Social Network Computing

Inbunden, Engelska, 2024

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Subsequently, the focus shifts to the laws dictating social networks, elucidating phenomena like the small world effect, power law distribution, community detection, diffusion processes, game theory dynamics, and hypernetworks, also including multiplex networks, multi-mode networks and temporal networks.

Produktinformation

Utgivningsdatum2024-11-02
Mått155 x 235 x 43 mm
Vikt1 136 g
FormatInbunden
SpråkEngelska
Antal sidor627
FörlagSpringer Verlag, Singapore
ISBN9789819740833
Originaltitel社会网络计算

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Professor Jiang Wu is a scholar specializing in digital social-technical system, holding the position of Associate Dean at Wuhan University’s School of Information Management. He also leads the university’s E-commerce and Information System discipline development, and directs the university’s Center for E-commerce Research and Development and holds the position of Secretary-General at the Hubei E-commerce Association. His contributions include over 150 research articles published in prestigious journals and conferences, as well as three academic monographs until now. A PH.D graduate of Huazhong University of Science and Technology, Jiang Wu also studied at Carnegie Mellon University as a joint doctoral student. His research interests include data intelligence, social network, smart healthcare, digital village, and so on.

Chapter 1 Introduction to Social Network Computing.- Chapter 2 Visualization of Social Networks.- Chapter 3 Triadic Closure in Social Networks.- Chapter 4 Strong and Weak Relationships in Social Networks.- Chapter 5 Homophily in Social Networks.- Chapter 6 Positive and Negative Balance in Social Networks.- Chapter 7 The Small World in Social Networks.- Chapter 8 Power Laws in Social Networks.- Chapter 9 Communities in Social Networks.- Chapter 10 Communication in Social Networks.- Chapter 11 Games in Social Networks.- Chapter 12 Networks in Social Networks.- Chapter 13 Link Prediction for Social Networks.- Chapter 14 Evaluation of the Influence of Social Networks.- Chapter 15 Dynamic Analysis of Social Networks.- Chapter 16 Randomized Experiments in Social Networks.- Chapter 17 Modeling and Simulation of Social Networks.- Chapter 18 Representation Learning for Social Networks.