On visualization of (social) networks Networks Approaches - PowerPoint PPT Presentation

Social networks visualization V. Batagelj On visualization of (social) networks Networks Approaches Statistics Drawing of Vladimir Batagelj large graphs Drawing of dense graphs IMFM Ljubljana and IAM UP Koper Network decomposition Big Data Visual Analytics Layouts Shonan meeting – November 8-11, 2015 Problems References V. Batagelj Social networks visualization

Outline Social 1 Networks networks visualization 2 Approaches V. Batagelj 3 Statistics 4 Drawing of large graphs Networks 5 Drawing of dense graphs Approaches 6 Network decomposition Statistics 7 Layouts Drawing of large graphs 8 Problems Drawing of 9 References dense graphs Network decomposition Layouts Vladimir Batagelj : Problems vladimir.batagelj@fmf.uni-lj.si References Current version of slides (November 8, 2015, 05 : 13): http://vlado.fmf.uni-lj.si/pub/slides/shonan.pdf V. Batagelj Social networks visualization

Networks Social networks A network N = ( V , L , P , W ) visualization is based on two sets – a set of V. Batagelj nodes (vertices) V , that repre- Networks sent the selected units , and a Approaches set of links (lines) L , that rep- Statistics resent ties between units. They Drawing of determine a graph . A line can large graphs be directed – an arc , or undi- Drawing of rected – an edge ; L = E ∪ A . dense graphs Additional data about nodes or Network decomposition links can be known – the prop- Layouts erties (attributes) P of nodes Alexandra Schuler/ Marion Laging-Glaser: Problems and weights W of links. Analyse von Snoopy Comics References Network = Graph + Data V. Batagelj Social networks visualization

”Countryside” school district Social networks visualization V. Batagelj Only small or sparse networks Networks can be displayed Approaches readably. Statistics On large networks Drawing of large graphs graph drawing algorithms Drawing of dense graphs can reveal their overal Network decomposition structure. Layouts Can we explain the obtained Problems structure? References James Moody (2001) AJS Vol 107, 3,679–716, friendship relation V. Batagelj Social networks visualization

Display of properties – school (Moody) Social networks visualization V. Batagelj Networks Approaches Statistics Drawing of large graphs Drawing of dense graphs Network decomposition Layouts Problems To understand networks we need (additional) data! References V. Batagelj Social networks visualization

Approaches to large networks Social networks From algorithmic complexity analysis: for dealing with large data visualization (millions of units) we are limited to subquadratic algorithms. V. Batagelj Most of large networks are sparse – Dunbar’s number. Networks Approaches Approaches to large networks analysis and visualization: Statistics • statistics; Drawing of large graphs • important parts: Drawing of dense graphs • decomposition; Network • identification of important elements and substructures. decomposition Layouts Visualization: initial network exploration, reporting results, story Problems telling. References For additional details see [4, 3, 5]. V. Batagelj Social networks visualization

EAT all-degree distribution Social networks EAT all−degree distribution visualization V. Batagelj 5000 ● ● ● Networks ● ● Approaches ● 500 ● ● Statistics ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Drawing of ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● large graphs freq ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Drawing of ● ● ● ● ● ● ● ● ● ● ● ● dense graphs ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10 ● ● ● ● Network ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● decomposition ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Layouts ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Problems 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● References 1 5 10 50 100 500 deg V. Batagelj Social networks visualization

in/out-degree distributions Social networks in−degree distribution out−degree distribution visualization 500 ● ● ● ● ● ● ● ● ● ● ● ● ● V. Batagelj ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 200 ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 100 ● Networks 10000 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ●● Approaches ● ● ● ● ● ● ● ● ● ● ● ● outFreq ● ● ● ● ● inFreq ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● Statistics ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 100 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Drawing of ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● large graphs ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Drawing of ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● dense graphs 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● 1 5 10 50 100 500 1 5 10 50 500 5000 Network decomposition inDeg outDeg Layouts The in-degree distribution is ”scale-free”-like. The parameters can be determined using the package of Clauset, Shalizi and Newman. See also Problems Stumpf, et al.: Critical Truths About Power Laws. References It is important to consider the direction of links! V. Batagelj Social networks visualization

VOSviewer / journals Social networks visualization V. Batagelj Networks Approaches Statistics Drawing of large graphs Drawing of dense graphs Network decomposition Layouts Problems links → projection of nodes References There are many algorithms for drawing large graphs and networks [11] (Brandes and Pich – MDS [8]; VOSviewer - level of detail, zooming, contours [17]). V. Batagelj Social networks visualization

VOSviewer Social networks visualization V. Batagelj Networks Approaches Statistics Drawing of large graphs Drawing of dense graphs Network decomposition Layouts Problems References zooming V. Batagelj Social networks visualization