Claire McWhite @clairemcwhite 4/25/18 A quick intro to networks 

Networks are …  First a data structure  Second a data visualization 

A minimal network Node A Node B Edge A-B node1 node2 A B Data table = 

Edges can have attributes node1 node2 weight relationship direction A B 5 red E A C 2 red E A D 5 blue F B C 1 blue EF C D 5 blue EF B A C D 

Nodes can have attributes node size color A 1 red B 2 red C 3 blue D 4 blue B A C D 

D Network layout algorithms node1 node2 A B A C A D B C C D B A C Network data structure text file Visualized network Network layout algorithm Calculates node coordinates Not very interpretable by humans Ideally more interpretable by humans 

Introduction to ggraph: Layouts – Thomas Lin Pederson https://www.data-imaginist.com/2017/ggraph-introduction-layouts/ 

Hierarchical layouts Introduction to ggraph: Layouts – Thomas Lin Pederson https://www.data-imaginist.com/2017/ggraph-introduction-layouts/ Yfiles circular layouts 

I use mostly force-directed layouts 

node1 node2 weight A B 1 B C 10 A B C A B C A B C 1 10 Edges act as springs in a force-directed layout 

CC-BY martinandjean.ch 2016 

CC-BY martinandjean.ch 2016 

There is a sweet spot of network size for most network layout algorithms. Then you enter the hairball zone https://www.systemsbiology.org/news/2012/12/19/combing-the-hairball/ 

When networks get too large, they may become uninterpretable hairballs 

Steps to break up hairball networks Apply a threshold for edge weights. ------------------------ Thresholding ------------------- 

Steps to break up hairball networks Apply a clustering algorithm to network data ------------------------ Clustering ------------------- 

My data: No clustering 10 million edge, 1 million node network, Large Graph Layout (LGL) 

MCL clustering strength 3 

MCL clustering strength 4 

MCL clustering strength 5 

MCL clustering strength 8 

Software  R – ggraph/tidygraph  Python – NetworkX  Gui -Cytoscape (lots of tools for biology)  Gui – Gephi  Gui - Graphviz 

Large graph layout - LGL Protein homology graph – Edward Marcotte and Alex Adai - MOMA • My fav • Minimizes hairballness of larger networks • Scalable (handles over 1 million node networks) Adai 2004, LGL: creating a map of protein function with an algorithm for visualizing very large biological networks. 

Post-talk addendum  It would be great if someone could make a LGL network layout algorithm plugin for cytoscape or any network visualization software.  The program currently only runs on Linux  https://github.com/TheOpteProject/LGL  My user guide -> http://clairemcwhite.github.io/lgl-guide/  Original paper describing algorithm: https://www.sciencedirect.com/science/article/pii /S0022283604004851?via%3Dihub 

If your data looks like this at all, throw it in a network layout. node1 node2 A B A C A D B C C D (but don’t overinterpret any one layout, and remember that network layouts are fickle and minor changes to thresholds and data input choices can change everything)