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Informetrics on Social Network Mining: Research, Policy and Practice Challenges

Special issue call for papers from Library Hi Tech

Data Science or data driven science has recently attracted considerable attention. With advances in information technology and infrastructure, large amounts of data can be instantly analysed, interpreted, and visualized by scientists. One of the popular emerged techniques in Data Science is social network mining and anticipatory computing. Informetrics is the study of quantitative aspects of scientific research, library and information science using methods from other fields, such as computer science, network science, social sciences, mathematical sciences, medical and biological sciences, financial, management and political sciences. The main focus is usually on bibliometrics, webometrics and altmetrics. These days many social networks (e.g. Academic Social Networks (ASNs)) have emerged for professional interactions between academic scholars. Specifically, Informetrics on Social Network Mining is focused on using data mining techniques for dealing with informetrics tasks in ASNs. The impact of research work is related to a scholar's reputation and future promotions. Greater research impact not only inspires scholars to continue their research, but also increases the possibility of a larger research budget from sponsors.

We encourage submission of papers especially that are utilizing datasets of Academic Social Networks, such as,,, and, but not limited to it. Bibliometric datasets, such as,, dblp, google scholar, or similar sources can also be used to perform or access various types of research data in academic domain.



  • Anomaly Detection / Group Anomaly Detection
  • Author Contribution Ranking and Patterns Mining
  • Citations Prediction / Reference Prediction
  • Community and Sub-Community Detection
  • Dynamic Author Name Disambiguation
  • Expert Finding / Expert Ranking / Expert Indexing / Expert Prediction
  • Finding Emerging Research Streams or Topics / Tracing Research Topics
  • Finding Author Collaboration Patterns
  • Finding Influential Authors
  • Reciprocal and Heterogeneous Link Prediction Techniques
  • Classification and Prediction
  • Clustering and Spatial Analysis
  • Neural Networks and Deep Learning
  • Ontology-based Meta-Analysis
  • Semantic Mining
  • Sequential Patterns Mining or Association Discovery
  • Social Network Analysis and Mining

Important Dates:

Submission due: 31 January, 2019
Notification of final acceptance: 31 April, 2019
Final papers: 30 June, 2019

Submissions to the special issue will be screened by the Special Issue Editors to insure that they conform to the quality standards of Library Hi Tech Journal. Papers that do not pass this initial screening will be immediately returned to the authors. Reviewers will apply those standards in forming recommendations for acceptance, revision, or rejection. A maximum of two revisions will be invited. Papers should be formatted with Library Hi Tech Journal style (

The submission deadline is December 31, 2018. The prospective contributors should submit their papers directly to the online submission system ( In addition, Authors please choose the Special Issue (Informetrics on Social Network Mining: Research, Policy and Practice challenges) in the online submission.

Related Past Events by Co-Editors
Special Issue on Anticipatory Computing: Crowd Intelligence from Social Network and Big Data, Computers in Human Behavior, 2017.
Special Issue on Human behavior analysis for library and information science, Library Hi Tech, Vol.35, No.4, 2017.
Special Issue on Big Data and Situation-aware Technology for Smarter Healthcare, Journal of Medical and Biological Engineering, 2017.
Special Issue on Data Stream Mining and Soft Computing Applications, Applied Soft Computing, 2017.                                    Special Issue on Smart Space Technology Innovations, Library Hi Tech, Vol.31, No.2, 2013.
Special Issue on Online Fuzzy Machine Learning and Data Mining in Information Sciences (Elsevier), 2013
Special Issue on Learning in nonstationary and evolving environments in IEEE Transactions on Neural Networks and Learning Systems (IEEE press), 2014
Special Issue on Hybrid and Ensemble Techniques in Soft Computing', Soft Computing (Springer), 2014
Special Issue on Evolving Soft Computing Techniques and Applications, Applied Soft Computing (Elsevier), 2014
Special Issue on Information Fusion in Smart Living Technology Innovations, Information Fusion (Elsevier), 2015
Special Issue on Smart Living in Healthcare and Innovations, Journal of Medical Systems (Springer), 2015

Guest Editors
Mu-Yen Chen, Ph.D
Department of Information Management
National Taichung University of Science and Technology, Taiwan

Chien-Hsiang Liao, Ph.D
Department of Information Management
Fu Jen Catholic University, Taiwan

Edwin David Lughofer, Ph.D
Department of Knowledge-Based Mathematical Systems
Johannes Kepler University Linz, Linz, Austria

Erol Egrioglu, Ph.D
Department of Statistics
Giresun University, Turkey