6 - Graph Metrics and Analysis
In this document we will practice various types of network analyses using an edgelist of dolphins
Exercise 1 - Basic graph summary metrics
# Download the dolphin dataset from the URL provided
url <- "https://ona-book.org/data/dolphins.csv"
dolphin_edgelist <- read.csv(url)
# look at the data to see what you're dealing with# create an undirected graph
library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## uniondolphin_graph <- igraph::graph_from_data_frame(
dolphin_edgelist,
directed = FALSE
)# View the graph object - how many vertices and edges are there?
dolphin_graph## IGRAPH b67d85b UN-- 62 159 --
## + attr: name (v/c)
## + edges from b67d85b (vertex names):
## [1] CCL --Double DN16 --Feather DN21 --Feather
## [4] Beak --Fish Bumper --Fish DN16 --Gallatin
## [7] DN21 --Gallatin Feather --Gallatin Beak --Grin
## [10] CCL --Grin Beak --Haecksel Grin --Hook
## [13] Beescratch--Jet DN21 --Jet Feather --Jet
## [16] Gallatin --Jet Haecksel --Jonah Beescratch--Knit
## [19] DN63 --Knit Double --Kringel Hook --Kringel
## [22] Jonah --Kringel Jonah --MN105 Jet --MN23
## + ... omitted several edges# what is the distance between Zig and Fork
distances(dolphin_graph, weights = NULL)["Zig","Fork"]## [1] 7# Is this a dense network?
edge_density(dolphin_graph)## [1] 0.0840825# How many components are in this network?
components(dolphin_graph)## $membership
## CCL DN16 DN21 Beak Bumper Feather
## 1 1 1 1 1 1
## Grin Beescratch Gallatin Haecksel DN63 Double
## 1 1 1 1 1 1
## Hook Jonah Jet Mus Notch Kringel
## 1 1 1 1 1 1
## Fish MN105 MN83 Knit Oscar Fork
## 1 1 1 1 1 1
## Scabs Patchback MN60 Shmuddel SN100 SN4
## 1 1 1 1 1 1
## PL SN63 Stripes SN96 TR120 Topless
## 1 1 1 1 1 1
## Cross Five TR99 SN9 SN90 Trigger
## 1 1 1 1 1 1
## SN89 TR82 Upbang Ripplefluke TSN83 MN23
## 1 1 1 1 1 1
## Number1 Quasi SMN5 Thumper TR77 TR88
## 1 1 1 1 1 1
## TSN103 Vau Wave Web Whitetip Zap
## 1 1 1 1 1 1
## Zig Zipfel
## 1 1
##
## $csize
## [1] 62
##
## $no
## [1] 1# What does this mean for our network?
# we have a complete graph where all dolphins are somehow connected# What is the network diameter? What does this mean?
diameter(dolphin_graph)## [1] 8# how large is the largest clique?
clique_num(dolphin_graph)## [1] 5# how many cliques are between size 3 and 5?
length(cliques(dolphin_graph, min = 3, max = 5))## [1] 125Exercise 2 - Centrality measures
