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Titlebook: Social Network Based Big Data Analysis and Applications; Mehmet Kaya,Jalal Kawash,Min-Yuh Day Book 2018 Springer International Publishing

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發(fā)表于 2025-3-25 04:55:10 | 只看該作者
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發(fā)表于 2025-3-25 10:00:43 | 只看該作者
Hiding in Plain Sight: The Anatomy of Malicious , on Facebook,gories of hostile entities who abuse the platform by posting malicious content. In this chapter, we identify and characterize Facebook . that engage in spreading URLs pointing to malicious domains. We revisit the scope and definition of what is deemed as “malicious” in the modern day Internet, and i
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發(fā)表于 2025-3-26 03:56:08 | 只看該作者
Exploring the Role of Intrinsic Nodal Activation on the Spread of Influence in Complex Networks,h influence. In this work, we consider the problem of modeling the spread of influence and the identification of influential entities in a complex network when nodal activation can happen via two different mechanisms. The first mechanism of activation stems from factors that are intrinsic to the nod
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發(fā)表于 2025-3-26 08:01:47 | 只看該作者
Influence and Extension of the Spiral of Silence in Social Networks: A Data-Driven Approach,iral of Silence in social networks based on the real information diffusion data. In this paper, four factors including width, depth, message sentiment, and modularity of information diffusion trees are analyzed to verify the applicability of the theory. Disparities between majority and minority are
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發(fā)表于 2025-3-26 12:21:36 | 只看該作者
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發(fā)表于 2025-3-26 13:55:17 | 只看該作者
Dynamic Pattern Detection for Big Data Stream Analytics,ics design leading to the production of computing devices of various sizes and capabilities, ranging from tiny sensors with limited specifications to mobile devices with huge power and rich functionalities, among others. These stimulated researchers and practitioners work hard seeking the best possi
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發(fā)表于 2025-3-26 17:34:47 | 只看該作者
Community-Based Recommendation for Cold-Start Problem: A Case Study of Reciprocal Online Dating Recacted with other users, such recommender systems tend to work well. However, recommending dates to new users who have made few interactions with others yet, the so-called “cold start” problem, still poses a problem. To address this challenge, in this paper, we propose a novel community-based recomme
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