RDataMining slides-network-analysis-with-r
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This document provides an overview of social network analysis using R. It discusses graph construction, visualization, querying, and centrality measures. Various R packages that support social network analysis are also presented, including igraph for network analysis and visualization, and visNetwork for interactive visualization. Finally, further readings and online resources on social network analysis and related R packages are listed.
![Graph Query
## nodes
V(g)
## + 8/8 vertices, named, from 8dfec3f:
## [1] Tom Ben Bob John Mary Alice Wendy David
## edges
E(g)
## + 7/7 edges from 8dfec3f (vertex names):
## [1] Tom --John Ben --John Bob --John John --Mary
## [5] Mary --Alice Mary --Wendy Wendy--David
## immediate neighbors (friends) of John
friends <- ego(g,order=1,nodes="John",mindist=1)[[1]] %>% print()
## + 4/8 vertices, named, from 8dfec3f:
## [1] Tom Ben Bob Mary
## female friends of John
friends[friends$gender == "F"]
## + 1/8 vertex, named, from 8dfec3f:
## [1] Mary
12 / 37](https://image.slidesharecdn.com/rdm-slides-network-analysis-with-r-190703041149/85/RDataMining-slides-network-analysis-with-r-12-320.jpg)
![Graph Query (cont.)
## 1- and 2-order neighbors (friends) of John
g2 <- make_ego_graph(g, order=2, nodes="John")[[1]]
g2 %>% plot(vertex.size=30, vertex.color=colors)
Tom
Ben
Bob
John
Mary
Alice
Wendy
13 / 37](https://image.slidesharecdn.com/rdm-slides-network-analysis-with-r-190703041149/85/RDataMining-slides-network-analysis-with-r-13-320.jpg)
![Centrality Measures (cont.)
Eigenvector centrality: the values of the first eigenvector of
the graph adjacency matrix
Transivity, a.k.a clustering coefficient, measures the
probability that the adjacent nodes of a node are connected.
eigenvector <- evcent(g)$vector %>% round(2) %>% print()
## Tom Ben Bob John Mary Alice Wendy David
## 0.45 0.45 0.45 1.00 0.85 0.38 0.48 0.22
transitivity <- g %>% transitivity(type = "local") %>% print()
## [1] NaN NaN NaN 0 0 NaN 0 NaN
17 / 37](https://image.slidesharecdn.com/rdm-slides-network-analysis-with-r-190703041149/85/RDataMining-slides-network-analysis-with-r-17-320.jpg)
![Build a Graph
head(nodes, 3)
## name type
## 1 T9 tid
## 2 T24 tid
## 3 T13 tid
head(edges, 3)
## from to
## 1 T9 P27
## 2 T24 P8
## 3 T13 P2
## buid a graph object
g <- graph.data.frame(edges, directed = F, vertices = nodes)
g
## IGRAPH 9597c42 UN-B 61 60 --
## + attr: name (v/c), type (v/c)
## + edges from 9597c42 (vertex names):
## [1] T9 --P27 T24--P8 T13--P2 T27--P10 T29--P29 T2 --P27
## [7] T16--P21 T27--P20 T17--P30 T14--P20 T29--P22 T14--P17
## [13] T21--P18 T18--P9 T4 --P5 T9 --A29 T24--A28 T13--A21 23 / 37](https://image.slidesharecdn.com/rdm-slides-network-analysis-with-r-190703041149/85/RDataMining-slides-network-analysis-with-r-23-320.jpg)
![Online Resources
Book titled R and Data Mining: Examples and Case
Studies [Zhao, 2012]
http://www.rdatamining.com/docs/RDataMining-book.pdf
R Reference Card for Data Mining
http://www.rdatamining.com/docs/RDataMining-reference-card.pdf
Free online courses and documents
http://www.rdatamining.com/resources/
RDataMining Group on LinkedIn (27,000+ members)
http://group.rdatamining.com
Twitter (3,300+ followers)
@RDataMining
34 / 37](https://image.slidesharecdn.com/rdm-slides-network-analysis-with-r-190703041149/85/RDataMining-slides-network-analysis-with-r-34-320.jpg)
RDataMining slides-network-analysis-with-r
- 1. Social Network Analysis with R ∗ Yanchang Zhao http://www.RDataMining.com R and Data Mining Course Beijing University of Posts and Telecommunications, Beijing, China July 2019 ∗ Chapter 11: Social Network Analysis, in R and Data Mining: Examples and Case Studies. http://www.rdatamining.com/docs/RDataMining-book.pdf 1 / 37
