Informetrics on social network mining: research, policy and practice challenges

Abstract

Introduction 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.

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Papers that cite the same references — the relational fallback while embeddings are still backfilling.