Overview
- Substantially advances the state of the art in community detection by leveraging structure and attribute similarity
- Provides insight into a large real-world social network (Instagram)
- Shows that treating attributes of qualitatively different kinds as different pays off in revealing network structure
Part of the book series: Lecture Notes in Social Networks (LNSN)
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Keywords
- community detection
- typed social networks
- spectral graph embedding
- crawling to extract social network content and structure
- topic modeling
- clustering
- computer application in social and behavioral sciences
- machine learning
- computer and information systems applications
About this book
Community detection in social networks is an important but challenging problem. This book develops a new technique for finding communities that uses both structural similarity and attribute similarity simultaneously, weighting them in a principled way. The results outperform existing techniques across a wide range of measures, and so advance the state of the art in community detection. Many existing community detection techniques base similarity on either the structural connections among social-network users, or on the overlap among the attributes of each user. Either way loses useful information. There have been some attempts to use both structure and attribute similarity but success has been limited. We first build a large real-world dataset by crawling Instagram, producing a large set of user profiles. We then compute the similarity between pairs of users based on four qualitatively different profile properties: similarity of language used in posts, similarity of hashtags used (which requires extraction of content from them), similarity of images displayed (which requires extraction of what each image is 'about'), and the explicit connections when one user follows another. These single modality similarities are converted into graphs. These graphs have a common node set (the users) but different sets a weighted edges. These graphs are then connected into a single larger graph by connecting the multiple nodes representing the same user by a clique, with edge weights derived from a lazy random walk view of the single graphs. This larger graph can then be embedded in a geometry using spectral techniques. In the embedding, distance corresponds to dissimilarity so geometric clustering techniques can be used to find communities. The resulting communities are evaluated using the entire range of current techniques, outperforming all of them. Topic modelling is also applied to clusters to show that they genuinely represent users with similar interests. This can form the basis for applications such as online marketing, or key influence selection.
Authors and Affiliations
About the authors
Mosab ALfaqeeh is a doctoral graduate of the School of Computing at Queen’s. He works as a software developer.
David Skillicorn has worked extensively in adversarial data analytics, including the use of natural language processing and social network analysis. His work has applications in intelligence, policing, counterterrorism, and cybersecurity. He is the author of two hundred papers and several books, most recently "Cyberspace, Data Analytics, and Policing" (Taylor and Francis).
Bibliographic Information
Book Title: Finding Communities in Social Networks Using Graph Embeddings
Authors: Mosab Alfaqeeh, David B. Skillicorn
Series Title: Lecture Notes in Social Networks
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024
Hardcover ISBN: 978-3-031-60915-2Due: 07 July 2024
Softcover ISBN: 978-3-031-60918-3Due: 07 July 2024
eBook ISBN: 978-3-031-60916-9Due: 07 July 2024
Series ISSN: 2190-5428
Series E-ISSN: 2190-5436
Edition Number: 1
Number of Pages: X, 229
Number of Illustrations: 56 b/w illustrations, 34 illustrations in colour