# Continuing with our same dolphin graph, let's calculate centrality metrics.
# HINT: try using the tidygraph package
cent <-
dolphin_graph |>
tidygraph::as_tbl_graph() |>
dplyr::mutate(
DEGREE_CENT = tidygraph::centrality_degree(),
BTWN_CENT = tidygraph::centrality_betweenness(),
CLOSE_CENT = tidygraph::centrality_closeness()
)## Warning in betweenness(graph = graph, v = V(graph), directed = directed, :
## 'nobigint' is deprecated since igraph 1.3 and will be removed in igraph 1.4# Which dolphin is an important connector? (SN100)
cent %>%
dplyr::arrange(
desc(BTWN_CENT)
)## # A tbl_graph: 62 nodes and 159 edges
## #
## # An undirected simple graph with 1 component
## #
## # Node Data: 62 x 4 (active)
## name DEGREE_CENT BTWN_CENT CLOSE_CENT
## <chr> <dbl> <dbl> <dbl>
## 1 SN100 7 454. 0.00685
## 2 Beescratch 8 390. 0.00610
## 3 SN9 8 262. 0.00662
## 4 SN4 11 254. 0.00654
## 5 DN63 5 216. 0.00599
## 6 Jet 9 209. 0.00508
## # … with 56 more rows
## #
## # Edge Data: 159 x 2
## from to
## <int> <int>
## 1 31 48
## 2 32 44
## 3 26 32
## # … with 156 more rows# Which dolphin has the most connections? (Grin)
cent %>%
dplyr::arrange(
desc(DEGREE_CENT)
)## # A tbl_graph: 62 nodes and 159 edges
## #
## # An undirected simple graph with 1 component
## #
## # Node Data: 62 x 4 (active)
## name DEGREE_CENT BTWN_CENT CLOSE_CENT
## <chr> <dbl> <dbl> <dbl>
## 1 Grin 12 113. 0.00617
## 2 SN4 11 254. 0.00654
## 3 Topless 11 74.4 0.00568
## 4 Scabs 10 105. 0.00599
## 5 Trigger 10 155. 0.00541
## 6 Jet 9 209. 0.00508
## # … with 56 more rows
## #
## # Edge Data: 159 x 2
## from to
## <int> <int>
## 1 23 42
## 2 14 37
## 3 14 21
## # … with 156 more rows# Which dolphin is the most central? (SN100)
cent %>%
dplyr::arrange(
desc(CLOSE_CENT)
)## # A tbl_graph: 62 nodes and 159 edges
## #
## # An undirected simple graph with 1 component
## #
## # Node Data: 62 x 4 (active)
## name DEGREE_CENT BTWN_CENT CLOSE_CENT
## <chr> <dbl> <dbl> <dbl>
## 1 SN100 7 454. 0.00685
## 2 SN9 8 262. 0.00662
## 3 SN4 11 254. 0.00654
## 4 Kringel 9 188. 0.00641
## 5 Grin 12 113. 0.00617
## 6 Beescratch 8 390. 0.00610
## # … with 56 more rows
## #
## # Edge Data: 159 x 2
## from to
## <int> <int>
## 1 10 37
## 2 50 56
## 3 46 50
## # … with 156 more rowslibrary(ggraph)## Loading required package: ggplot2# plot the dolphin graph and locate Grin and SN100 to see if these results make intuitive sense
V(dolphin_graph)$highcent <- ifelse(V(dolphin_graph)$name %in% c("SN100","Grin"),1,0)
ggraph::ggraph(dolphin_graph) +
geom_edge_link(color = "grey") +
geom_node_point(aes(color = as.factor(highcent), size = 3), show.legend = FALSE) +
scale_color_manual(values = c("lightblue","pink")) +
geom_node_text(aes(label = name)) +
theme_void()## Using `stress` as default layout
Exercise 3: Community detection
# What communities do you see in the dolphin graph? Try out a couple of methods and compare results
# e.g. cluster_louvain, cluster_leiden, cluster_fast_greedy
set.seed(123)
communities <- cluster_louvain(dolphin_graph)
V(dolphin_graph)$community <- membership(communities)
sizes(communities)## Community sizes
## 1 2 3 4 5
## 5 18 9 18 12# plot your favorite option
set.seed(123)
ggraph(dolphin_graph, layout = "fr") +
geom_edge_link(color = "grey") +
geom_node_point(aes(color = as.factor(community)),
show.legend = FALSE) +
theme_void()
Exercise 4 - Do people tend to email with people in their same department?
