Question: Library / Module For Network Analysis
13
gravatar for Khader Shameer
9.0 years ago by
Manhattan, NY
Khader Shameer18k wrote:

I would like to know what libraries/modules are used by BioStar members for large scale analysis of networks (for example PPI, DDI etc). I am looking for a module that can provide various functions to calculate network algorithms and network properties. Since I have to deal with thousands of networks, Cytoscape via GUI will not be a feasible option. I think a command line version of version of the Cytoscape plugin NetworkAnalyzer will be a good start.

I know about Bio::Network at Bioperl, but the functionality is very limited. Networkx have some of the implementation of network algorithms from a graph theory perspective, but not as extensive as in NetworkAnalyzer.

Looking forward for your suggestions.

network • 4.3k views
ADD COMMENTlink modified 10 months ago by RamRS22k • written 9.0 years ago by Khader Shameer18k
18
gravatar for Neilfws
9.0 years ago by
Neilfws48k
Sydney, Australia
Neilfws48k wrote:

I like igraph. It's available as an R package, a Ruby gem, a Python extension or a C library. It reads/writes most of the common network formats and provides a good selection of network statistics - see for example the R documentation.

I wrote a very basic getting started tutorial - it focuses on social network data, but should easily adapt to any kind of network.

For visualisation I prefer Gephi to Cytoscape (although they are both RAM-guzzling Java monsters). I believe they are developing an API but it's primarily a visualisation GUI at the moment.

ADD COMMENTlink modified 10 months ago by RamRS22k • written 9.0 years ago by Neilfws48k

Thanks Neil. igraph looks like a good start for me. Your tutorial will be really useful.

ADD REPLYlink written 9.0 years ago by Khader Shameer18k

Yesterday I heard from Gephi regarding their API : http://twitter.com/kshameer/status/18101377504

ADD REPLYlink written 9.0 years ago by Khader Shameer18k
9
gravatar for Istvan Albert
9.0 years ago by
Istvan Albert ♦♦ 80k
University Park, USA
Istvan Albert ♦♦ 80k wrote:

For large graph analysis I can fully recommend LEDA (library for efficient algorithms). It is written in C++ but provides a very high level interface that looks almost like a scripting language. Those with knowledge of how to program in any language can pick it up in hours. The library is very well optimized and has great performance.

For example here is a code fragment demonstrating both creating a graph and then calling the single source shortest path algorithm with the Dijkstra method:

#include [HTML]
#include [HTML]
#include [HTML]

int main() {

  graph G; 

  // generate the graph
  node n0=G.new_node(); node n1=G.new_node();
  node n2=G.new_node(); node n3=G.new_node();

  edge e0=G.new_edge(n0,n1); edge e1=G.new_edge(n0,n2);
  edge e2=G.new_edge(n1,n2); edge e3=G.new_edge(n1,n3);
  edge e4=G.new_edge(n2,n3); edge e5=G.new_edge(n3,n1);

  // create the edge costs     
  edge_array[HTML] cost(G);
  cost[e0]=1; cost[e1]=2; cost[e2]=3;
  cost[e3]=4; cost[e4]=5; cost[e5]=6;

  DIJKSTRA_T(G,n0,cost,dist,pred);

  node v;
  forall_nodes(v,G) {
    G.print_node(v);
    if (v==n0) 
    std::cout << " was source node." << std::endl;
    else 
    if (pred[v]==nil) 
      std::cout << " is unreachable." << std::endl;
      else {
        std::cout << " " << dist[v] << " "; 
        G.print_edge(pred[v]);
        std::cout << std::endl;
      }
  }
}
ADD COMMENTlink modified 10 months ago by RamRS22k • written 9.0 years ago by Istvan Albert ♦♦ 80k
4
gravatar for Larry_Parnell
7.7 years ago by
Larry_Parnell16k
Boston, MA USA
Larry_Parnell16k wrote:

I recently learned about WGCNA. This is an R package for weighted correlation network analysis. It looks rather interesting and useful and is something I plan to implement. This R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.

ADD COMMENTlink modified 10 months ago by RamRS22k • written 7.7 years ago by Larry_Parnell16k
3
gravatar for Russh
8.4 years ago by
Russh1.2k
U. Liverpool
Russh1.2k wrote:

If you want to do network analysis on a really large scale (ie, analysing many different networks), I can't imagine R or cytoscape would be particularly good options. The former haeorrhages memory and the latter isn't (easily?) scriptable and the plugins aren't interoperable.

I'd recommend JUNG, which has loads of network analysis algorithms already coded up. JUNG also has graph IO modules etc. It's probably not perfect for what you want, because you'll have to stitch modules together yourself. However, they're a very helpful bunch.

R

ADD COMMENTlink modified 10 months ago by RamRS22k • written 8.4 years ago by Russh1.2k
3
gravatar for Marcin Cieslik
8.4 years ago by
Marcin Cieslik520 wrote:

I would suggest Gremlin. It is a domain specific language (DSL) to analyze (think algorithms) and explore (think traverse) graphs originally influenced by XPath. The implementation is superb and the community vibrant. Gremlin sits on top of Blueprints which is a Graph API that "wraps" various types of graph providers e.g. a OpenRDF Sail or the highly scalable Neo4J graph database, and provides implementations of other Java Graph APIs e.g. Jung. This gives great flexibility - if your data grows just switch the backend.

ADD COMMENTlink modified 10 months ago by RamRS22k • written 8.4 years ago by Marcin Cieslik520
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