Overview
- Features state-of-the-art techniques for online social media and graph analysis
- Contains case studies describing how various domains may benefit from online social media and networks
- Covers the link between machine learning techniques and social media network analysis
- Includes practical test results from synthetic and real data
Part of the book series: Lecture Notes in Social Networks (LNSN)
Included in the following conference series:
Conference proceedings info: ENIC 2017.
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Table of contents (16 chapters)
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Knowledge and Information Diffusion
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Algorithms and Applications I
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Algorithms and Applications II
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Content Analysis
Keywords
- distributed networks
- interconnected systems
- interrelated data
- open source software development
- social media analysis
- network analysis literacy
- online social networking
- graph analysis
- European Network Intelligence Conference
- ENIC 2017
- Machine learning applications
- machine learning for social network
- data mining and social network analysis
- graph model for social networks
- Social intelligence
- patterns in online social communities
- Twitter communication in US primaries
- computational social sciences
About this book
Editors and Affiliations
About the editors
Dr. H. Ulrich Hoppe holds a full professorship in Computer Science dedicated to the area of “Learning and Knowledge Technologies” at the University of Duisburg-Essen (Germany). After his PhD on interactive programming in mathematics education in 1984, Ulrich Hoppe has worked for about ten years in the field of intelligent user interfaces and cognitive models in Human-Computer Interaction, before he re-focused his research on intelligent support in educational systems and distributed collaborative environments in 1995. With his COLLIDE Research Group he has participated in more than ten European projects on Technology-Enhanced Learning. He was one of the initiators of the European Network of Excellence Kaleidoscope (2004-07). Currently he is engaged in as a PI in a Research Training Group on “User Centred Social Media” funded by the German National Science Foundation since 2015. In his current research he is particularly interested in combining network analysis techniques with other data mining methods in the context of studying and supporting online learning and knowledge building communities.
Dr. Tobias Hecking received his PhD in Computer Science in 2017 and is currently working as a postdoctoral researcher in the COLLIDE research group located in the Department of Computer Science of the University of Duisburg-Essen.
His research focuses on advancednetwork analysis techniques and their applications especially in the domain of learning and
knowledge creating communities. In this context he authored several research papers in the thematic overlap of the fields of social network analysis and mining
and learning analytics. Since 2017 Tobias Hecking is also the coordinator of the research training group "User-centred Social Media" founded by the German
Research Foundation (DFG), which constitutes an interdisciplinary research and qualification environment for excelent young researchers
with backgrounds in computer science and cognitive sciences.
Dr. Piotr Bródka is an assistant professor of Computer Science at the Department of Computational Intelligence, Wroclaw University of Science and Technology. He received his Ph.D. degree from Wroclaw University of Technology in 2012. Dr. Piotr Bródka was a visiting scholar at Stanford University in 2013. He has authored over 70 scholarly and research articles on a variety of areas related to Network Science, focusing on the extraction and dynamics of communities within social networks, spreading processes in complex networks and the analysis of multilayer networks. In 2015 Dr. Piotr Bródka received a 3 year scholarship for the best young scientists awarded by Polish Ministry of Science and Higher Education.
Przemysław Kazienko, Ph.D. is a full professor and leader of ENGINE - the European Centre for Data Science at Wroclaw University of Science and Technology, Poland. He authored over 200 research papers, including 35 in journals with impact factor covering a variety of topics related to social network analysis, complex networks, spread of influence, collective classification, machine learning, sentiment analysis, DSS in medicine, finances and telecommunication, knowledge management, collaborative systems, data mining, recommender systems, information retrieval, and data security. He gave 16 keynote/invited talks for internationalaudience, served as a co-chair of over 20 international scientific conferences and workshops and also led over 50 research project including European (FP7, H2020) and industrial ones. He is an IEEE Senior Member, a member of the Editorial Board of Social Network Analysis and Mining, Social Informatics, International Journal of Knowledge Society Research, International Journal of Human Capital. He is also on the board of Network Science Society and the leader of its Polish Chapter.
Bibliographic Information
Book Title: Network Intelligence Meets User Centered Social Media Networks
Editors: Reda Alhajj, H. Ulrich Hoppe, Tobias Hecking, Piotr Bródka, Przemyslaw Kazienko
Series Title: Lecture Notes in Social Networks
DOI: https://doi.org/10.1007/978-3-319-90312-5
Publisher: Springer Cham
eBook Packages: Social Sciences, Social Sciences (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-90311-8Published: 01 August 2018
Softcover ISBN: 978-3-030-07989-5Published: 19 January 2019
eBook ISBN: 978-3-319-90312-5Published: 31 July 2018
Series ISSN: 2190-5428
Series E-ISSN: 2190-5436
Edition Number: 1
Number of Pages: VI, 247
Number of Illustrations: 9 b/w illustrations, 54 illustrations in colour
Topics: Computational Social Sciences, Applications of Graph Theory and Complex Networks, Data Mining and Knowledge Discovery, Online Marketing/Social Media, Graph Theory