiawei Zhang

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

Cited by: Papers (11)

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Graph neural networks (GNNs) have achieved outstanding performance in learning graph-structured data and various tasks. However, many current GNNs suffer from three common problems when facing large-size graphs or using a deeper structure: neighbors explosion, node dependence, and oversmoothing. Such problems attribute to the data structures of the graph itself or the designing of the multi-layers...Show More

Ripple Walk Training: A Subgraph-based Training Framework for Large and Deep Graph Neural Network

Jiyang Bai;Yuxiang Ren;Jiawei Zhang

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization

Jiyang Bai;Yuxiang Ren;Jiawei Zhang

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

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For various optimization methods, gradient descent-based algorithms can achieve outstanding performance and have been widely used in various tasks. Among those commonly used algorithms, ADAM owns many advantages such as fast convergence with both the momentum term and the adaptive learning rate. However, since the loss functions of most deep neural networks are non-convex, ADAM also shares the dra...Show More

BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization

Jiyang Bai;Yuxiang Ren;Jiawei Zhang

2021 International Joint Conference on Neural Networks (IJCNN)

Year: 2021 | Conference Paper |

EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph

Yuxiang Ren;Hao Zhu;Jiawei Zhang;Peng Dai;Liefeng Bo

2021 IEEE 37th International Conference on Data Engineering (ICDE)

Year: 2021 | Conference Paper |

Cited by: Papers (10)

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Fraud detection is extremely critical for e-commerce business platforms. Utilizing graph structure data and identifying unexpected dense subgraphs as suspicious is a category of commonly used fraud detection methods. Among them, spectral methods solve the problem efficiently but hurt the performance due to the relaxed constraints. Heuristic methods cannot be accelerated with parallel computation a...Show More

EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph

Yuxiang Ren;Hao Zhu;Jiawei Zhang;Peng Dai;Liefeng Bo

2021 IEEE 37th International Conference on Data Engineering (ICDE)

Year: 2021 | Conference Paper |

ActiveIter: Meta Diagram Based Active Learning in Social Networks Alignment

Yuxiang Ren;Charu C. Aggarwal;Jiawei Zhang

IEEE Transactions on Knowledge and Data Engineering

Year: 2021 | Volume: 33, Issue: 5 | Journal Article |

Cited by: Papers (12)

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Network alignment aims at inferring a set of anchor links matching the shared entities between different information networks, which has become a prerequisite step for effective fusion of multiple information networks. In this paper, we will study the network alignment problem to fuse online social networks specifically. Social network alignment is extremely challenging to address due to several r...Show More

ActiveIter: Meta Diagram Based Active Learning in Social Networks Alignment

Yuxiang Ren;Charu C. Aggarwal;Jiawei Zhang

IEEE Transactions on Knowledge and Data Engineering

Year: 2021 | Volume: 33, Issue: 5 | Journal Article |

DEAM: Adaptive Momentum with Discriminative Weight for Stochastic Optimization

Jiyang Bai;Yuxiang Ren;Jiawei Zhang

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Year: 2020 | Conference Paper |

Cited by: Papers (1)

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Optimization algorithms with momentum, e.g., (ADAM) helps accelerate SGD in parameter updating, which can minify the oscillations of parameters update route. However, the fixed momentum weight (e.g., β1 in ADAM) will propagate errors in momentum computing. Besides, such a hyperparameter can be extremely hard to tune in applications. In this paper, we introduce a novel optimization algorithm, namel...Show More

DEAM: Adaptive Momentum with Discriminative Weight for Stochastic Optimization

Jiyang Bai;Yuxiang Ren;Jiawei Zhang

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Year: 2020 | Conference Paper |

Adversarial Active Learning Based Heterogeneous Graph Neural Network for Fake News Detection

Yuxiang Ren;Bo Wang;Jiawei Zhang;Yi Chang

2020 IEEE International Conference on Data Mining (ICDM)

Year: 2020 | Conference Paper |

Cited by: Papers (38)

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The explosive growth of fake news along with destructive effects on politics, economy, and public safety has increased the demand for fake news detection. Fake news on social media does not exist independently in the form of an article. Many other entities, such as news creators, news subjects, and so on, exist on social media and have relationships with news articles. Different entities and relat...Show More

Adversarial Active Learning Based Heterogeneous Graph Neural Network for Fake News Detection