- 2. Contents Graph and Social Network Analysis Graph Construction Graph Visualization Graph Query Centrality Measures Advanced Graph Visualization R Packages Wrap Up Further Readings and Online Resources 2 / 37
- 3. Network and Graph Nodes, vertices or entities Edges, links or relationships Network analysis, graph mining Link prediction, community/group detection, entity resolution, recommender system, information propogation modeling 3 / 37
- 4. Graph Databases Neo4j: https://neo4j.com Giraph on Hadoop: http://giraph.apache.org GraphX on Spark: http://spark.apache.org/graphx/ 4 / 37
- 5. Social Network Analysis Graph construction Graph query Centrality measures Graph visualization Clustering and community detection 5 / 37
- 6. Contents Graph and Social Network Analysis Graph Construction Graph Visualization Graph Query Centrality Measures Advanced Graph Visualization R Packages Wrap Up Further Readings and Online Resources 6 / 37
- 7. Graph Construction Tom, Ben, Bob and Mary are friends of John. Alice and Wendy are friends of Mary. Wendy is a friend of David. library(igraph) # nodes nodes <- data.frame( name = c("Tom","Ben","Bob","John","Mary","Alice","Wendy","David"), gender = c("M", "M", "M", "M", "F", "F", "F", "M"), age = c( 16, 30, 42, 29, 26, 32, 18, 22) ) # relations edges <- data.frame( from = c("Tom", "Ben", "Bob", "Mary", "Alice", "Wendy", "Wendy"), to = c("John", "John", "John", "John","Mary", "Mary", "David") ) # build a graph object g <- graph.data.frame(edges, directed=F, vertices=nodes) 7 / 37
- 8. Contents Graph and Social Network Analysis Graph Construction Graph Visualization Graph Query Centrality Measures Advanced Graph Visualization R Packages Wrap Up Further Readings and Online Resources 8 / 37
- 9. Graph Visualization layout1 <- g %>% layout_nicely() ## save layout for reuse g %>% plot(vertex.size = 30, layout = layout1) Tom Ben Bob John Mary AliceWendy David 9 / 37
- 10. Graph Visualization (cont.) ## use blue for male and pink for female colors <- ifelse(V(g)$gender=="M", "skyblue", "pink") g %>% plot(vertex.size=30, vertex.color=colors, layout=layout1) Tom Ben Bob John Mary AliceWendy David 10 / 37
- 11. Contents Graph and Social Network Analysis Graph Construction Graph Visualization Graph Query Centrality Measures Advanced Graph Visualization R Packages Wrap Up Further Readings and Online Resources 11 / 37
- 12. Graph Query ## nodes V(g) ## + 8/8 vertices, named, from 8dfec3f: ## [1] Tom Ben Bob John Mary Alice Wendy David ## edges E(g) ## + 7/7 edges from 8dfec3f (vertex names): ## [1] Tom --John Ben --John Bob --John John --Mary ## [5] Mary --Alice Mary --Wendy Wendy--David ## immediate neighbors (friends) of John friends <- ego(g,order=1,nodes="John",mindist=1)[[1]] %>% print() ## + 4/8 vertices, named, from 8dfec3f: ## [1] Tom Ben Bob Mary ## female friends of John friends[friends$gender == "F"] ## + 1/8 vertex, named, from 8dfec3f: ## [1] Mary 12 / 37
- 13. Graph Query (cont.) ## 1- and 2-order neighbors (friends) of John g2 <- make_ego_graph(g, order=2, nodes="John")[[1]] g2 %>% plot(vertex.size=30, vertex.color=colors) Tom Ben Bob John Mary Alice Wendy 13 / 37
- 14. Contents Graph and Social Network Analysis Graph Construction Graph Visualization Graph Query Centrality Measures Advanced Graph Visualization R Packages Wrap Up Further Readings and Online Resources 14 / 37
- 16. Centrality Measures Degree: the number of adjacent edges; indegree and outdegree for directed graphs Closeness: the inverse of the average length of the shortest paths to/from all other nodes Betweenness: the number of shortest paths going through a node degree <- g %>% degree() %>% print() ## Tom Ben Bob John Mary Alice Wendy David ## 1 1 1 4 3 1 2 1 closeness <- g %>% closeness() %>% round(2) %>% print() ## Tom Ben Bob John Mary Alice Wendy David ## 0.06 0.06 0.06 0.09 0.09 0.06 0.07 0.05 betweenness <- g %>% betweenness() %>% print() ## Tom Ben Bob John Mary Alice Wendy David ## 0 0 0 15 14 0 6 0 16 / 37
