# An edgelist is usually formatted as a table where the first two columns contain the IDs of a pair of nodes in the network that have a tie between them.
# Optional additional columns may contain properties of the relationship between the nodes (e.g., the value of a tie).
# Any pair of nodes that does not have a tie between them is usually not included in an edgelist.
# This property is what makes edgelists a more efficient network data storage format than sociomatrices (see below).
# Unobsered edges can be encoded in edgelist format by including "NA" in the value column.
# The columns in an edgelist table are usually ordered "Ego" (often the person who completed the interview or who was the subject of a focal follow) followed by "Alter" (the person that the focal individual named or interacted with).
# In the case of undirected network data, the ordering of these columns does not matter, but in directed data, it does.
# `igraph` and `statnet` software will encode directed edgelist data with the arrow pointing from the first to the second column, so if the ties you recorded have reversed directionality from ego to alter, you should flip the order of the columns before converting the data to a network.
# CREATING YOUR EDGELIST
# A critical first step in constructing an edgelist is to ensure that all individuals in the dataset have unique identifiers.
# Note that names, even first and last names together, are frequently not unique identifiers, even in relatively small communities.
# Using some form of ID number or a code generated using a combination of individual characteristics is safer.
# Implementing a good system for obtaining uniquely identifying information about both egos and alters at the beginning of data collection is important for avoiding serious entity resolution problems later on.
# To code up an edgelist from fieldnotes or other unstructured datasets, each time you find a tie between two individuals, simply enter a row with their ID numbers into the edgelist, in the correct order.
# If the same two individuals are observed to interact multiple times, the best procedure is to record an additional edge in your edgelist everytime that interaction is observed, and to maintain an additional column with the date and/or time the interaction was observed, or some other identifying information. It is easy to aggregate or remove these duplicates in R at later stages.
# The columns "Chapter" and "Page" may not be necessary for any analysis I want to do, but they help me find the original data again in the future, in case I needed to double check something, and they also allow me to distinguish duplicate ties between characters.
# Usually, you will save your edgelist as a tab- or comma-delimited file (.txt or .csv) and then import it to R.
hp5edges <- read.table('/Users/rheemh/Dropbox (ASU)/Coding/R/hp5edgelist.txt', sep='\t', header=TRUE)
# take a look
head(hp5edges)
## X.From. X.To.
## 1 Alicia Spinnet Alicia Spinnet
## 2 Alicia Spinnet Angelina Johnson
## 3 Alicia Spinnet Fred Weasley
## 4 Alicia Spinnet George Weasley
## 5 Alicia Spinnet Harry James Potter
## 6 Alicia Spinnet Katie Bell
# covert to an igraph network
library(igraph)
##
## Attaching package: 'igraph'
## The following objects are masked from 'package:stats':
##
## decompose, spectrum
## The following object is masked from 'package:base':
##
## union
hp5edgesmat <- as.matrix(hp5edges) # igraph wants our data in matrix format
hp5net <- graph_from_edgelist(hp5edgesmat, directed=TRUE)
# extract first names from list of names to make nicer labels.
# FYI this is insane R notation to extract elements from a list.
firsts <- unlist(lapply(strsplit(V(hp5net)$name, " "), '[[', 1))
# let's take a look
plot(hp5net, vertex.shape="none", vertex.label.cex=0.6, edge.arrow.size=0.4, vertex.label=firsts, layout=layout.kamada.kawai)
# The function `simplify()` in `igraph` handily removes self-loops from a network.
hp5netsimple <- simplify(hp5net)
plot(hp5netsimple, vertex.shape="none", vertex.label.cex=0.7,
edge.arrow.size=0.4, vertex.label=firsts, layout=layout.kamada.kawai)
# A flaw in the edgelist format is that nodes that are isolates are not listed in the edgelist.
# So in the network contains isolates, these must be added afterwards.
# There are several characters who appear in Book 5 who do not have any ties in the network:
book5isolates <- c("Lavender Brown", "Millicent Bulstrode", "Michael Corner", "Roger Davies", "Theodoer Nott", "Zacharias Smith")
hp5netfull <- add_vertices(hp5netsimple, nv=length(book5isolates), attr=list(name=book5isolates))
# note that here we add as many vertices as in our list of isolates, and assign them the attribute "name" which is stored in our list of isolates.