# Download the email dataset from the URL provided
url <- "https://ona-book.org/data/email_edgelist.csv"
email_edgelist <- read.csv(url)
# Download the corresponding department mappings from the URL provided
url_v <- "https://ona-book.org/data/email_vertices.csv"
email_dept <- read.csv(url_v)
# look through the edges and vertices to understand the data source# create the graph with vertice labels
email_graph <- igraph::graph_from_data_frame(
email_edgelist,
directed = FALSE,
vertices = email_dept
)
# Calculate assortativity
assortativity_nominal(email_graph, factor(V(email_graph)$dept))## [1] 0.3139728Exercise 5 - Exploring the friends graph (EXTENSION)
# Download the friends dataset from the URL provided
url <- "https://ona-book.org/data/friends_tv_edgelist.csv"
friends_edgelist <- read.csv(url)# which two characters interact most?
friends_edgelist |>
dplyr::arrange(desc(weight)) |>
head()## from to weight
## 1 Chandler Monica 938
## 2 Chandler Joey 889
## 3 Rachel Ross 836
## 4 Monica Phoebe 800
## 5 Monica Rachel 774
## 6 Phoebe Rachel 771# is this a connected network?
friends_graph <-
igraph::graph_from_data_frame(
friends_edgelist,
directed = FALSE
)
