Description: Machine Learning in Social Networks by Manasvi Aggarwal, M.N. Murty Estimated delivery 3-12 business days Format Paperback Condition Brand New Description This book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein–protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area ofcurrent interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs structure information to a low-/high-dimension vector space maintaining all the relevant properties. Author Biography M.N. Murty is currently a Professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore. His research interests are in the area of pattern recognition, data mining, and social network analysis. Ms. Manasvi Aggarwal is currently pursuing her M.S. at the Indian Institute of Science, Bangalore. Her research interest is in the areas of social networks and machine learning Details ISBN 9813340215 ISBN-13 9789813340213 Title Machine Learning in Social Networks Author Manasvi Aggarwal, M.N. Murty Format Paperback Year 2020 Pages 112 Edition 1st Publisher Springer Verlag, Singapore GE_Item_ID:137658601; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys
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ISBN-13: 9789813340213
Book Title: Machine Learning in Social Networks
Number of Pages: Xi, 112 Pages
Publication Name: Machine Learning in Social Networks : Embedding Nodes, Edges, Communities, and Graphs
Language: English
Publisher: Springer
Subject: Engineering (General), Intelligence (Ai) & Semantics, Probability & Statistics / General
Publication Year: 2020
Item Weight: 16 Oz
Type: Textbook
Subject Area: Mathematics, Computers, Technology & Engineering
Author: Manasvi Aggarwal, M. N. Murty
Item Length: 9.3 in
Item Width: 6.1 in
Series: Springerbriefs in Applied Sciences and Technology Ser.
Format: Trade Paperback