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G6:A web-based library for graph visualization

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Visual Informatics 2021年第4期5卷 49-55页

作者： Yanyan Wang Zhanning Bai Zhifeng Lin Xiaoqing Dong Yingchaojie Feng Jiacheng Pan Wei Chen ant group China Alibaba group China tate Key Lab of CAD&CG Zhejiang UniversityHangzhouZhejiangChina

Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development *** existing libraries and tools reduce the difficulty of generating graph visualization,there are still many *** work closely with developers and formulate several design goals,then design and implement G6,a web-based library for graph *** combines template-based configuration for high usability and flexible customization for high *** enhance development efficiency,G6 proposes a range of optimizations,including state management and interaction *** demonstrate its capabilities through an extensive gallery,a quantitative performance evaluation,and an expert interview.G6 was first released in 2017 and has been iterated for 317 *** has served as a web-based library for thousands of applications and received 8312 stars on GitHub.

关键词： Graph visualization Node-link diagram Web-based visualization Visualization library

Distributed Energy Resource Scheduling with Focus on Demand Response Complex Contracts

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Journal of Modern Power Systems and Clean Energy 2021年第5期9卷 1172-1182页

作者： Pedro Faria Zita Vale GECAD–Research group on Intelligent Engineering and Computing for Advanced Innovation and Development R.Dr.António Bernardino de Almeida4314249-015PortoPortugal Polytechnic of Porto R.Dr.António Bernardino de Almeida4314249-015PortoPortugal

Demand response(DR)is a flexible way to improve distributed energy resource *** innovative contribution of this paper is to include complex contracts in the model,which can accommodate the constraints according to the special expectations of each *** contracts are included in the optimization of distributed energy resource scheduling to dispatch DR according to the expectations of ***-period DR events are *** this way,consumers can specify the limits on the time,power,and remuneration regarding participation in DR events,which has not been considered in the *** state of the art treats these aspects separately or uses a statistical approach,without providing consumers with options to combine their preferences regarding different aspects of their flexibility *** model has been validated for 218 consumers using several scenarios and different types of distributed generation,showing that it is possible to increase DR with respect to the preferences of consumers.

关键词： Complex contracts demand response distributed generation virtual power player

AVA:An automated and AI-driven intelligent visual analytics framework

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Visual Informatics 2024年第2期8卷 106-114页

作者： Jiazhe Wang Xi Li Chenlu Li Di Peng Arran Zeyu Wang Yuhui Gu Xingui Lai Haifeng Zhang Xinyue Xu Xiaoqing Dong Zhifeng Lin Jiehui Zhou Xingyu Liu Wei Chen Intelligent Big Data Visualization Lab at Tongji University ShanghaiChina ant group China Alibaba group China State Key Lab of CAD&CG Zhejiang UniversityHangzhouChina

With the incredible growth of the scale and complexity of datasets,creating proper visualizations for users becomes more and more challenging in large *** several visualization recommendation systems have been proposed,so far,the lack of practical engineering inputs is still a major concern regarding the usage of visualization recommendations in the *** this paper,we proposed AVA,an open-sourced web-based framework for Automated Visual *** contains both empiric-driven and insight-driven visualization recommendation methods to meet the demands of creating aesthetic visualizations and understanding expressible insights *** code is available at https://***/antvis/AVA.

关键词： Automated visual analytics Visualization recommendation Insight mining

Building a High-Performance Graph Storage on Top of Tree-Structured Key-Value Stores

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Big Data Mining and Analytics 2024年第1期7卷 156-170页

作者： Heng Lin Zhiyong Wang Shipeng Qi Xiaowei Zhu Chuntao Hong Wenguang Chen Yingwei Luo School of Computer Science Peking UniversityBeijing 100871China ant group Beijing 100020China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

Graph databases have gained widespread adoption in various industries and have been utilized in a range of applications,including financial risk assessment,commodity recommendation,and data lineage *** the principles and design of these databases have been the subject of some investigation,there remains a lack of comprehensive examination of aspects such as storage layout,query language,and *** present study focuses on the design and implementation of graph storage layout,with a particular emphasis on tree-structured key-value *** also examine different design choices in the graph storage layer and present our findings through the development of TuGraph,a highly efficient single-machine graph database that significantly outperforms well-known Graph DataBase Management System(GDBMS).Additionally,TuGraph demonstrates superior performance in the Linked Data Benchmark Council(LDBC)Social Network Benchmark(SNB)interactive benchmark.

关键词： graph database high-performance graph storage

Bi-objective evolutionary Bayesian network structure learning via skeleton constraint

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Frontiers of Computer Science 2023年第6期17卷 111-123页

作者： Ting WU Hong QIAN Ziqi LIU Jun ZHOU Aimin ZHOU Shanghai Institute of AI for Education and School of Computer Science and Technology East China Normal UniversityShanghai 200062China ant group Hangzhou 310023China

Bayesian network is a popular approach to uncertainty knowledge representation and reasoning. Structure learning is the first step to learn a Bayesian network. Score-based methods are one of the most popular ways of learning the structure. In most cases, the score of Bayesian network is defined as adding the log-likelihood score and complexity score by using the penalty function. If the penalty function is set unreasonably, it may hurt the performance of structure search. Thus, Bayesian network structure learning is essentially a bi-objective optimization problem. However, the existing bi-objective structure learning algorithms can only be applied to small-scale networks. To this end, this paper proposes a bi-objective evolutionary Bayesian network structure learning algorithm via skeleton constraint (BBS) for the medium-scale networks. To boost the performance of searching, BBS introduces the random order prior (ROP) initial operator. ROP generates a skeleton to constrain the searching space, which is the key to expanding the scale of structure learning problems. Then, the acyclic structures are guaranteed by adding the orders of variables in the initial skeleton. After that, BBS designs the Pareto rank based crossover and skeleton guided mutation operators. The operators operate on the skeleton obtained in ROP to make the search more targeted. Finally, BBS provides a strategy to choose the final solution. The experimental results show that BBS can always find the structure which is closer to the ground truth compared with the single-objective structure learning methods. Furthermore, compared with the existing bi-objective structure learning methods, BBS is scalable and can be applied to medium-scale Bayesian network datasets. On the educational problem of discovering the influencing factors of students’ academic performance, BBS provides higher quality solutions and is featured with the flexibility of solution selection compared with the widely-used Bayesian netwo

