paga:index
Differences
This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
paga:index [2012/10/16 07:43] – moreno | paga:index [2013/03/27 12:41] (current) – gdangelo | ||
---|---|---|---|
Line 1: | Line 1: | ||
====== | ====== | ||
- | // | + | // |
Welcome to the PAGA homepage. | Welcome to the PAGA homepage. | ||
- | |||
- | ===== Project Objectives ===== | ||
The goal of this project is to design, implement and evaluate parallel algorithms for large-scale graph analysis. In particular, we are interested in scalable approaches for computing centrality metrics on distributed memory architectures. Centrality metrics such as betweenness centrality, clustering coefficient, | The goal of this project is to design, implement and evaluate parallel algorithms for large-scale graph analysis. In particular, we are interested in scalable approaches for computing centrality metrics on distributed memory architectures. Centrality metrics such as betweenness centrality, clustering coefficient, | ||
- | The PAGA project | + | The PAGA project |
- | + | ||
- | + | ||
- | ===== Scientific Rationale ===== | + | |
+ | === Background === | ||
A social network can be analyzed by computing appropriate metrics on the underlying graph. Unfortunately, | A social network can be analyzed by computing appropriate metrics on the underlying graph. Unfortunately, | ||
- | |||
- | |||
- | |||
- | ===== Innovation Potential ===== | ||
- | |||
Computing centrality metrics in large social graphs is challenging and is subject of active research. However, most of the existing solutions rely on shared memory architectures, | Computing centrality metrics in large social graphs is challenging and is subject of active research. However, most of the existing solutions rely on shared memory architectures, | ||
- | + | === State of the art === | |
- | + | ||
- | ===== State of the art ===== | + | |
We carried out a fairly complete review of the state of the art in a forthcoming paper: M. Lambertini, M. Magnani, M. Marzolla, D. Montesi, C. Paolino, [[http:// | We carried out a fairly complete review of the state of the art in a forthcoming paper: M. Lambertini, M. Magnani, M. Marzolla, D. Montesi, C. Paolino, [[http:// | ||
+ | === Expected Outcomes === | ||
- | + | Our primary goal is to understand if and how centrality metrics can be effectively computed on distributed memory architectures. A positive answer will be a significant contribution to the research community. Our secondary goal is to lay down the foundations for the development of a software package for social network analysis on distributed memory architectures based on MPI. This package should run both on general-purpose, | |
- | + | ||
- | ===== Outcomes and high-impact scientific advances expected ===== | + | |
- | + | ||
- | + | ||
- | Our primary goal is to understand if and how centrality metrics can be effectively computed on distributed memory architectures. A positive answer will be a significant contribution to the research community. Our secondary goal is to laid the foundations for the development of a software package for social network analysis on distributed memory architectures based on MPI. This package should run both on general-purpose, | + |
paga/index.1350373419.txt.gz · Last modified: 2012/10/16 07:43 by moreno