# regenerate our list of first names to update it ot include these new characters.
firsts <- unlist(lapply(strsplit(V(hp5netfull)$name, " "), '[[', 1))
plot(hp5netfull, vertex.shape="none", vertex.label.cex=0.6, edge.arrow.size=0.4, vertex.label=firsts, layout=layout.kamada.kawai)
# A common problem of edgelists is that the same edges may be present more than once. For instance, you might have interviewed both the ego and alter and the same tie might appear more than once. (e.g., Ron said he was friends with Harry, and Harry reported the same thing.)
# If you just want to remove duplicate edges, you can use the same `simplify()` functions used above to remove self-loops.
# But you might prefer to count the number of times an edge appeared and use it as an edge value.
# Let's add some duplicate edges to our Book 5 edgelist, then use the `aggregate()` functions to count occurrences.
hp5newedges <- data.frame(rbind(hp5edges,
c("Harry James Potter", "Ronald Weasley"),
c("Ronald Weasley", "Harry James Potter"),
c("Hermione Granger", "Ronald Weasley"),
c("Hermione Granger", "Harry James Potter"),
c("Hermione Granger", "Harry James Potter")))
# this takes a list of ones and takes the sum of them for each unique row in the edgelist
(hp5edgeweights <- aggregate(list(count=rep(1, nrow(hp5newedges))), hp5newedges, FUN=sum))
## X.From. X.To. count
## 1 Alicia Spinnet Alicia Spinnet 1
## 2 Angelina Johnson Alicia Spinnet 1
## 3 Fred Weasley Alicia Spinnet 1
## 4 George Weasley Alicia Spinnet 1
## 5 Harry James Potter Alicia Spinnet 1
## 6 Katie Bell Alicia Spinnet 1
## 7 Alicia Spinnet Angelina Johnson 1
## 8 Angelina Johnson Angelina Johnson 1
## 9 Fred Weasley Angelina Johnson 1
## 10 George Weasley Angelina Johnson 1
## 11 Harry James Potter Angelina Johnson 1
## 12 Katie Bell Angelina Johnson 1
## 13 Anthony Goldstein Anthony Goldstein 1
## 14 Ernie Macmillan Anthony Goldstein 1
## 15 Hannah Abbott Anthony Goldstein 1
## 16 Justin Finch-Fletchley Anthony Goldstein 1
## 17 Susan Bones Anthony Goldstein 1
## 18 Terry Boot Anthony Goldstein 1
## 19 Cho Chang Cho Chang 1
## 20 Harry James Potter Cho Chang 1
## 21 Dean Thomas Dean Thomas 1
## 22 Draco Malfoy Draco Malfoy 1
## 23 Gregory Goyle Draco Malfoy 1
## 24 Pansy Parkinson Draco Malfoy 1
## 25 Vincent Crabbe Draco Malfoy 1
## 26 Anthony Goldstein Ernie Macmillan 1
## 27 Ernie Macmillan Ernie Macmillan 1
## 28 Hannah Abbott Ernie Macmillan 1
## 29 Harry James Potter Ernie Macmillan 1
## 30 Justin Finch-Fletchley Ernie Macmillan 1
## 31 Susan Bones Ernie Macmillan 1
## 32 Terry Boot Ernie Macmillan 1
## 33 Alicia Spinnet Fred Weasley 1
## 34 Angelina Johnson Fred Weasley 1
## 35 Fred Weasley Fred Weasley 1
## 36 George Weasley Fred Weasley 1
## 37 Ginny Weasley Fred Weasley 1
## 38 Harry James Potter Fred Weasley 1
## 39 Hermione Granger Fred Weasley 1
## 40 Katie Bell Fred Weasley 1
## 41 Lee Jordan Fred Weasley 1
## 42 Ronald Weasley Fred Weasley 1
## 43 Alicia Spinnet George Weasley 1
## 44 Angelina Johnson George Weasley 1
## 45 Fred Weasley George Weasley 1
## 46 George Weasley George Weasley 1
## 47 Ginny Weasley George Weasley 1
## 48 Harry James Potter George Weasley 1
## 49 Hermione Granger George Weasley 1
## 50 Katie Bell George Weasley 1
## 51 Lee Jordan George Weasley 1
## 52 Ronald Weasley George Weasley 1
## 53 Fred Weasley Ginny Weasley 1
## 54 George Weasley Ginny Weasley 1
## 55 Ginny Weasley Ginny Weasley 