Yuxiang Ren;Bo Wang;Jiawei Zhang;Yi Chang

2020 IEEE International Conference on Data Mining (ICDM)

Year: 2020 | Conference Paper |

PERFECT: A Hyperbolic Embedding for Joint User and Community Alignment

Li Sun;Zhongbao Zhang;Jiawei Zhang;Feiyang Wang;Yang Du;Sen Su;Philip S. Yu

2020 IEEE International Conference on Data Mining (ICDM)

Year: 2020 | Conference Paper |

Cited by: Papers (8)

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Social network alignment shows fundamental importance in a wide spectrum of applications. To the best of our knowledge, existing studies mainly focus on network alignment at the individual user level, requiring abundant common information between shared individual users. For the networks that cannot meet such requirements, social community structures actually provide complementary and critical inf...Show More

PERFECT: A Hyperbolic Embedding for Joint User and Community Alignment

Li Sun;Zhongbao Zhang;Jiawei Zhang;Feiyang Wang;Yang Du;Sen Su;Philip S. Yu

2020 IEEE International Conference on Data Mining (ICDM)

Year: 2020 | Conference Paper |

Sub-Graph Contrast for Scalable Self-Supervised Graph Representation Learning

Yizhu Jiao;Yun Xiong;Jiawei Zhang;Yao Zhang;Tianqi Zhang;Yangyong Zhu

2020 IEEE International Conference on Data Mining (ICDM)

Year: 2020 | Conference Paper |

Cited by: Papers (68)

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Graph representation learning has attracted lots of attention recently. Existing graph neural networks fed with the complete graph data are not scalable due to limited computation and memory costs. Thus, it remains a great challenge to capture rich information in large-scale graph data. Besides, these methods mainly focus on supervised learning and highly depend on node label information, which is...Show More

Sub-Graph Contrast for Scalable Self-Supervised Graph Representation Learning

Yizhu Jiao;Yun Xiong;Jiawei Zhang;Yao Zhang;Tianqi Zhang;Yangyong Zhu

2020 IEEE International Conference on Data Mining (ICDM)

Year: 2020 | Conference Paper |

Low-shot Learning in Natural Language Processing

Congying Xia;Chenwei Zhang;Jiawei Zhang;Tingting Liang;Hao Peng;Philip S. Yu

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)

Year: 2020 | Conference Paper |

Cited by: Papers (6)

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This paper study the low-shot learning paradigm in Natural Language Processing (NLP), which aims to provide the ability that can adapt to new tasks or new domains with limited annotation data, like zero or few labeled examples. Specifically, Low-shot learning unifies the zero-shot and few-shot learning paradigm. Diverse low-shot learning approaches, including capsule-based networks, data-augmentat...Show More

Low-shot Learning in Natural Language Processing

Congying Xia;Chenwei Zhang;Jiawei Zhang;Tingting Liang;Hao Peng;Philip S. Yu

2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)

Year: 2020 | Conference Paper |

Discovering Localized Information for Heterogeneous Graph Node Representation Learning

Lin Meng;Ning Yan;Masood Mortazavi;Jiawei Zhang

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)

Year: 2020 | Conference Paper |

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Representation learning for heterogeneous graphs aims at learning meaningful node (or edge) representations to facilitate downstream tasks such as node classification, node clustering, and link prediction. While graph neural networks (GNNs) have recently proven to be effective in representation learning, one of the limitations is that most investigations focus on homogeneous graphs. Existing inves...Show More

Discovering Localized Information for Heterogeneous Graph Node Representation Learning

Lin Meng;Ning Yan;Masood Mortazavi;Jiawei Zhang

2020 IEEE 6th International Conference on Collaboration and Internet Computing (CIC)

Year: 2020 | Conference Paper |

Few-shot Radiology Report Generation for Rare Diseases

Xing Jia;Yun Xiong;Jiawei Zhang;Yao Zhang;Yangyong Zhu

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2020 | Conference Paper |

Cited by: Papers (7)

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Automatic radiology report generation that interprets medical images and writes their diagnostic reports is in high demand, as the manual written-report can be laborintensive and error-prone. By this context so far, some radiology report generation models have been proposed already which can hardly detect rare diseases accurately due to insufficient training data of such diseases. Radiology report...Show More

Few-shot Radiology Report Generation for Rare Diseases

Xing Jia;Yun Xiong;Jiawei Zhang;Yao Zhang;Yangyong Zhu

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Year: 2020 | Conference Paper |

FakeDetector: Effective Fake News Detection with Deep Diffusive Neural Network

Jiawei Zhang;Bowen Dong;Philip S. Yu

2020 IEEE 36th International Conference on Data Engineering (ICDE)

Year: 2020 | Conference Paper |

Cited by: Papers (128)

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In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in ...Show More

