From Protein Prediction 2 Winter Semester 2014
Revision as of 22:15, 30 December 2014 by Ppwikiuser (talk | contribs) (RoadMap)

The objective of this project is to visualize a network (large networks of >2000 nodes) in a way that the distance of a node from the rest of the network is determined by the number of nodes it is connected to => the more neighbors a node has the larger is its distance from the network. The component must allow zooming in/out, selection by the number of neighbors, coloring by various thresholds and other graph-related features.


Force Directed Network is obtained by using the Force-directed graph drawing algorithms(SPRING ALGORITHM). This algorithm is mainly based on the forces assigned among the set of nodes and edges of a graph.The forces can be either atractive which is used to attract pairs of endpoints of the graph's edges towards each other or repulsive which is used to seperate all pairs of nodes.In equilibrium states for this system of forces,the edges tend to have uniform length(using spring forces) and the nodes which are not connected by any edge tend to be drawn further apart(due to electrical repulsion).


mockup 1
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  • Zoom in and Zoom out of the graph.
  • Distance of a node from the rest of the network is determined by the number of nodes it is connected to.
  • Selection by the number of neighbors
  • Coloring by various thresholds
  • Dividing the whole network into module based on the modularity.
  • Applying filters.
  • Defining the layout of your choice.
  • Exporting the visualisation as an image.


  • Import from Text format
  • Export to image
  • Visualization Using FORCE DIRECTED NETWORK

Application Design

  • Fancy Libraries
    • d3.js
    • jQuery
    • JavaScript InfoVis Toolkit


The input data should be in json/csv format

E.g: var json = [{

     "adjacencies": [  
           "nodeTo": "graphnode1",  
           "nodeFrom": "graphnode0",  
           "data": {  
             "$color": "#557EAA"  
         }, {  
           "nodeTo": "graphnode13",  
           "nodeFrom": "graphnode0",  
           "data": {  
             "$color": "#909291"  
         }, {  
           "nodeTo": "graphnode14",  
           "nodeFrom": "graphnode0",  
           "data": {  
             "$color": "#557EAA"  



mockup 1

Roadmap: Implementation

Cytoscape Worker API :

04.12 Goal Understand Max Goals To define this Milestone Plan Decide on the Approach to parallelize code Reading List (BenchmarkJS, ParallelJS, Google Dev Tools) Discuss with Max Debug Tools of Cytoscape

Max Comments & Hints: Separate the Graph into separate components (Subgraph components, Subsets of the Graph (Force-directed Layout)) Normalization, Fitting Step in the End to resolve conflicts if subset has connection / edges between Approach Arbor uses (good example to look at). Own Worker API Lot’s of different ways to parallelize things (depends on the layout) Spread Layout using ParallelJS (convert using Cytoscape Worker API) One thread is offload the computation Always measure performance on the same machine! (console, timer, profiling) It would be great to have a automated benchmarking


Unexpected difficulties: synchronization between workers (avoid communication between them in the beginning)

11.12 Basic Program which is parallelised (Nested in Cytoscape) Please mind the Coding Style of Cytoscape Send Graph Data to the Worker Program and back Benchmark the simple program i.e. BenchmarkJS

18.12 Report, work on Spread Layout Algorithm. Some functions parallelized if possible.

29.12 (Monday) Run the first Layout Algorithm (pick one algorithm) with multiple cores (parallelized) Test and BenchmarkJS

05.01 (Monday)

08.01 Final Presentation

15.01 Submission Deadline

Source Code


  • PP2_CS_2014 mentors, Björn Grüning (Galaxy) gruening. (at)
  • Students: Kommanapalli Vasantha Kumari,Anuradha Ganapati,Ahsan ZiaUllah

Additional Links