1
## 56 Harry James Potter Ginny Weasley 1
## 57 Hermione Granger Ginny Weasley 1
## 58 Luna Lovegood Ginny Weasley 1
## 59 Neville Longbottom Ginny Weasley 1
## 60 Ronald Weasley Ginny Weasley 1
## 61 Gregory Goyle Gregory Goyle 1
## 62 Pansy Parkinson Gregory Goyle 1
## 63 Vincent Crabbe Gregory Goyle 1
## 64 Anthony Goldstein Hannah Abbott 1
## 65 Ernie Macmillan Hannah Abbott 1
## 66 Hannah Abbott Hannah Abbott 1
## 67 Justin Finch-Fletchley Hannah Abbott 1
## 68 Susan Bones Hannah Abbott 1
## 69 Terry Boot Hannah Abbott 1
## 70 Alicia Spinnet Harry James Potter 1
## 71 Angelina Johnson Harry James Potter 1
## 72 Anthony Goldstein Harry James Potter 1
## 73 Cho Chang Harry James Potter 1
## 74 Dean Thomas Harry James Potter 1
## 75 Ernie Macmillan Harry James Potter 1
## 76 Fred Weasley Harry James Potter 1
## 77 George Weasley Harry James Potter 1
## 78 Ginny Weasley Harry James Potter 1
## 79 Hannah Abbott Harry James Potter 1
## 80 Harry James Potter Harry James Potter 1
## 81 Hermione Granger Harry James Potter 3
## 82 Justin Finch-Fletchley Harry James Potter 1
## 83 Katie Bell Harry James Potter 1
## 84 Luna Lovegood Harry James Potter 1
## 85 Neville Longbottom Harry James Potter 1
## 86 Padma Patil Harry James Potter 1
## 87 Percy Weasley Harry James Potter 1
## 88 Ronald Weasley Harry James Potter 2
## 89 Seamus Finnigan Harry James Potter 1
## 90 Susan Bones Harry James Potter 1
## 91 Terry Boot Harry James Potter 1
## 92 Fred Weasley Hermione Granger 1
## 93 George Weasley Hermione Granger 1
## 94 Ginny Weasley Hermione Granger 1
## 95 Harry James Potter Hermione Granger 1
## 96 Hermione Granger Hermione Granger 1
## 97 Luna Lovegood Hermione Granger 1
## 98 Neville Longbottom Hermione Granger 1
## 99 Ronald Weasley Hermione Granger 1
## 100 Terry Boot Hermione Granger 1
## 101 Anthony Goldstein Justin Finch-Fletchley 1
## 102 Ernie Macmillan Justin Finch-Fletchley 1
## 103 Hannah Abbott Justin Finch-Fletchley 1
## 104 Harry James Potter Justin Finch-Fletchley 1
## 105 Justin Finch-Fletchley Justin Finch-Fletchley 1
## 106 Susan Bones Justin Finch-Fletchley 1
## 107 Terry Boot Justin Finch-Fletchley 1
## 108 Alicia Spinnet Katie Bell 1
## 109 Angelina Johnson Katie Bell 1
## 110 Fred Weasley Katie Bell 1
## 111 George Weasley Katie Bell 1
## 112 Harry James Potter Katie Bell 1
## 113 Katie Bell Katie Bell 1
## 114 Fred Weasley Lee Jordan 1
## 115 George Weasley Lee Jordan 1
## 116 Harry James Potter Lee Jordan 1
## 117 Lee Jordan Lee Jordan 1
## 118 Ginny Weasley Luna Lovegood 1
## 119 Harry James Potter Luna Lovegood 1
## 120 Hermione Granger Luna Lovegood 1
## 121 Luna Lovegood Luna Lovegood 1
## 122 Neville Longbottom Luna Lovegood 1
## 123 Ronald Weasley Luna Lovegood 1
## 124 Ginny Weasley Neville Longbottom 1
## 125 Harry James Potter Neville Longbottom 1
## 126 Hermione Granger Neville Longbottom 1
## 127 Luna Lovegood Neville Longbottom 1
## 128 Neville Longbottom Neville Longbottom 1
## 129 Ronald Weasley Neville Longbottom 1
## 130 Draco Malfoy Pansy Parkinson 1
## 131 Gregory Goyle Pansy Parkinson 1
## 132 Pansy Parkinson Pansy Parkinson 1
## 133 Vincent Crabbe Pansy Parkinson 1
## 134 Parvati Patil Parvati Patil 1
## 135 Percy Weasley Percy Weasley 1
## 136 Fred Weasley Ronald Weasley 1
## 137 George Weasley Ronald Weasley 1
## 138 Ginny Weasley Ronald Weasley 1
## 139 Harry James Potter Ronald Weasley 2
## 140 Hermione Granger