components(friends_graph)## $membership
## a Casino Boss a Crew Member a Disembodied Voice a Drunken Gambler
## 1 1 1 1
## a Female Student a Male Customer a Student a Tourist
## 1 1 1 1
## a Waiter a Waiter in Drag a Woman Actor
## 1 1 1 1
## Adoption Agency Guy Adrienne Agency Guy Air Stewardess
## 1 1 1 1
## Airline Employee Alan Alex Alexandra Steele
## 1 1 1 1
## Alice Alison Amanda Amber
## 1 1 1 1
## Amy Amy Storms Out Anchorwoman Andrea
## 1 1 1 1
## Angela Annabelle Announcer Another Extra
## 1 1 1 1
## Another Man's Voice Another Scientist Another Tour Guide Answering Machine
## 1 1 1 1
## Arthur Ashley Assistant Aunt Iris
## 1 1 1 1
## Aunt Lillian Aunt Lisa Aunt Millie Aurora
## 1 1 1 1
## Bandleader Bank Officer Barry Bass Singer
## 1 1 1 1
## Ben Benjamin Bernice Best Man
## 1 1 1 1
## Big Bully Big Nosed Rachel Bill Billy Crystal
## 1 1 1 1
## Bitter Lady Bitter Woman Blackjack Dealer Blonde Girl
## 1 1 1 1
## Bob Bonnie Boy Boy in the Cape
## 1 1 1 1
## Boys Burt C.h.e.e.s.e C.h.e.e.s.e.
## 1 1 1 1
## Cailin Caitlin Carl Carol
## 1 1 1 1
## Caroline Casey Cashier Cassie
## 1 1 1 1
## Casting Assistant Casting Director #1 Casting Director #2 Casting Director #3
## 1 1 1 1
## Casting Guy Cecilia Celia Chandler
## 1 1 1 1
## Chandlers Charity Guy Charlie Charlton Heston
## 1 1 1 1
## Chase Lassiter Cheryl Chip Chloe
## 1 1 1 1
## Claudia Clerk Cliff Clown
## 1 1 1 1
## Colleen Coma Guy Cookie Cop
## 1 1 1 1
## Cowgirl Croupler Customer Customers
## 1 1 1 1
## Cut to Cynthia Dan Danielle
## 1 1 1 1
## Danny Danny's Sister Dave David
## 1 1 1 1
## Delivery Girl Delivery Guy Dennis Phillips Devon
## 1 1 1 1
## Dick Clark Dina Director Dirk
## 1 1 1 1
## Doctor Doctor Connelly Don Donny
## 1 1 1 1
## Donny Osmond Doug Dr Baldhara Dr Biely
## 1 1 1 1
## Dr Burke Dr Drake Ramoray Dr Drake Remoray Dr Franzblau
## 1 1 1 1
## Dr Gettleman Dr Green Dr Harad Dr Horton
## 1 1 1 1
## Dr Johnson Dr Ledbetter Dr Leedbetter Dr Li
## 1 1 1 1
## Dr Long Dr Miller Dr Mitchell Dr Oberman
## 1 1 1 1
## Dr Remore Dr Rhodes Dr Roger Dr Rosen
## 1 1 1 1
## Dr Schiff Dr Stryker Remoray Dr Timothy Burke Dr Wesley
## 1 1 1 1
## Dr Zane Drew Drunk Man Drunken Gambler
## 1 1 1 1
## Duncan Earl Eddie Eldad
## 1 1 1 1
## Elizabeth Emeril Emil Alexander Emily
## 1 1 1 1
## Emma Employee Eric Erica
## 1 1 1 1
## Erin Ernie Estelle Estl
## 1 1 1 1
## Evil Bitch Extra Fake Monica Fat Girl
## 1 1 1 1
## Fat Monica Fbob Felicity Female Clerk
## 1 1 1 1
## Female Jeweler Female Student Fergie Fireman
## 1 1 1 1
## Fireman #1 Fireman #2 Fireman No. 1 Fireman No. 2
## 1 1 1 1
## Fireman No. 3 First Dorm Guy Fran Frank
## 1 1 1 1
## Frank Jr. Frank Sr. Frannie Fredrick
## 1 1 1 1
## Front Desk Clerk Funny Gail Gang
## 1 1 1 1
## Gary Gary Collins Gary's Radio Gate Agent
## 1 1 1 1
## Gavin Gene Gerston Gert
## 1 1 1 1
## Ginger Girl Girl's Voice Girls
## 1 1 1 1
## Grandma Tribbiani Grandmom Guest #1 Guest #2
## 1 1 1 1
## Guest #3 Gunther Guru Saj Guy
## 1 1 1 1
## Guy #1 Guy #2 Guys Gym Employee
## 1 1 1 1
## Handyman Hayley Hayley's Roommate Health