- 17. Centrality Measures (cont.) Eigenvector centrality: the values of the first eigenvector of the graph adjacency matrix Transivity, a.k.a clustering coefficient, measures the probability that the adjacent nodes of a node are connected. eigenvector <- evcent(g)$vector %>% round(2) %>% print() ## Tom Ben Bob John Mary Alice Wendy David ## 0.45 0.45 0.45 1.00 0.85 0.38 0.48 0.22 transitivity <- g %>% transitivity(type = "local") %>% print() ## [1] NaN NaN NaN 0 0 NaN 0 NaN 17 / 37
- 18. Contents Graph and Social Network Analysis Graph Construction Graph Visualization Graph Query Centrality Measures Advanced Graph Visualization R Packages Wrap Up Further Readings and Online Resources 18 / 37
- 19. Static Network Visualization Static network visualization Fast in rendering big graphs For very big graphs, the most efficient way is to save visualization result into a file, instead of directly to screen. Save network diagram into files: pdf(), bmp(), jpeg(), png(), tiff() library(igraph) ## plot directly to screen when graph is small plot(g) ## for big graphs, save visualization to a PDF file pdf("mygraph.pdf") plot(g) graphics.off() ## or dev.off() 19 / 37
- 20. Interactive Network Visualization Coordinates of other nodes are not adjusted when moving a node. Can be slow when rendering big graphs Save network diagram into files: visSave(), visExport() visIgraph(g, idToLabel=T) %>% ## highlight nodes connected to a selected node visOptions(highlightNearest=T) %>% ## use different icons for different types (groups) of nodes visGroups(groupname="person", shape="icon", icon=list(code="f007")) %>% ... %>% ## use FontAwesome icons addFontAwesome() %>% ## add legend of nodes visLegend() %>% ## to save to file visSave(file = "network.html") 20 / 37
- 21. Interactive Network Visualization (cont.) Dynamically adjusting coordinates for better visualization Very slow when rendering big graphs x <- toVisNetworkData(g) visNetwork(nodes=x$nodes, edges=x$edges)%>% ## use different icons for different types (groups) of nodes visGroups(groupname="person", shape="icon", icon=list(code="f007")) %>% ... %>% ## use FontAwesome icons addFontAwesome() %>% ## add legend of nodes visLegend() 21 / 37
- 22. Load Graph Data ## download graph data url <- "http://www.rdatamining.com/data/graph.rdata" download.file(url, destfile = "./data/graph.rdata") library(igraph) # load graph data into R # what will be loaded: g, nodes, edges load("../data/graph.rdata") 22 / 37
- 23. Build a Graph head(nodes, 3) ## name type ## 1 T9 tid ## 2 T24 tid ## 3 T13 tid head(edges, 3) ## from to ## 1 T9 P27 ## 2 T24 P8 ## 3 T13 P2 ## buid a graph object g <- graph.data.frame(edges, directed = F, vertices = nodes) g ## IGRAPH 9597c42 UN-B 61 60 -- ## + attr: name (v/c), type (v/c) ## + edges from 9597c42 (vertex names): ## [1] T9 --P27 T24--P8 T13--P2 T27--P10 T29--P29 T2 --P27 ## [7] T16--P21 T27--P20 T17--P30 T14--P20 T29--P22 T14--P17 ## [13] T21--P18 T18--P9 T4 --P5 T9 --A29 T24--A28 T13--A21 23 / 37
- 24. Example of Static Network Visualization library(igraph) plot(g, vertex.size=12, vertex.label.cex=0.7, vertex.color=as.factor(V(g)$type), vertex.frame.color=NA) T9 T24 T13 T27 T29 T2 T16 T17 T14 T21 T18 T4 P27 P8 P2 P10 P29 P21 P20 P30 P22 P17 P18 P9 P5 A29 A28 A21 A24 A1A15 A23 A7 A10 A5 A13 A12 N5 N7 N14 N8 N26 N2 N24 N4 N17 N23 N27 N12 E20 E3 E12 E9 E25 E14 E24 E23 E19 E22 E1 E15 24 / 37
- 25. Example of Interactive Network Visualization library(visNetwork) V(g)$group <- V(g)$type ## visualization data <- toVisNetworkData(g) visNetwork(nodes=data$nodes, edges=data$edges) %>% visGroups(groupname="tid",shape="icon",icon=list(code="f15c")) %>% visGroups(groupname="person",shape="icon",icon=list(code="f007")) %>% visGroups(groupname="addr",shape="icon",icon=list(code="f015")) %>% visGroups(groupname="phone",shape="icon",icon=list(code="f095")) %>% visGroups(groupname="email",shape="icon",icon=list(code="f0e0")) %>% addFontAwesome() %>% visLegend() 25 / 37
