CfP: Decision Making in Heterogeneous Network Data Scenarios and Applications
Decision making is the process of making choices by identifying a decision, gathering information, and assessing alternative resolutions. Using a step-by-step decision-making process can help customers make more deliberate, thoughtful decisions by organizing relevant information and defining alternatives. Traditionally, decision making has been investigated in recommendation in social networks, autonomous operations in multi-agent environments, production planning and scheduling in manufacturing systems, patients’ care and treatment in emergency department management in hospitals. However, nowadays, the data used for decision making analysis is often linked and it is in the form of heterogeneity. These heterogeneous relationships may be implicit and cannot be directly processed using the traditional approaches. Therefore, this special issue will establish an emerging forum to attract high-quality research submissions from worldwide scholars to solve the new challenges of making smart decisions in heterogeneous network data scenarios and applications.
Aims and Scope
Existing studies on heterogeneous network data mainly include link prediction, network embedding/representation learning, node classification and clustering, and recommendation problems. There are few researches to discuss how these techniques can be extended to support decision making in heterogeneous network data scenarios and applications. This special issue will encourage researchers to pay more attention to the significant research gap, and deliver practical solutions for making decision in real-life applications. Therefore, this special issue will focus on emerging techniques of decision making in the heterogeneous network data scenarios, and advance applications of decision making in complex situations.
Topics of Interest
The topics of this special issues include but not limited to:
- Group decision in social recommendation
- Sequential choices in decision making for e-commerce
- Predictive decision making in incomplete data scenarios
- Dynamic decision making in data streaming environments
- Trust based decision making in hostile environments
- Cross-types decision making in heterogeneous network data scenarios
- Multi-criteria decision making in heterogeneous network data applications
- Personalised behaviour engagement in decision learning and making
- New knowledge discovery in decision making process
- Knowledge driven decision making in heterogeneous network data
- Benchmark studies of decision-making frameworks and algorithms
Submissions open: July 15, 2021
Submission deadline: October 15, 2021
First review notification: December 14, 2021
Resubmission of revised manuscripts: January 31, 2022
Final notification due: March 20, 2022
Camera ready deadline: May 15, 2022
Associate Professor Jianxin Li, Deakin University, Australia (email@example.com)
Professor Chengfei Liu, Swinburne University of Technology, Australia (firstname.lastname@example.org)
Professor Ziyu Guan, Xidian University, China, (email@example.com)
Assistant Professor Yinghui Wu, Case Western Reserve University, USA, firstname.lastname@example.org
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals.
All papers will be reviewed following standard reviewing procedures for the Journal.
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Biographies of Guest Editors
Jianxin Li received the PhD degree in computer science from the Swinburne University of Technology, Australia, in 2009. He is an Associate Professor in Data Science and the director of the Smart Networks Lab at the School of Information Technology, Deakin University. His research interests include graph database query processing & optimization, social network analytics & computing, complex network data representation learning traffic, personalized online learning analytics, route planning and recommendation in traffic network, outlier detection using machine learning and deep learning. He has published over 80 peer-reviewed articles in international journals and conferences. Jianxin is also a grant assessor in Australia Research Council in Discovery Programs and Linkage Programs. He is the editorial board member in Information Systems, and also serves as the invited reviewer for multiple international journals including The VLDB Journal, IEEE TKDE, WWW Journal, Information Systems, and technical program committee members in many top international conferences like PVLDB, ICDE, AAAI and IJCAI.
Chengfei Liu is a Professor and the head of Web and Data Engineering research group in the Department of Computer Science and Software Engineering at Swinburne University of Technology, Australia. He received the BS, MS and PhD degrees in Computer Science from Nanjing University, China in 1983, 1985 and 1988, respectively. Prior to joining Swinburne, he taught at the University of South Australia and the University of Technology Sydney, and was a Senior Research Scientist at Cooperative Research Centre for Distributed Systems Technology (DSTC) located at University of Queensland. He also held visiting positions at the Chinese University of Hong Kong, the University of Aizu in Japan, and IBM Silicon Valley Lab in USA. He has attracted 8 million dollars in research grants (mostly awarded by the Australia Research Council – ARC), published more than 250 papers in prestigious journals and conferences, and served in over 130 organization committees and program committees. His current research interests include graph data management over large networks, keyword search on structured data, query processing and refinement for advanced database applications, and data-centric workflows.
Ziyu Guan is currently a full professor in Xidian University. His research work is focused on social media computing, with an emphasis on developing novel machine learning and data mining techniques for exploiting the wisdom of crowd in social media, to solve application problems such as recommendation, retrieval, etc. He has published over 70 high quality journal/conference papers, out of which 41 papers were published in leading journals/conferences, such as IEEE TKDE, IEEE TIP, VLDB, SIGMOD, SIGIR, ICDE, CVPR, WWW, AAAI, IJCAI and SIGKDD. According to Google Scholar, his research work has been cited over 1600 times (h-index 21). In 2015, he acquired support from the National Excellent Youth Science Foundation (China). He is an associated editor for Neurocomputing and the International Journal of Machine Learning and Cybernetics. He has served or is serving as (senior) program committee members of related leading international conferences, e.g., SIGKDD, AAAI, IJCAI, NeurIPS, ICML, ICLR, SIGIR.
Yinghui Wu is an Assistant Professor in the Department of Computer and Data Sciences, Case School of Engineering at Case Western Reserve University, US, and a joint staff scientist at Pacific Northwest National Lab, US. He received his Ph.D. in Computer Science from the University of Edinburgh, UK in 2011, and B.S. in Computer Science from Peking University, China in 2007. Before joining CWRU, he held the positions of faculty at Washington State University, research scientist at University of California Santa Barbara, and visiting position at US Army Research Lab. He served as a member of the Network Science Collaborative Technique Alliance (NS-CTA). His research and service have received several awards including the ACM SIGMOD research highlight award (2018), best papers (SIGMOD, VLDB Demo, IEEE Bigdata), and runner-up awards (ICDE), a Google Faculty Research Award, and a VLDB Distinguished Reviewer award. His area is in data management and analytics, including data quality, graph query processing, and knowledge base systems. His research is supported by NSF, DOE, PNNL, USDA, and industry partners. His current research develops scalable graph analytical systems for multidisciplinary database applications.