Inspector
## 1 1 1 1
## Heather Helen Helena Henrietta
## 1 1 1 1
## Hilda Hillary Hitchhiker Hold Voice
## 1 1 1 1
## Hombre Man Homo Ergaster Hooker Hope
## 1 1 1 1
## Hoshi Host Hotel Clerk Housekeeper
## 1 1 1 1
## Hums While He Pees Hypnosis Tape i Understand Intercom
## 1 1 1 1
## Interviewer Isabella Issac It's 6
## 1 1 1 1
## It's 9 Jack Jade Jake
## 1 1 1 1
## Jamie Jane Janice Janice's Voice
## 1 1 1 1
## Janine Janitor Jasmine Jason
## 1 1 1 1
## Jay Leno Jeanette Jeannie Jeannine
## 1 1 1 1
## Jen Jennifer Jessica Ashley Jessica Lockhart
## 1 1 1 1
## Jester Jill Jim Jo Lynn
## 1 1 1 1
## Joanna Joanne Joey Joey Laughs
## 1 1 1 1
## Joey Nods Joey on Tv Joey's Co-Star Joey's Date
## 1 1 1 1
## Joey's Doctor Joey's Grandmom Joey's Hand Twin Joey's Head
## 1 1 1 1
## Joey's Sister Joey's Sisters Josh Joshua
## 1 1 1 1
## Judge Judy Julie Julio
## 1 1 1 1
## Kara Karin Kash Kate
## 1 1 1 1
## Kathy Kathy's Co-Star Katie Ken
## 1 1 1 1
## Kevin Kid Kids Kiki
## 1 1 1 1
## Kim Kitchen Worker Kori Krista
## 1 1 1 1
## Kristen Kristin Kyle Kyle Lowder
## 1 1 1 1
## Lady Larry Laura Lauren
## 1 1 1 1
## Leader Lecturer Leslie Lewis
## 1 1 1 1
## Liam Lipson Lisa Little Bully
## 1 1 1 1
## Little Girl Lizzie Locksmith Lorraine
## 1 1 1 1
## Luisa Lydia Mac Machine
## 1 1 1 1
## Maitre D' Malcom Male Guest Male Jeweler
## 1 1 1 1
## Male Student Man Man at the Wedding Man on Tv
## 1 1 1 1
## Man with a Bow Tie Man's Voice Manny Marc
## 1 1 1 1
## Marge Margha Marjorie Mark
## 1 1 1 1
## Marsha Mary Ellen Mary-Angela Matire'd
## 1 1 1 1
## Matress King Matthew Ashford Max Meg
## 1 1 1 1
## Megan Mel Melanie Melissa
## 1 1 1 1
## Message Mich Michelle Mike
## 1 1 1 1
## Mike's Dad Mike's Mom Mindy Minister
## 1 1 1 1
## Minster Mischa Missy Molly
## 1 1 1 1
## Mona Mona's Date Monica Morse
## 1 1 1 1
## Mover Mr a Mr Bing Mr Bowmont
## 1 1 1 1
## Mr Boyle Mr Buffay Mr Burgin Mr Campbell
## 1 1 1 1
## Mr Douglas Mr Franklin Mr Geller Mr Green
## 1 1 1 1
## Mr Greene Mr Heckles Mr Kaplan Mr Oberblau
## 1 1 1 1
## Mr Posner Mr Simon Mr Thompson Mr Treeger
## 1 1 1 1
## Mr Tribbiani Mr Waltham Mr Wineburg Mr Zellner
## 1 1 1 1
## Mr Zelner Mrs Bing Mrs Burgin Mrs Burkart
## 1 1 1 1
## Mrs Chatracus Mrs Geller Mrs Green Mrs Greene
## 1 1 1 1
## Mrs Lynch Mrs Potter Mrs Tedlock Mrs Waltham
## 1 1 1 1
## Mrs Wineburg Ms Geller Ms Lambert Nancy
## 1 1 1 1
## Narrator Nina Nurse Nurse #1
## 1 1 1 1
## Nurse #2 Old Woman Older Scientist Others
## 1 1 1 1
## Oven Pa Paleontologist Paolo
## 1 1 1 1
## Parker Party Guests Passenger Passenger #1
## 1 1 1 1
## Passenger #2 Passenger #3 Patrick Paul
## 1 1 1 1
## Paulo Pbs Volunteer Pete Pete's Mom
## 1 1 1 1
## Peter Petrie Phil Phoebe
## 1 1 1 1
## Phoebe Sr Photographer Pizza Guy Policeman
## 1 1 1 1
## Priest on Tv Prof. Sherman Professor Feesen Professor Sherman
## 1 1 1 1
## Professor Spafford Professore Clerk Quartet Racel
## 1 1 1 1
## Rache Rachel Radio Rahcel
## 1 1 1 1
## Raymond Receptionist Referee Richard
## 1 1 1 1
## Rita Rob Robbie Robert
## 1 1 1 1
## Robin Williams Roger