Titlebook: Machine Learning Techniques for Online Social Networks; Tansel Özyer,Reda Alhajj Book 2018 Springer International Publishing AG, part of S

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2190-5428 cial networks.Contains case studies describing how various dThe book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies for analyzing social network data are c

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Ameliorating Search Results Recommendation System Based on ,-Means Clustering Algorithm and Distancof a clustering algorithm combined with a distance measure filters and classifies the results in order to reduce the amount of documents efficiently and gain in terms of documents quality and search time. The proposed architecture is based on . clustering algorithm and the cosine similarity measure. The system showed encouraging results.

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Dynamics of Large-Scale Networks Following a Merger,r the merger. As the original avatars are gradually removed, these structures slowly dissolve, but they remain observable for a surprisingly long time. We present a number of visualizations illustrating the post-merger dynamics and discuss time evolution of selected quantities characterizing the topology of the network.

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Text-Based Analysis of Emotion by Considering Tweets,facebook, twitter, etc. is a very challenging task, still it can give researchers a valuable insight into the complexity of human emotions. In this paper, test from tweets has been used for detecting 32 primary human emotions and then the emotions were analyzed against gender, location, and temporal information of the considered people.

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2190-5428 overed. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. .Machine Learning Techniques for Online Social Networks .will appeal to researchers and students in these fields. .978-3-030-07896-6978-3-319-89932-9Series ISSN 2190-5428 Series E-ISSN 2190-5436

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Book 2018cal aspects, application domains, and case studies for analyzing social network data are covered. The aim is to provide new perspectives on utilizing machine learning and related scientific methods and techniques for social network analysis. .Machine Learning Techniques for Online Social Networks .w

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