FakeDetector: Effective Fake News Detection with Deep Diffusive Neural Network

Jiawei Zhang;Bowen Dong;Philip S. Yu

2020 IEEE 36th International Conference on Data Engineering (ICDE)

Year: 2020 | Conference Paper |

Locally Balanced Inductive Matrix Completion for Demand-Supply Inference in Stationless Bike-Sharing Systems

Senzhang Wang;Hao Chen;Jiannong Cao;Jiawei Zhang;Philip S. Yu

IEEE Transactions on Knowledge and Data Engineering

Year: 2020 | Volume: 32, Issue: 12 | Journal Article |

Cited by: Papers (18)

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Stationless bike-sharing systems such as Mobike are currently becoming extremely popular in China as well as some other big cities in the world. Compared to traditional bicycle-sharing systems, stationless bike-sharing systems do not need bike stations. Users can rent and return bikes at arbitrary locations through an App installed on their smart phones. Such a convenient and flexible bike-sharing...Show More

Locally Balanced Inductive Matrix Completion for Demand-Supply Inference in Stationless Bike-Sharing Systems

Senzhang Wang;Hao Chen;Jiannong Cao;Jiawei Zhang;Philip S. Yu

IEEE Transactions on Knowledge and Data Engineering

Year: 2020 | Volume: 32, Issue: 12 | Journal Article |

LATTE: Application Oriented Social Network Embedding

Lin Meng;Jiyang Bai;Jiawei Zhang

2019 IEEE International Conference on Big Data (Big Data)

Year: 2019 | Conference Paper |

Cited by: Papers (2)

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In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node will be represented as a feature vector. However, due to the detachment of the embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community...Show More

LATTE: Application Oriented Social Network Embedding

Lin Meng;Jiyang Bai;Jiawei Zhang

2019 IEEE International Conference on Big Data (Big Data)

Year: 2019 | Conference Paper |

JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation

Zhiwei Liu;Lei Zheng;Jiawei Zhang;Jiayu Han;Philip S. Yu

2019 IEEE International Conference on Big Data (Big Data)

Year: 2019 | Conference Paper |

Cited by: Papers (11)

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Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-domain recommendation area and suffer from the domain-incompatibility problem. I...Show More

JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendation

Zhiwei Liu;Lei Zheng;Jiawei Zhang;Jiayu Han;Philip S. Yu

2019 IEEE International Conference on Big Data (Big Data)

Year: 2019 | Conference Paper |

Deep Diffusive Neural Network based Fake News Detection from Heterogeneous Social Networks

Jiawei Zhang;Bowen Dong;Philip S. Yu

2019 IEEE International Conference on Big Data (Big Data)

Year: 2019 | Conference Paper |

Cited by: Papers (14)

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In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in ...Show More

Deep Diffusive Neural Network based Fake News Detection from Heterogeneous Social Networks

Jiawei Zhang;Bowen Dong;Philip S. Yu

2019 IEEE International Conference on Big Data (Big Data)

Year: 2019 | Conference Paper |

Deep Heterogeneous Social Network Alignment

Lin Meng;Yuxiang Ren;Jiawei Zhang;Fanghua Ye;Philip S. Yu

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)

Year: 2019 | Conference Paper |

Cited by: Papers (1)

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The online social network alignment problem aims at inferring the anchor links connecting the shared users across social networks, which are usually subject to the one-to-one cardinality constraint. Several existing social network alignment models have been proposed, many of which are based on the supervised learning setting. Given a set of labeled anchor links, a group of features can be extracte...Show More

Deep Heterogeneous Social Network Alignment

Lin Meng;Yuxiang Ren;Jiawei Zhang;Fanghua Ye;Philip S. Yu

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)

Year: 2019 | Conference Paper |

Missing Entity Synergistic Completion across Multiple Isomeric Online Knowledge Libraries

Bowen Dong;Jiawei Zhang;Chenwei Zhang;Yang Yang;Philip S. Yu

2019 International Joint Conference on Neural Networks (IJCNN)

Year: 2019 | Conference Paper |

Cited by: Papers (2)

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Online knowledge libraries refer to the online data warehouses that systematically organize and categorize the knowledge-based information about different kinds of concepts and entities. In the era of big data, the setup of online knowledge libraries is an extremely challenging and laborious task, in terms of efforts, time and expense required in the completion of knowledge entities. Especially no...Show More

Missing Entity Synergistic Completion across Multiple Isomeric Online Knowledge Libraries

Bowen Dong;Jiawei Zhang;Chenwei Zhang;Yang Yang;Philip S. Yu

2019 International Joint Conference on Neural Networks (IJCNN)

Year: 2019 | Conference Paper |

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