Ronald Weasley 2
## 141 Luna Lovegood Ronald Weasley 1
## 142 Neville Longbottom Ronald Weasley 1
## 143 Percy Weasley Ronald Weasley 1
## 144 Ronald Weasley Ronald Weasley 1
## 145 Seamus Finnigan Seamus Finnigan 1
## 146 Anthony Goldstein Susan Bones 1
## 147 Ernie Macmillan Susan Bones 1
## 148 Hannah Abbott Susan Bones 1
## 149 Justin Finch-Fletchley Susan Bones 1
## 150 Susan Bones Susan Bones 1
## 151 Terry Boot Susan Bones 1
## 152 Anthony Goldstein Terry Boot 1
## 153 Ernie Macmillan Terry Boot 1
## 154 Hannah Abbott Terry Boot 1
## 155 Justin Finch-Fletchley Terry Boot 1
## 156 Susan Bones Terry Boot 1
## 157 Terry Boot Terry Boot 1
## 158 Gregory Goyle Vincent Crabbe 1
## 159 Pansy Parkinson Vincent Crabbe 1
## 160 Vincent Crabbe Vincent Crabbe 1
# take a look at the edges that appear more than once
hp5edgeweights[which(hp5edgeweights$count>1),]
## X.From. X.To. count
## 81 Hermione Granger Harry James Potter 3
## 88 Ronald Weasley Harry James Potter 2
## 139 Harry James Potter Ronald Weasley 2
## 140 Hermione Granger Ronald Weasley 2
# create a network and assign the counts as an edge weight
(hp5netweight <- graph_from_edgelist(as.matrix(hp5edgeweights[,1:2]),directed=TRUE))
## IGRAPH a7f6a56 DN-- 28 160 --
## + attr: name (v/c)
## + edges from a7f6a56 (vertex names):
## [1] Alicia Spinnet ->Alicia Spinnet Angelina Johnson ->Alicia Spinnet
## [3] Fred Weasley ->Alicia Spinnet George Weasley ->Alicia Spinnet
## [5] Harry James Potter->Alicia Spinnet Katie Bell ->Alicia Spinnet
## [7] Alicia Spinnet ->Angelina Johnson Angelina Johnson ->Angelina Johnson
## [9] Fred Weasley ->Angelina Johnson George Weasley ->Angelina Johnson
## [11] Harry James Potter->Angelina Johnson Katie Bell ->Angelina Johnson
## [13] Anthony Goldstein ->Anthony Goldstein Ernie Macmillan ->Anthony Goldstein
## [15] Hannah Abbott ->Anthony Goldstein
## + ... omitted several edges
(E(hp5netweight)$weight <- hp5edgeweights$count)
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 3 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 1 1 1 1
# Normally, in addition to your edgelist, you will have a table of attributes for the nodes in your network; variables like age and gender, for example.
# Loading these data is just like importing any other data in R.
attributes <- read.csv("/Users/rheemh/Dropbox (ASU)/Coding/R/hpindividuals.csv", header=TRUE)
head(attributes)
## id name schoolyear gender house
## 1 12 Demelza Robins 1993 2 1
## 2 3 Angelina Johnson 1989 2 1
## 3 34 Lucian Bole 1988 1 4
## 4 13 Dennis Creevey 1994 1 1
## 5 56 Ronald Weasley 1991 1 1
## 6 28 Justin Finch-Fletchley 1991 1 2
V(hp5netfull)
## + 34/34 vertices, named, from c3ecd40:
## [1] Alicia Spinnet Angelina Johnson Fred Weasley
## [4] George Weasley Harry James Potter Katie Bell
## [7] Anthony Goldstein Ernie Macmillan Hannah Abbott
## [10] Justin Finch-Fletchley Susan Bones Terry Boot
## [13] Cho Chang Dean Thomas Draco Malfoy
## [16] Pansy Parkinson Ginny Weasley Hermione Granger
## [19] Lee Jordan Ronald Weasley Luna Lovegood
## [22] Neville Longbottom Gregory Goyle Vincent Crabbe
## [25] Padma Patil Parvati Patil Percy Weasley
## [28] Seamus Finnigan Lavender Brown Millicent Bulstrode
## + ... omitted several vertices
# This table appears to contain individuals who are not in the Book 5 network. Let's use the `%in%` operator to extract just the individuals in the Book 5 network from our attributes dataframe.
