IEEE Transactions on Big Data最新文献_第6页

Towards Scalable Multi-View Clustering via Joint Learning of Many Bipartite Graphs 通过联合学习多双向图实现可扩展的多视图聚类

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-10-16 DOI: 10.1109/TBDATA.2023.3325045

Jinghuan Lao;Dong Huang;Chang-Dong Wang;Jian-Huang Lai

{"title":"Towards Scalable Multi-View Clustering via Joint Learning of Many Bipartite Graphs","authors":"Jinghuan Lao;Dong Huang;Chang-Dong Wang;Jian-Huang Lai","doi":"10.1109/TBDATA.2023.3325045","DOIUrl":"10.1109/TBDATA.2023.3325045","url":null,"abstract":"This paper focuses on two limitations to previous multi-view clustering approaches. First, they frequently suffer from quadratic or cubic computational complexity, which restricts their feasibility for large-scale datasets. Second, they often rely on a single graph on each view, yet lack the ability to jointly explore many versatile graph structures for enhanced multi-view information exploration. In light of this, this paper presents a new Scalable Multi-view Clustering via Many Bipartite graphs (SMCMB) approach, which is capable of jointly learning and fusing many bipartite graphs from multiple views while maintaining high efficiency for very large-scale datasets. Different from the one-anchor-set-per-view paradigm, we first produce multiple diversified anchor sets on each view and thus obtain many anchor sets on multiple views, based on which the anchor-based subspace representation learning is enforced and many bipartite graphs are simultaneously learned. Then these bipartite graphs are efficiently partitioned to produce the base clusterings, which are further re-formulated into a unified bipartite graph for the final clustering. Note that SMCMB has almost linear time and space complexity. Extensive experiments on twenty general-scale and large-scale multi-view datasets confirm its superiority in scalability and robustness over the state-of-the-art.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 1","pages":"77-91"},"PeriodicalIF":7.2,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136372168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features 基于特征袋的集成数据交互时间相关性分析

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-10-13 DOI: 10.1109/TBDATA.2023.3324482

Zhifei Ding;Jiahao Han;Rongtao Qian;Liming Shen;Siru Chen;Lingxin Yu;Yu Zhu;Richen Liu

{"title":"eBoF: Interactive Temporal Correlation Analysis for Ensemble Data Based on Bag-of-Features","authors":"Zhifei Ding;Jiahao Han;Rongtao Qian;Liming Shen;Siru Chen;Lingxin Yu;Yu Zhu;Richen Liu","doi":"10.1109/TBDATA.2023.3324482","DOIUrl":"https://doi.org/10.1109/TBDATA.2023.3324482","url":null,"abstract":"We propose eBoF, a novel time-varying ensemble data visualization approach based on the Bag-of-Features (BoF) model. In the eBoF model, we extract a simple and monotone interval from all target variables of ensemble scalar data as a local feature patch. Each local feature of a semantically simple single interval can be defined as a feature patch within the BoF model, with the duration of each interval (i.e., feature patch) serving as its frequency. Feature clusters in ensemble runs are then identified based on the similarity of temporal correlations. eBoF generates clusters along with their probability distributions across all feature patches while preserving the geo-spatial information, which is often lost in traditional topic modeling or clustering algorithms. The probability distribution across different clusters can help to generate reasonable clustering results, evaluated by domain knowledge. We conduct case studies and performance tests to evaluate the eBoF model and gather feedback from domain experts to further refine it. Evaluation results suggest the proposed eBoF can provide insightful and comprehensive evidence on ensemble simulation data analysis.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"9 6","pages":"1726-1737"},"PeriodicalIF":7.2,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138138250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Label-Weighted Graph-Based Learning for Semi-Supervised Classification Under Label Noise 标签噪声下基于标签加权图的半监督分类学习

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-09-27 DOI: 10.1109/TBDATA.2023.3319249

Naiyao Liang;Zuyuan Yang;Junhang Chen;Zhenni Li;Shengli Xie

引用次数: 0

Legal Transition Sequence Recognition of a Bounded Petri Net Using a Gate Recurrent Unit 使用门递归单元识别有界 Petri 网的合法转换序列

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-09-27 DOI: 10.1109/TBDATA.2023.3319252

Qingtian Zeng;Shuai Guo;Rui Cao;Ziqi Zhao;Hua Duan

引用次数: 0

A Multi-Aspect Neural Tensor Factorization Framework for Patent Litigation Prediction 用于专利诉讼预测的多视角神经张量因子化框架

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-09-20 DOI: 10.1109/TBDATA.2023.3313030

Han Wu;Guanqi Zhu;Qi Liu;Hengshu Zhu;Hao Wang;Hongke Zhao;Chuanren Liu;Enhong Chen;Hui Xiong