- 26. 26 / 37
- 27. Contents Graph and Social Network Analysis Graph Construction Graph Visualization Graph Query Centrality Measures Advanced Graph Visualization R Packages Wrap Up Further Readings and Online Resources 27 / 37
- 28. R Packages Network analysis: igraph, sna, statnet Network visualization: visNetwork Interface with graph databases: RNeo4j 28 / 37
- 29. Package igraph † V(g), E(g): nodes and edges of graph g degree, betweenness, closeness, transitivity: various centrality scores neighborhood: neighborhood of graph vertices cliques, largest.cliques, maximal.cliques, clique.number: find cliques, ie. complete subgraphs clusters, no.clusters: maximal connected components of a graph and the number of them fastgreedy.community, spinglass.community: community detection cohesive.blocks: calculate cohesive blocks induced.subgraph: create a subgraph of a graph (igraph) read.graph, write.graph: read and writ graphs from and to files of various formats † https://cran.r-project.org/package=igraph 29 / 37
- 30. Contents Graph and Social Network Analysis Graph Construction Graph Visualization Graph Query Centrality Measures Advanced Graph Visualization R Packages Wrap Up Further Readings and Online Resources 30 / 37
- 31. Wrap Up Package igraph and sna Static visualization Can visualize nodes with shapes, images and icons Visualise very large graph Support network analysis and graph mining Package visNetwork Interactive visualization Can visualize nodes with shapes, images and icons Image rendering can be very slow for large graphs Designed for visualization only, and does not support network analysis and graph mining 31 / 37
- 32. Contents Graph and Social Network Analysis Graph Construction Graph Visualization Graph Query Centrality Measures Advanced Graph Visualization R Packages Wrap Up Further Readings and Online Resources 32 / 37
- 33. Further Readings Social network analysis (SNA) https://en.wikipedia.org/wiki/Social_network_analysis igraph – a network analysis package, supporting R, Python and C/C++ http://igraph.org sna – an R package for social network analysis https://cran.r-project.org/web/packages/sna/index.html statnet – software tools for the analysis, simulation and visualization of network data; also available as an R package http://www.statnet.org visNetwork – an R package for network visualization http://datastorm-open.github.io/visNetwork/ 33 / 37
- 34. Online Resources Book titled R and Data Mining: Examples and Case Studies [Zhao, 2012] http://www.rdatamining.com/docs/RDataMining-book.pdf R Reference Card for Data Mining http://www.rdatamining.com/docs/RDataMining-reference-card.pdf Free online courses and documents http://www.rdatamining.com/resources/ RDataMining Group on LinkedIn (27,000+ members) http://group.rdatamining.com Twitter (3,300+ followers) @RDataMining 34 / 37
- 35. The End Thanks! Email: yanchang(at)RDataMining.com Twitter: @RDataMining 35 / 37
- 36. How to Cite This Work Citation Yanchang Zhao. R and Data Mining: Examples and Case Studies. ISBN 978-0-12-396963-7, December 2012. Academic Press, Elsevier. 256 pages. URL: http://www.rdatamining.com/docs/RDataMining-book.pdf. BibTex @BOOK{Zhao2012R, title = {R and Data Mining: Examples and Case Studies}, publisher = {Academic Press, Elsevier}, year = {2012}, author = {Yanchang Zhao}, pages = {256}, month = {December}, isbn = {978-0-123-96963-7}, keywords = {R, data mining}, url = {http://www.rdatamining.com/docs/RDataMining-book.pdf} } 36 / 37
- 37. References I Zhao, Y. (2012). R and Data Mining: Examples and Case Studies, ISBN 978-0-12-396963-7. Academic Press, Elsevier. 37 / 37