book5students <- attributes[attributes$name %in% V(hp5netfull)$name,]
# then reorder the attributes to match the order that the individual appear as network vertices.
book5students <- book5students[match(V(hp5netfull)$name, book5students$name), ]
# now we can simply assign the dataframe columns as vertex attributes, e.g.,
V(hp5netfull)$house <- book5students$house
# but let's use a factor to get nice colors
housecolors <- c("firebrick", "darkgoldenrod", "darkslateblue", "darkgreen")
V(hp5netfull)$housecolors <- as.character(factor(book5students$house, levels=1:4, labels=housecolors))
plot(hp5netfull, vertex.shape="none", vertex.label.color=V(hp5netfull)$housecolors, vertex.label.cex=0.6, edge.arrow.size=0.3, vertex.label=firsts, layout=layout.kamada.kawai)
# Network data may also be formatted as a sociomatrix, which is a square matrix where the rows and columns represent individuals in the network and the cell values represent the ties between them (valued or 0/1 for unvalued).
# In general, it is impractical to record data in this format. In most cases, trying to record data in a sociomatric means you will be constantly scrambling to add more columns and rows that will be mostly filled with zeroes.
# The storage requirements for a sociomatrix increase with the square of the number of observed vertices in your network, even if most of the entries in the sociomatrix are zero (as they typically are for even moderately-sized populations).
# Much better to maintain a list of individuals and edges as they appear.
# Sociomatrices are more useful when there is a non-zero value in most cells (i.e., when the data are not sparse).
# Such is the case when the relationship between nodes involves some kind of distance metric (e.g., physical distance between households or relatedness coefficients).
# Distance matrices like these are often important covariates of other networks (e.g., relatedness and physical distance often correlate with ties of support between individuals), but you will not usually generate such matrices by hand.
# Instead, you will likely generate distance matrices in R or some other software program using calculations based off other data (e.g., latitutde and longitude coordinates).
# Here's how to import a sociomatrix (and get it correctly lined up with your attribute data).
# read in data
hp5df <- read.table("/Users/rheemh/Dropbox (ASU)/Coding/R/hp5matrix.txt", sep="\t", header=FALSE) #note that this file has no row or column names)
# inspect it
head(hp5df)
## V1
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
(dim(hp5df))
## [1] 64 1
# R thinks our data frame has only one column. That's not right.
# Taking a look at the data in a spreadsheet viewer suggests maybe this dataset is formatted differently.
# Let's try a different separator when we read in the data.
# read in data
hp5df <- read.table("/Users/rheemh/Dropbox (ASU)/Coding/R/hp5matrix.txt", sep=" ", header=FALSE)
# inspect
dim(hp5df) # much better
## [1] 64 64
# Sometimes networks may be generated from data on the presence or absence of individuals at certain palce or time.
# Such data should normally be formatted as an affiliation matrix, where individuals appear in the rows and events/places/times in the columns, with values in the cells filled out as appropriate (did the individual attend the event or not).
# To convert an affiliation (person-by-event) matrix to a person-to-person network, simply multiply the affiliation matrix by its transpose.
# Carefully consider what an affiliation matrix means in terms of relationships between persons. Not all group, place, or event affiliations correspond to relationships between persons that re meaning ful with respect to your research question.
# The intergraph package allows you to convert network obejcts from `igraph` to `network` (i.e., `statnet`) format and vice versa as well as to or from formats used by other network platforms such as pajeck.
