Network Visualization guest lecture at #DataVizQMSS at @Columbia / #SNA at PUC Chile

Network Visualiza0on + Gephi Tutorial Denis Parra, Ph.D. Assistant Professor Pon0ﬁcal Catholic University of Chile MPGI ~ Master Program Monday, October 5th, 2015

Expecta0ons •  What will you learn at the end of this class? 1.  Basic concepts of Networks, Graphs, and Social Network Analysis (SNA) 2.  Systems/Applica0ons that make use of network visualiza0ons 3.  Recent Research on Network Visualiza0on 4.  How to use a Network Visualiza0on and Analysis tool (Gephi) ~ in class tutorial 5.  Bonus : Where do I ﬁnd data sets to do more cool visualiza0ons? 10/5/15 @denisparra | 2

1. Basic Concepts and Deﬁni0ons

We live in a connected world •  … and we need visualiza0on models to represent networks such as: –  Online Social networks: Facebook, Twiaer ~ people connected online –  Informa3on networks: WWW ~ web pages connected through hyperlinks –  Computer networks: The internet ~ computers and routers connected through wired/wireless connec0ons •  What is a network? (Easley and Kleinberg, 2011) “a network is any collec0on of objects in which some pairs of these objects are connected by links”. 10/5/15 @denisparra | 4

A bit of history: Graph models •  Around 1735, the mathema0cian Venn Euler set the founda0on for graph theory by crea0ng a model to represent the problem of the “7 bridges of Königsberg” Source: hap://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg and “Linked” by A-‐L. Barabasi 10/5/15 @denisparra | 5

A formal deﬁni0on of Graph •  Based on (Easley and Kleinberg, 2011): A graph is a way of specifying rela.onships among a collec.on of items. A graph consists of a set of objects, called nodes, with certain pairs of these objects connected by links called edges. 10/5/15 @denisparra | 6

Graphs as Models of Networks •  Based on (Easley and Kleinberg, 2011): “Graphs are useful because they serve as mathema0cal models of network structures.” •  But keep in mind: Graphs are only one way to represent networks (though the most popular) { arc , link , edge } { node , vertex } Source: L. Adamic SNA class @coursera 10/5/15 @denisparra | 7

The Historic Development of Network Visualiza0on •  The following slides are based on the work of Pfeﬀer and Freeman (2015) Pfeﬀer, Juergen & Freeman, Lin. Methods of Social Network VisualizaAon. Encyclopedia of Complexity and Systems Science, 2nd EdiAon, Springer Reference. •  They categorize this historic development on three categories: 1.  Nodes, Links, Shape, Size 2.  Substance-‐Based Layout 3.  Two-‐Mode Networks 10/5/15 @denisparra | 8

Overall View of the Visualiza0ons Reference: Pfeﬀer, Juergen & Freeman, Lin (forthcoming). Methods of Social Network Visualiza0on. Encyclopedia of Complexity and Systems Science, 2nd Edi0on, Springer Reference. hap://www.pfeﬀer.at/ data/visposter/ @denisparra | 9

1. Nodes, Links, Shape, Size (1/2) @denisparra | 10

1. Nodes, Links, Shape, Size (2/2) @denisparra | 11

2. Substance-‐Based Layout (1/2) @denisparra | 12

2. Substance-‐Based Layout (2/2) @denisparra | 13

3. Two-‐mode Networks (1/2) @denisparra | 14

3. Two-‐mode Networks (2/2) @denisparra | 15

The tennis players’ social network Sharonpova Sharapova Serena Li Na Rafa Djoker Soderling Prof. Parra Roger 10/5/15 @denisparra | 16

Some Types of Networks Undirected (Facebook friendships) Directed (TwiAer following) mul3mode (Amazon user-‐ product) Weighted (Facebook likes) and more …. 9 3 •  Hereinauer, I will refer indis0nc0vely to graphs and networks. Here some types: 10/5/15 @denisparra | 17

Analyzing a network: SNA •  How do we analyze a network? •  How do we compare diﬀerent networks? •  This class is about network visualiza0ons, but some founda0onal concepts of SNA need to be understood before. •  Let’s see ways to describe the network at local and at global level Source: hap://moviegalaxies.com 10/5/15 @denisparra | 18