Ross Salon Girl
## 1 1 1 1
## Same Man's Voice Sarah Second Dorm Guy Second Message
## 1 1 1 1
## Security Guard Sherman Whitfield Sleep Clinic Worker Soothing Male Voice
## 1 1 1 1
## Stanley Stephanie Steve Supervisor
## 1 1 1 1
## Susan Susie Teacher Terry
## 1 1 1 1
## The Colonel The Conductor The Cooking Teacher The Director
## 1 1 1 1
## The Doctor The Girls The Guys The Head Librarian
## 1 1 1 1
## The Hot Girl The Interviewer The Little Girl The Lurker
## 1 1 1 1
## The Other Woman The Producer The Second Guest Ticket Agent
## 1 1 1 1
## Trainer Tv Announcer Ursula Waiter
## 1 1 1 1
## Waitress Woman No. 1 The Fireman The Croupier
## 1 1 1 1
## Woman The Waiter Waiter in Drag Woman at Door
## 1 1 1 1
## Store Guy The Presenter Tour Guide Second Girl
## 1 1 1 1
## Uncle Dan The Woman The Teacher Owen
## 1 1 1 1
## Wayne Tommy The Writer Dana
## 1 1 1 1
## Frankie Lowell Mary-Theresa Ms Mckenna
## 1 1 1 1
## Phoebe Sr. Realtor Richard's Date Ronni
## 1 1 1 1
## Roy Russ Sandy Santos
## 1 1 1 1
## Scott Sebastian Shelley Singer
## 1 1 1 1
## Sister 1 Sophie Stevens Stripper
## 1 1 1 1
## Tag The Chorus Line The Cigarette Guy The Knocker
## 1 1 1 1
## The Man The Museum Official The Paramedic The Photographer
## 1 1 1 1
## The Rabbi The Salesman The Stripper The Vampire
## 1 1 1 1
## The Waitress Tilly Tim Tom
## 1 1 1 1
## Tony Tv Tv Doctor Van Damme
## 1 1 1 1
## Vince Voice Walker Whitfield
## 1 1 1 1
## will Witch Woman No. 2 Woman on Tv
## 1 1 1 1
## Woman's Voice Zack Shop Assistant Patron
## 1 1 1 1
## Woman Giving Birth Stage Manager The "Hey Guy" Guy Zoe
## 1 1 1 1
## Secretary Ray Sick Bastard Stage Director
## 1 1 1 1
## Mackenzie The Fan The Porsche Owner Sid
## 1 1 1 1
## Mrs Tribbiani Phoebe-Estelle Smart Kid Sonia
## 1 1 1 1
## Student Tape The A.d The Acting Teacher
## 1 1 1 1
## The Dr Cleaner The Food Critic The Grip The Husband
## 1 1 1 1
## The Old Man The Pastor The Security Guard The Singing Man
## 1 1 1 1
## The Woman Dealer Waiters We Have 8 Young Ethan
## 1 1 1 1
## Ralph Lauren Whitney The Bass Barber Strange Man
## 1 1 1 1
## Precious Sergei Paula Rtst
## 1 1 1 1
## Spokeswoman Stu The Cute Guy The Professor
## 1 1 1 1
## The Saleslady The Wedding Guest Video Rick
## 1 1 1 1
## The Housekeeper Phoebe's Assistant Producer Ryan
## 1 1 1 1
## Smoke Detector Stranger Tattoo Artist The Smoking Woman
## 1 1 1 1
## Trudie Styler Russell Wedding Planner Salesman
## 1 1 1 1
## The Instructor The Librarian The Saleswoman Waiter #2
## 1 1 1 1
## Woman on Train Waiter No. 2
## 1 1
##
## $csize
## [1] 650
##
## $no
## [1] 1# who is the most "important" character?
c_vals <-
friends_graph |>
tidygraph::as_tbl_graph() |>
dplyr::mutate(
DEGREE_CENT = tidygraph::centrality_degree(),
BTWN_CENT = tidygraph::centrality_betweenness(),
CLOSE_CENT = tidygraph::centrality_closeness()
)## Warning in betweenness(graph = graph, v = V(graph), directed = directed, :
## 'nobigint' is deprecated since igraph 1.3 and will be removed in igraph 1.4# Ross and Joey come out on top of all metrics
c_vals |>
