Social Cloud Computing
Download as KEY, PDF1 like644 views
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.
Social Cloud Computing
- 1. SOCIAL CLOUD COMPUTING
- 2. Outline
- 8. Check In
- 10. Check In
- 11. Check In
- 12. Check In
- 13. Check In
- 14. Check In
- 22. om bi ne C
- 25. Community
- 37. Label Prediction
- 38. Community Detection Partitioning: KL Algo Hierarchical: Hierarchical Clustering Edge-Removal: Modularity Maximization Edge-Removal: Bridge- Cut Algo
- 42. Twitter crawler Twitter API
- 43. 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.
- 45. 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
- 46. THANK YOU