Measures in SNA Node-‐level metrics •  Centrality –  (In/Out) Degree –  Betweenness –  Closeness –  Eigenvector •  Clustering coeﬃcient Graph-‐level metrics •  Size •  Diameter (longest path) •  Average path length •  Average [node metric] 10/5/15 @denisparra | 19 •  These are only a few representa0ve measures •  For further understanding of these measures: See the presenta0on of Giorgos Chelo0s in slideshare, from slide 8 hap://www.slideshare.net/gchelio0s/social-‐network-‐analysis-‐3273045

Interpreta0on of measures 10/5/15 @denisparra | 20 Source: hap://www.slideshare.net/gchelio0s/social-‐network-‐analysis-‐3273045 slide 24 Interpreta3on in Social Networks Degree How many people can this person reach directly? Betweenness How likely is this person to be the most direct route between two people in the network?

Interpreta0on of measures 10/5/15 @denisparra | 21 Source: hap://www.slideshare.net/gchelio0s/social-‐network-‐analysis-‐3273045 slide 24 Interpreta3on in Social Networks Closeness How fast can this person reach everyone in the network? Eigenvector How well is this person connected to other well-‐connected people?

Two more concepts… •  Total possible number of edges in a network #edges = n * (n -‐1 ) /2 (undirected network) #edges = n * (n -‐1 ) (directed network) •  (Shortest) Path: the shortest sequence of edges to be followed to reach a node B from a node A in a network. Which is the length of the shortest path between Rafa Nadal and Sharonpova? 10/5/15 @denisparra | 22

Prac0ce the learned concepts… •  Prac0ce the learned concepts comparing these 2 movie networks (characters’ interac0ons) : Traﬃc (2000) Forrest Gump (1994) Source: hap://moviegalaxies.com 10/5/15 @denisparra | 23

Forrest Gump (1994) Network metrics: •  Size: 94/271 •  Density: 0.06 •  Diameter: 4 •  Clustering coefficient: 0.8 •  Avg. Path Length: 1.99 Node metrics: Forrest •  Degree: 89 •  Betweetnness: 3453.8 Abbie Hoffman •  Degree: 6 •  Betweenness: 0 hap://moviegalaxies.com/movies/316-‐Forrest-‐Gump Abbie Hoffman 10/5/15 @denisparra | 24

Traffic (2000) Network metrics: •  Size: 68 •  Density: 0.04 •  Diameter: 7 •  Clustering coefficient: 0.55 •  Avg. Path Length: 3.54 Node metrics: Robert •  Degree: 24 •  Betweetnness: 1437.7 Francisco •  Degree: 5 •  Betweenness: 1031 hap://moviegalaxies.com/movies/837-‐Traffic * Francisco: is a bridge (structural holes) 10/5/15 @denisparra | 25

Network Components •  (from G. Chelio0s) “many large groups and online communi0es have a core of densely connected users … and a much larger periphery” •  Source: hap://www.slideshare.net/gchelio0s/social-‐network-‐analysis-‐3273045, page 34 •  (from L. Adamic) “if the largest component encompasses a significant fraction of the graph, it is called the giant component” •  Source: haps://class.coursera.org/sna-‐2012-‐001/class/index , week 1 slides 10/5/15 @denisparra | 26

Remarks and Further topics in SNA •  With the concepts already described, we will aaempt to visualize and analyze two networks in the NodeXL & Gephi tutorial. •  Not covered in this class, but worth men0oning other SNA topics: –  Network growth/forma0on: Erdős–Rényi, Waas-‐Strogatz, Barabassi-‐Albert (preferen0al aaachment) –  Community Structure: Girvan-‐Newman, Clauset-‐Moore-‐ Newman (max-‐modularity), aﬃnity propaga0on, etc. –  Processes in networks: Diﬀusion, epidemics, innova0on, etc. –  Network mo0fs: small subgraphs that are over-‐represented in the network 10/5/15 @denisparra | 27