dplyr::arrange(desc(DEGREE_CENT))## # A tbl_graph: 650 nodes and 2976 edges
## #
## # An undirected simple graph with 1 component
## #
## # Node Data: 650 x 4 (active)
## name DEGREE_CENT BTWN_CENT CLOSE_CENT
## <chr> <dbl> <dbl> <dbl>
## 1 Ross 359 46721. 0.00106
## 2 Joey 353 46871. 0.00106
## 3 Chandler 342 35337. 0.00105
## 4 Monica 339 33493. 0.00104
## 5 Phoebe 334 39774. 0.00104
## 6 Rachel 331 32353. 0.00103
## # … with 644 more rows
## #
## # Edge Data: 2,976 x 3
## from to weight
## <int> <int> <int>
## 1 243 244 1
## 2 3 243 1
## 3 2 243 1
## # … with 2,973 more rowsc_vals |>
dplyr::arrange(desc(BTWN_CENT))## # A tbl_graph: 650 nodes and 2976 edges
## #
## # An undirected simple graph with 1 component
## #
## # Node Data: 650 x 4 (active)
## name DEGREE_CENT BTWN_CENT CLOSE_CENT
## <chr> <dbl> <dbl> <dbl>
## 1 Joey 353 46871. 0.00106
## 2 Ross 359 46721. 0.00106
## 3 Phoebe 334 39774. 0.00104
## 4 Chandler 342 35337. 0.00105
## 5 Monica 339 33493. 0.00104
## 6 Rachel 331 32353. 0.00103
## # … with 644 more rows
## #
## # Edge Data: 2,976 x 3
## from to weight
## <int> <int> <int>
## 1 164 171 1
## 2 4 164 1
## 3 1 164 1
## # … with 2,973 more rowsc_vals |>
dplyr::arrange(desc(CLOSE_CENT))## # A tbl_graph: 650 nodes and 2976 edges
## #
## # An undirected simple graph with 1 component
## #
## # Node Data: 650 x 4 (active)
## name DEGREE_CENT BTWN_CENT CLOSE_CENT
## <chr> <dbl> <dbl> <dbl>
## 1 Ross 359 46721. 0.00106
## 2 Joey 353 46871. 0.00106
## 3 Chandler 342 35337. 0.00105
## 4 Monica 339 33493. 0.00104
## 5 Phoebe 334 39774. 0.00104
## 6 Rachel 331 32353. 0.00103
## # … with 644 more rows
## #
## # Edge Data: 2,976 x 3
## from to weight
## <int> <int> <int>
## 1 219 220 1
## 2 3 219 1
## 3 2 219 1
## # … with 2,973 more rows# Are the friends part of the same community? Who else is in the community?
# Does it make intuitive sense (if you're a friends fan :-) )
set.seed(123)
communities <- cluster_louvain(friends_graph, weights = NULL)
V(friends_graph)$community <- membership(communities)
membership(communities)[c("Joey","Monica","Chandler","Ross","Phoebe","Rachel")]## Joey Monica Chandler Ross Phoebe Rachel
## 5 5 5 5 5 5# is anyone else in cluster 5? so many random people....
length(membership(communities)[membership(communities) == 5])## [1] 246# try leiden
set.seed(123)
communities <- cluster_leiden(friends_graph, weights = NULL)
V(friends_graph)$community <- membership(communities)
membership(communities)[c("Joey","Monica","Chandler","Ross","Phoebe","Rachel")]## Joey Monica Chandler Ross Phoebe Rachel
## 49 49 49 49 49 49# this list of people makes a lot more sense to a Friends fan
membership(communities)[membership(communities) == 49]## Ben Carol Chandler Charlie Elizabeth
## 49 49 49 49 49
## Emily Erica Frank Gary Gunther
## 49 49 49 49 49
## Guy Janice Janine Jill Joey
## 49 49 49 49 49
## Julie Kathy Man Mike Mona
## 49 49 49 49 49
## Monica Mr Geller Mrs Bing Mrs Geller Nurse
## 49 49 49 49 49
## Paul Pete Phoebe Rachel Richard
## 49 49 49 49 49
## Ross Susan The Guys The Interviewer Waiter
## 49 49 49 49 49
## Woman Tag
## 49 49