Σχεδιασμός και υλοποίηση αλγοριθμικής ανάλυσης και οπτικοποίησης δικτύων με χρήση τεχνολογιών cloud computing
Network analysis provides various information regarding their most important parts and 
their topology while also it can be additionally used for their validation. The simplest way for 
network analysis is their visualization, however this is not a trivial problem, mainly when 
dealing with large scale networks with thousands of nodes and edges. 
The object of this thesis is the design and implementation of an integrated algorithmic 
network analysis and visualization with use of parallelization techniques and cloud 
computing technology. Specifically, as a first preprocessing step, the networks are 
simplified with filtering techniques and then they are split to subnetworks by identifying 
their connected components and applying min-cut iteratively to each connected component. 
Afterwards the subnetworks are visualized separately with use of available network 
visualization libraries and packages such as Cytoscape. The visualization of the 
subnetworks is performed on parallel with use of suitable virtual machines, so as to 
minimize the needs of computer memory and the analysis time. 
The implemented methodology was evaluated using as input biological networks and more 
specifically networks of Protein-Protein Interactions (PPI) and Gene co-expression networks. 
The human PPI network, as it’s contained in HINTKB database, and Gene co-expression 
networks derived from Parkinson patients and non-patients and are stored at http://beta.insybio.com were used. 
The implemented methodology was compared with known visualization and network 
analysis software packages like Cytoscape and Graphviz . The comparison of the 
different methodologies was done using as evaluation metrics the execution time, the 
computer memory consumption and the qualitative interpretation of the important network 
components that have been identified through network analysis and quality.