Link Prediction in Social Networks
Springer International Publishing (Verlag)
978-3-319-28921-2 (ISBN)
Thiswork presents link prediction similarity measures for social networks that exploitthe degree distribution of the networks. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Also presented are similarity measures based on cliques(CNC, AAC, RAC), which assign extra weight between nodes sharing a greater numberof cliques. Additionally, a locally adaptive (LA) similarity measure isproposed that assigns different weights to common nodes based on the degreedistribution of the local neighborhood and the degree distribution of thenetwork. In the context of link prediction in dense networks, the textintroduces a novel two-phase framework that adds edges to the sparse graph toforma boost graph.
Dr. Virinchi Srinivas is a Graduate Research Assistant in the Department of Computer Science at the University of Maryland, College Park, MD, USA. Dr. Pabitra Mitra is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Kharagpur, India.
Introduction.- Link Prediction Using Degree Thresholding.- Locally Adaptive Link Prediction.- Two Phase Framework for Link Prediction.- Applications of Link Prediction.- Conclusion.
| Erscheinungsdatum | 08.10.2016 |
|---|---|
| Reihe/Serie | SpringerBriefs in Computer Science |
| Zusatzinfo | IX, 67 p. 5 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Informatik ► Netzwerke | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Computer Communication Networks • Computer Science • data mining and knowledge discovery • graph mining • link prediction • Local Neighborhood • Power Law Degree Distribution • Recommender Systems |
| ISBN-10 | 3-319-28921-7 / 3319289217 |
| ISBN-13 | 978-3-319-28921-2 / 9783319289212 |
| Zustand | Neuware |
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