关键词： Bayesian network structure learning multi-objective optimization conditional independence test

Exploring financially constrained small- and medium-sized enterprises based on a multi-relation translational graph attention network

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Frontiers of Information Technology & Electronic Engineering 2023年第3期24卷 388-402页

作者： Qianqiao LIANG Hua WEI Yaxi WU Feng WEI Deng ZHAO Jianshan HE Xiaolin ZHENG Guofang MA Bing HAN College of Computer Science and Technology Zhejiang UniversityHangzhou 310000China MYbank Ant GroupHangzhou 310000China Department of ant Intelligent Engine Technology Ant GroupHangzhou 310000China School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310000China

Financing needs exploration(FNE),which exploresfinancially constrained small-and medium-sized enterprises(SMEs),has become increasingly important in industry forfinancial institutions to facilitate SMEs’*** this paper,wefirst perform an insightful exploratory analysis to exploit the transfer phenomenon offinancing needs among SMEs,which motivates us to fully exploit the multi-relation enterprise social network for boosting the effectiveness of *** main challenge lies in modeling two kinds of heterogeneity,i.e.,transfer heterogeneity and SMEs’behavior heterogeneity,under different relation types *** address these challenges,we propose a graph neural network named Multi-relation tRanslatIonal GrapH a Ttention network(M-RIGHT),which not only models the transfer heterogeneity offinancing needs along different relation types based on a novel entity–relation composition operator but also enables heterogeneous SMEs’representations based on a translation mechanism on relational hyperplanes to distinguish SMEs’heterogeneous behaviors under different relation *** experiments on two large-scale real-world datasets demonstrate M-RIGHT’s superiority over the state-of-the-art methods in the FNE task.

关键词： Financing needs exploration Graph representation learning Transfer heterogeneity Behavior heterogeneity

Unified Enclave Abstraction and Secure Enclave Migration on Heterogeneous Security Architectures

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Journal of Computer Science & Technology 2022年第2期37卷 468-486页

作者： Jin-Yu Gu Hao Li Yu-Bin Xia Hai-Bo Chen Cheng-Gang Qin Zheng-Yu He Engineering Research Center for Domain-Specific Operating Systems Ministry of EducationShanghai 200240China Institute of Parallel and Distributed Systems Shanghai Jiao Tong UniversityShanghai 200240China ant group Hangzhou 310099China

ion and secure enclave migration on heterogeneous security architectures.

关键词： heterogeneous trusted execution environment(TEE) enclave abstraction enclave migration

FinB rain 2.0:when finance meets trustworthy AI

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Frontiers of Information Technology & Electronic Engineering 2022年第12期23卷 1747-1764页

作者： Jun ZHOU Chaochao CHEN Longfei LI Zhiqiang ZHANG Xiaolin ZHENG College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China ant group Hangzhou 310027China

Artificial intelligence(AI) has accelerated the advancement of financial services by identifying hidden patterns from data to improve the quality of financial decisions. However, in addition to commonly desired attributes,such as model accuracy, financial services demand trustworthy AI with properties that have not been adequately realized. These properties of trustworthy AI are interpretability, fairness and inclusiveness, robustness and security,and privacy protection. Here, we review the recent progress and limitations of applying AI to various areas of financial services, including risk management, fraud detection, wealth management, personalized services, and regulatory technology. Based on these progress and limitations, we introduce FinBrain 2.0, a research framework toward trustworthy AI. We argue that we are still a long way from having a truly trustworthy AI in financial services and call for the communities of AI and financial industry to join in this effort.

关键词： Artificial intelligence in finance Trustworthy artificial intelligence Risk management Fraud detection Wealth management

Evolutionary Multi-Tasking Optimization for High-Efficiency Time Series Data Clustering

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Tsinghua Science and Technology 2024年第2期29卷 343-355页

作者： Rui Wang Wenhua Li Kaili Shen Tao Zhang Xiangke Liao Xiangjiang Laboratory Changsha 410205China College of Systems Engineering National University of Defense TechnologyChangsha 410073China ant group Co. Ltd.Hangzhou 310000China College of Computer Science and Technology NUDTChangsha 410073China

Time series clustering is a challenging problem due to the large-volume,high-dimensional,and warping characteristics of time series *** clustering methods often use a single criterion or distance measure,which may not capture all the features of the *** paper proposes a novel method for time series clustering based on evolutionary multi-tasking optimization,termed i-MFEA,which uses an improved multifactorial evolutionary algorithm to optimize multiple clustering tasks simultaneously,each with a different validity index or distance ***,i-MFEA can produce diverse and robust clustering solutions that satisfy various preferences of *** on two artificial datasets show that i-MFEA outperforms single-objective evolutionary algorithms and traditional clustering methods in terms of convergence speed and clustering *** paper also discusses how i-MFEA can address two long-standing issues in time series clustering:the choice of appropriate similarity measure and the number of clusters.

关键词： time series clustering evolutionary multi-tasking multifactorial optimization clustering validity index distance measure