{"title":"A Multi-Aspect Neural Tensor Factorization Framework for Patent Litigation Prediction","authors":"Han Wu;Guanqi Zhu;Qi Liu;Hengshu Zhu;Hao Wang;Hongke Zhao;Chuanren Liu;Enhong Chen;Hui Xiong","doi":"10.1109/TBDATA.2023.3313030","DOIUrl":"10.1109/TBDATA.2023.3313030","url":null,"abstract":"Patent litigation is an expensive and time-consuming legal process. To reduce costs, companies can proactively manage patents using predictive analysis to identify potential plaintiffs, defendants, and patents that may lead to litigation. However, there has been limited progress in predicting patent litigation due to the scarcity of lawsuits, the complexities of intentions, and the diversity of litigation characteristics. To this end, in this paper, we summarize the major causes of patent litigation into multiple aspects: the complex relations among plaintiffs, defendants and patents as well as the diverse content information from them. Along this line, we propose a Multi-aspect Neural Tensor Factorization (MANTF) framework for patent litigation prediction. First, a Pair-wise Tensor Factorization (PTF) module is designed to capture the complex relations among plaintiffs, defendants and patents inherent in a three-dimensional tensor, which will produce factorized latent vectors for companies and patents with pair-wise ranking estimators. Then, to better represent the patents and companies as an aid for PTF, we design a Patent Embedding Network (PEN) module and a Mask Company Embedding Network (MCEN) module to generate content-aware embedding for them, where PEN represents patents based on their meta, textual and graphical features, and MCEN represents companies by integrating their intrinsic features and competitions. Next, to integrate these three modules together, we leverage a Gaussian prior on the difference between factorized representations and content-aware embedding, and train MANTF in an end-to-end way. In the end, final predictions for patent litigation, i.e., the potentially litigated plaintiffs, defendants and patents, can be made with the well-trained model. We conduct extensive experiments on two real-world datasets, whose results prove that MANTF not only helps predict potential patent litigation but also shows robustness under various data sparse situations.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 1","pages":"35-54"},"PeriodicalIF":7.2,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135597577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference 细粒度城市流量推理的时空对比研究

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-09-18 DOI: 10.1109/TBDATA.2023.3316471

Xovee Xu;Zhiyuan Wang;Qiang Gao;Ting Zhong;Bei Hui;Fan Zhou;Goce Trajcevski

{"title":"Spatial-Temporal Contrasting for Fine-Grained Urban Flow Inference","authors":"Xovee Xu;Zhiyuan Wang;Qiang Gao;Ting Zhong;Bei Hui;Fan Zhou;Goce Trajcevski","doi":"10.1109/TBDATA.2023.3316471","DOIUrl":"https://doi.org/10.1109/TBDATA.2023.3316471","url":null,"abstract":"Fine-grained urban flow inference (FUFI) problem aims to infer the fine-grained flow maps from coarse-grained ones, benefiting various smart-city applications by reducing electricity, maintenance, and operation costs. Existing models use techniques from image super-resolution and achieve good performance in FUFI. However, they often rely on supervised learning with a large amount of training data, and often lack generalization capability and face overfitting. We present a new solution: \u0000<underline>S\u0000patial-\u0000<underline>T\u0000emporal \u0000<underline>C\u0000ontrasting for Fine-Grained Urban \u0000<underline>F\u0000low Inference (STCF). It consists of (i) two pre-training networks for spatial-temporal contrasting between flow maps; and (ii) one coupled fine-tuning network for fusing learned features. By attracting \u0000<italic>spatial-temporally similar\u0000 flow maps while distancing dissimilar ones within the representation space, STCF enhances efficiency and performance. Comprehensive experiments on two large-scale, real-world urban flow datasets reveal that STCF reduces inference error by up to 13.5%, requiring significantly fewer data and model parameters than prior arts.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"9 6","pages":"1711-1725"},"PeriodicalIF":7.2,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138138249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PHAED: A Speaker-Aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-Turn Dialogue Generation PHAED：用于多轮对话生成的说话者感知并行分层注意力编码器-解码器模型

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-09-18 DOI: 10.1109/TBDATA.2023.3316472