# I often end up using both `igraph` and `statnet` when I'm working with a network dataset, because different centrality measures are implemented in them, and also because sometimes a network just looks better plotted using one package rather than the other.
library(intergraph)
detach("package:igraph") # don't use igraph and statnet at the same time, they have many functions with the same names
library(statnet)
## Loading required package: tergm
## Loading required package: ergm
## Loading required package: network
## network: Classes for Relational Data
## Version 1.16.1 created on 2020-10-06.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Martina Morris, University of Washington
## Skye Bender-deMoll, University of Washington
## For citation information, type citation("network").
## Type help("network-package") to get started.
##
## ergm: version 3.11.0, created on 2020-10-14
## Copyright (c) 2020, Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Carter T. Butts, University of California -- Irvine
## Steven M. Goodreau, University of Washington
## Pavel N. Krivitsky, UNSW Sydney
## Martina Morris, University of Washington
## with contributions from
## Li Wang
## Kirk Li, University of Washington
## Skye Bender-deMoll, University of Washington
## Chad Klumb
## MichaĆ Bojanowski, Kozminski University
## Ben Bolker
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("ergm").
## NOTE: Versions before 3.6.1 had a bug in the implementation of the bd()
## constraint which distorted the sampled distribution somewhat. In
## addition, Sampson's Monks datasets had mislabeled vertices. See the
## NEWS and the documentation for more details.
## NOTE: Some common term arguments pertaining to vertex attribute and
## level selection have changed in 3.10.0. See terms help for more
## details. Use 'options(ergm.term=list(version="3.9.4"))' to use old
## behavior.
## Loading required package: networkDynamic
##
## networkDynamic: version 0.10.1, created on 2020-01-16
## Copyright (c) 2020, Carter T. Butts, University of California -- Irvine
## Ayn Leslie-Cook, University of Washington
## Pavel N. Krivitsky, University of Wollongong
## Skye Bender-deMoll, University of Washington
## with contributions from
## Zack Almquist, University of California -- Irvine
## David R. Hunter, Penn State University
## Li Wang
## Kirk Li, University of Washington
## Steven M. Goodreau, University of Washington
## Jeffrey Horner
## Martina Morris, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("networkDynamic").
##
## tergm: version 3.7.0, created on 2020-10-15
## Copyright (c) 2020, Pavel N. Krivitsky, UNSW Sydney
## Mark S. Handcock, University of California -- Los Angeles
## with contributions from
## David R. Hunter, Penn State University
## Steven M. Goodreau, University of Washington
## Martina Morris, University of Washington
## Nicole Bohme Carnegie, New York University
## Carter T. Butts, University of California -- Irvine
## Ayn Leslie-Cook, University of Washington
## Skye Bender-deMoll
## Li Wang
## Kirk Li, University of Washington
## Chad Klumb
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("tergm").
## Loading required package: ergm.count
##
## ergm.count: version 3.4.0, created on 2019-05-15
## Copyright (c) 2019, Pavel N. Krivitsky, University of Wollongong
## with contributions from
## Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("ergm.count").
## NOTE: The form of the term 'CMP' has been changed in version 3.2 of
## 'ergm.count'. See the news or help('CMP') for more information.
## Loading required package: sna
## Loading required package: statnet.common
##
## Attaching package: 'statnet.common'
## The following object is masked from 'package:base':
##
## order
## sna: Tools for Social Network Analysis
## Version 2.6 created on 2020-10-5.
## copyright (c) 2005, Carter T. Butts, University of California-Irvine
## For citation information, type citation("sna").
## Type help(package="sna") to get started.
## Loading required package: tsna
##
## statnet: version 2019.6, created on 2019-06-13
## Copyright (c) 2019, Mark S. Handcock, University of California -- Los Angeles
## David R. Hunter, Penn State University
## Carter T. Butts, University of California -- Irvine
## Steven M. Goodreau, University of Washington
## Pavel N. Krivitsky, University of Wollongong
## Skye Bender-deMoll
## Martina Morris, University of Washington
## Based on "statnet" project software (statnet.org).
## For license and citation information see statnet.org/attribution
## or type citation("statnet").
## unable to reach CRAN
hp5network <- asNetwork(hp5netfull)
hp5network
## Network attributes:
## vertices = 34
## directed = TRUE
## hyper = FALSE
## loops = FALSE
## multiple = FALSE
## bipartite = FALSE
## total edges= 133
## missing edges= 0
## non-missing edges= 133
##
## Vertex attribute names:
## house housecolors vertex.names
##
## No edge attributes
# Easy peasy. Note that `igraph` does not support network level attribtues (indicated with %n% in statnet).
# So if you convert an object from `statnet` to `igraph`, you'll lose these.
# Edge and vertex attributes are carried over, however.