2. Applica0ons

Examples of Applica0ons •  These are a few examples of applica0ons that make use of Network Visualiza0ons: –  Truthy –  Moviegalaxies –  Poderopedia –  TwiaerScope –  LinkedIn Maps •  These ARE NOT tools for generic Visualiza0on and Analysis (we’ll see those in the tutorial sec0on) 10/5/15 @denisparra | 29

Truthy •  Informa0on Diﬀusion research at Indiana U. •  hap://truthy.indiana.edu 10/5/15 @denisparra | 30

MovieGalaxies •  Visualize an discuss the characters of movies as networks •  hap://moviegalaxies.com 10/5/15 @denisparra | 31

Poderopedia •  Who is who in business and poli0cs in Chile? •  Knight Founda0on: Top 10 digital tools for journalists (Feb 4, 2013) hap://www.knigh•ounda0on.org/blogs/knightblog/2013/2/4/new-‐ digital-‐tools-‐journalists-‐10-‐learn/ 10/5/15 @denisparra | 32

TwiaerScope •  A visual monitor of tweets in real 0me. This is an enhanced graph model. •  hap://0bes0.research.aa.com/twiaerscope/ 10/5/15 @denisparra | 33

LinkedIn Maps •  Explore your LinkedIn contact network •  hap://inmaps.linkedinlabs.com/network 10/5/15 @denisparra | 34

3. Recent Research

Recent Research (~by Feb 2013) •  Can we go Beyond the Graph? •  ManyNets •  HivePlots •  Orion •  GraphPrism •  Mo0f Simpliﬁca0ons •  GeoSpa0al Network Visualiza0on 10/5/15 @denisparra | 36

Social Network Visualiza0on: Can we go Beyond the Graph? (2006) •  Authors support that social network visualiza0on for end users should go beyond the graph-‐only paradigm •  hap://web.media.mit.edu/~fviegas/papers/viegas-‐cscw04.pdf 10/5/15 @denisparra | 37

ManyNets (2010) •  Analyze and compare mul0ple networks •  hap://www.cs.umd.edu/hcil/manynets/ 10/5/15 @denisparra | 38

Hive Plots (2011) •  Hive plots—ra0onal approach to visualizing networks hap://www.hiveplot.net/ 10/5/15 @denisparra | 39

Orion (2011) •  Diﬀerent visualiza0ons to present network data •  hap://vis.stanford.edu/papers/orion a) Sorted matrix b) Node-‐link diagram c) Plot of betweenness for two networks 10/5/15 @denisparra | 40

GeoSpa0al Network Visualiza0on (2011) •  Interac0ve Explora0on of Geospa0al Network Visualiza0on •  hap://0llnagel.com/2011/10/interac0ve-‐ explora0on-‐of-‐geospa0al-‐network-‐ visualiza0on/ 10/5/15 @denisparra | 41

GraphPrism (2012) •  GraphPrism: Compact Visualiza0on of Network Structure, inspired in B-‐Matrices •  hap://vis.stanford.edu/papers/graphprism 10/5/15 @denisparra | 42

Mo0f Simpliﬁca0on (2012) •  Use of fans and parallel glyphs to improve readibility •  hap://hcil2.cs.umd.edu/trs/2012-‐11/2012-‐11.pdf 10/5/15 @denisparra | 43

MuxViz: Mul0layer Networks (2014) •  Mul0layer analysis and visualiza0on of networks •  hap://muxviz.net/index.php 10/5/15 @denisparra | 44

4. Using a Network Visualiza0on Tool (NodeXL & Gephi in a nutshell)

Network Analysis and Visualiza0on Tools •  NodeXL •  Gephi •  Pajek •  ORA (CMU) •  igraph (C++, R) •  UCINet •  NetworkX •  Tulip •  Visone •  Larger list: hap://www.gmw.rug.nl/~huisman/sna/ souware.html 10/5/15 @denisparra | 46

How do I format my network data? •  Depends on your informa0on needs. What do you want to describe? – GDF hap://guess.wikispot.org/The_GUESS_.gdf_format – GEXF hap://gexf.net/format/ – GraphML hap://graphml.graphdrawing.org – Pajek Net format hap://vlado.fmf.uni-‐lj.si/pub/networks/pajek/doc/pajekman.pdf – CSV haps://gephi.org/users/supported-‐graph-‐formats/csv-‐format/ •  For a summary and examples, check haps://gephi.org/users/supported-‐graph-‐formats/ 10/5/15 @denisparra | 47