Zihao Wang;Ming Jiang;Junli Wang

{"title":"PHAED: A Speaker-Aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-Turn Dialogue Generation","authors":"Zihao Wang;Ming Jiang;Junli Wang","doi":"10.1109/TBDATA.2023.3316472","DOIUrl":"10.1109/TBDATA.2023.3316472","url":null,"abstract":"This article presents a novel open-domain dialogue generation model emphasizing the differentiation of speakers in multi-turn conversations. Differing from prior work that treats the conversation history as a long text, we argue that capturing relative social relations among utterances (i.e., generated by either the same speaker or different persons) benefits the machine capturing fine-grained context information from a conversation history to improve context coherence in the generated response. Given that, we propose a Parallel Hierarchical Attentive Encoder-Decoder (PHAED) model that can effectively leverage conversation history by modeling each utterance with the awareness of its speaker and contextual associations with the same speaker's previous messages. Specifically, to distinguish the speaker roles over a multi-turn conversation (involving two speakers), we regard the utterances from one speaker as responses and those from the other as queries. After understanding queries via hierarchical encoder with inner-query and inter-query encodings, transformer-xl style decoder reuses the hidden states of previously generated responses to generate a new response. Our empirical results with three large-scale benchmarks show that PHAED significantly outperforms baseline models on both automatic and human evaluations. Furthermore, our ablation study shows that dialogue models with speaker tokens can generally decrease the possibility of generating non-coherent responses.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 1","pages":"23-34"},"PeriodicalIF":7.2,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135501710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding 通过关系嵌入实现同构GNN处理异构图

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-09-08 DOI: 10.1109/TBDATA.2023.3313031

Junfu Wang;Yuanfang Guo;Liang Yang;Yunhong Wang

{"title":"Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding","authors":"Junfu Wang;Yuanfang Guo;Liang Yang;Yunhong Wang","doi":"10.1109/TBDATA.2023.3313031","DOIUrl":"10.1109/TBDATA.2023.3313031","url":null,"abstract":"Graph Neural Networks (GNNs) have been generalized to process the heterogeneous graphs by various approaches. Unfortunately, these approaches usually model the heterogeneity via various complicated modules. This article aims to propose a simple yet effective framework to assign adequate ability to the homogeneous GNNs to handle the heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Network (RE-GNN), which employs only one parameter per relation to embed the importance of distinct types of relations and node-type-specific self-loop connections. To optimize these relation embeddings and the model parameters simultaneously, a gradient scaling factor is proposed to constrain the embeddings to converge to suitable values. Besides, we interpret the proposed RE-GNN from two perspectives, and theoretically demonstrate that our RE-GCN possesses more expressive power than GTN (which is a typical heterogeneous GNN, and it can generate meta-paths adaptively). Extensive experiments demonstrate that our RE-GNN can effectively and efficiently handle the heterogeneous graphs and can be applied to various homogeneous GNNs.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"9 6","pages":"1697-1710"},"PeriodicalIF":7.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44348282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts 基于协同专家的图分类长尾识别研究

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-09-07 DOI: 10.1109/TBDATA.2023.3313029

Si-Yu Yi;Zhengyang Mao;Wei Ju;Yong-Dao Zhou;Luchen Liu;Xiao Luo;Ming Zhang

{"title":"Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts","authors":"Si-Yu Yi;Zhengyang Mao;Wei Ju;Yong-Dao Zhou;Luchen Liu;Xiao Luo;Ming Zhang","doi":"10.1109/TBDATA.2023.3313029","DOIUrl":"https://doi.org/10.1109/TBDATA.2023.3313029","url":null,"abstract":"Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution. In fact, most real-world graph data naturally presents a long-tailed form, where the head classes occupy much more samples than the tail classes, it thus is essential to study the graph-level classification over long-tailed data while still remaining largely unexplored. However, most existing long-tailed learning methods in visions fail to jointly optimize the representation learning and classifier training, as well as neglect the mining of the hard-to-classify classes. Directly applying existing methods to graphs may lead to sub-optimal performance, since the model trained on graphs would be more sensitive to the long-tailed distribution due to the complex topological characteristics. Hence, in this paper, we propose a novel long-tailed graph-level classification framework via \u0000<underline>Co\u0000llaborative \u0000<underline>M\u0000ulti-\u0000<underline>e\u0000xpert Learning (CoMe) to tackle the problem. To equilibrate the contributions of head and tail classes, we first develop balanced contrastive learning from the view of representation learning, and then design an individual-expert classifier training based on hard class mining. In addition, we execute gated fusion and disentangled knowledge distillation among the multiple experts to promote the collaboration in a multi-expert framework. Comprehensive experiments are performed on seven widely-used benchmark datasets to demonstrate the superiority of our method CoMe over state-of-the-art baselines.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"9 6","pages":"1683-1696"},"PeriodicalIF":7.2,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138138227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Seq2CASE: Weakly Supervised Sequence to Commentary Aspect Score Estimation for Recommendation Seq2CASE:弱监督序列对推荐的评论方面评分估计

IF 7.2 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2023-09-07 DOI: 10.1109/TBDATA.2023.3313028

Chien-Tse Cheng;Yu-Hsun Lin;Chung-Shou Liao

引用次数: 0