Social Cloud Computing

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This document outlines different techniques for social cloud computing including community detection, partitioning, and hierarchical clustering algorithms that can be used to analyze social networks from Twitter data. The techniques include label prediction, community detection, partitioning using the KL algorithm, hierarchical and edge-removal clustering, and modularity maximization. Ruby is proposed as the main programming language to create a Twitter crawler and build web services, while R could be used for creating charts to analyze the data.

Community Detection
Partitioning: KL Algo
Hierarchical:
Hierarchical Clustering
Edge-Removal:
Modularity
Maximization
Edge-Removal: Bridge-
Cut Algo

Label Prediction

Weighted node
Weighted edge

We may use Ruby as main programming language for
creating Twitter crawler, analyzing data and building web
services.
Alternatively, R may be used in creating charts.

Fast unfolding of communities in large networks/
Cornel University 2008

https://sites.google.com/site/ﬁndcommunities

Community structure in social and biological networks/
Cornell University 2002

Leveraging Label-Independent Features for
Classiﬁcation in Sparsely Labeled Networks: An
Empirical Study

With the increasingly ubiquitous nature of Social networks and Cloud computing, users are starting to explore new ways to interact with, and exploit these developing paradigms. Social networks are used to reflect real world relationships that allow users to share information and form connections between one another, essentially creating dynamic Virtual Organizations. We propose leveraging the pre-established trust formed through friend relationships within a Social network to form a dynamic “Social Cloud”, enabling friends to share resources within the context of a Social network. We believe that combining trust relationships with suitable incentive mechanisms (through financial payments or bartering) could provide much more sustainable resource sharing mechanisms. This paper outlines our vision of, and experiences with, creating a Social Storage Cloud, looking specifically at possible market mechanisms that could be used to create a dynamic Cloud infrastructure in a Social network environment.

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