How do I format my Data? 10/5/15 @denisparra | 48

For the rest of my classes •  Will use this as reference for iGraph analysis

Gephi tutorial hap://web.ing.puc.cl/~dparra/classes/sna-‐ INP3460-‐2015-‐2/NetworkViz-‐tutorial-‐ instruc0ons.pdf 10/5/15 @denisparra | 50

Dynamic Network Visualiza0on •  Data: hap://web.ing.puc.cl/~dparra/classes/ sna-‐INP3460-‐2015-‐2/data/ bipar0te_nodes_CSCW2013_URL_0me.gdf •  Hot-‐to-‐visualize: haps://kawinproject.wordpress.com/ 2013/03/10/dynamic-‐data-‐longitudinal-‐ network-‐in-‐gephi/

Final Remarks •  In this class you learnt: –  Basic concepts of networks, graphs, and SNA –  Existent applica0ons that make use of network visualiza0ons –  Research related to network visualiza0on –  How to use a network visualiza0on and analysis tool •  My ﬁnal message: –  Graph model is great, but try to move beyond the graph-‐only visualiza0on. –  Think of ways to create visualiza0ons that help to make sense of the diﬀerent proper0es inherent to the network and to its elements (nodes and links). R and Javascript give you enough power to implement. 10/5/15 @denisparra | 52

Thanks! •  Ques0ons? •  denisparra@gmail.com or @denisparra •  Check my academic web page hap://web.ing.puc.cl/~dparra/ •  and my research blog hap://kawinproject.wordpress.com

5. Bonus Slides

Where do I ﬁnd cool NetVis? •  hap://www.visualcomplexity.com/vc/ Where do I ﬁnd network datasets? •  Jure Leskovec page hap://snap.stanford.edu/data/ •  Mark Newman’s page hap://www-‐personal.umich.edu/~mejn/netdata/ •  Gephi wiki datasets hap://wiki.gephi.org/index.php/Datasets •  From CMU’s Graphlab hap://graphlab.org/downloads/datasets/ 10/5/15 @denisparra | 55

Recommended books •  Linked by Albert L. Barabasi •  Networks, Crowds, and Markets by D. Easley and J. Kleinberg (pre-‐print available free online) 10/5/15 @denisparra | 56

Recommended Online Tutorials •  Gephi: – At ICWSM ‘11 hap://www.slideshare.net/Cloud/sp1-‐ exploratory-‐network-‐analysis-‐with-‐gephi – Gephi online tutorial hap://blog.ouseful.info/ 2012/11/09/drug-‐deal-‐network-‐analysis-‐with-‐ gephi-‐tutorial/# – Lada Adamic 2012 SNA class: hap://www.youtube.com/watch? v=JgDYV5ArXgw&list=PL828B49781EAA17ED 10/5/15 @denisparra | 57

•  Do you R? – Temporal networks with igraph and R (with 20 lines of code!) hap://markov.uc3m.es/2012/11/temporal-‐ networks-‐with-‐igraph-‐and-‐r-‐with-‐20-‐lines-‐of-‐ code/ 10/5/15 @denisparra | 58

LineSets (InfoVis 2011) •  Alper et al. (UCSB and Microsou research) •  Extend a concept from subway maps to sets of items 10/5/15 @denisparra | 59

Denis Parra’s Research •  Using networks vis. in recommenda0on approaches: “Visualizing Recommenda3ons to Support Explora3on, Transparency and Controllability” by Verbert, Parra, Brusilovsky and Duval, IUI Conference (2013).

Denis Parra’s Research •  Plo‡ng edges’ weight distribu0ons of several networks to compare community explain algorithms performance 10/5/15 @denisparra | 61

Denis Parra’s Research •  Twiaer in academic events: A study of temporal usage, communica0on, sen0mental and topical paaerns in 16 Computer Science conferences ( hap://dx.doi.org/10.1016/j.comcom. 2015.07.001 )