我正在尝试使用 igraph 生成 gexf 文件，但不幸的是我的布局有问题。我怎样才能解决它以获得像第二个这样的好图表。
第一个使用 1000 个节点创建的图像，但第二个使用 500 个节点
gD <- simplify(graph.data.frame(dataSet, directed=FALSE))
# Print number of nodes and edges
#vcount(gD)
#ecount(gD)
############################################################################################
# Calculate some node properties and node similarities that will be used to illustrate
# different plotting abilities
# Calculate degree for all nodes
degAll <- igraph::degree(gD, v = V(gD), mode = "all")
# Calculate betweenness for all nodes
#tnet::betweenness_w(data.frame(V1 = dataSet$V1,V2=dataSet$V2, V3 =dataSet$V3 ))
betAll <- igraph::betweenness(gD, v = V(gD),normalized=TRUE)
betAll <- betAll
#options("scipen"=100000000)
#betweenness()
#betAll.norm <- betAll / 100000000000000
betAll.norm <-betAll
#betAll.norm <- (betAll - min(betAll))/(max(betAll) - min(betAll))
rm(betAll)
# Calculate Dice similarities between all pairs of nodes
dsAll <- similarity.dice(gD, vids = V(gD), mode = "all")
############################################################################################
# Add new node/edge attributes based on the calculated node properties/similarities
newdataSet1 <- data.frame(username = dataSet$V1,gender = dataSet$V4)
newdataSet2 <- data.frame(username = dataSet$V2,gender = dataSet$V4)
newdataSet<-rbind(newdataSet1,newdataSet2)
genderdata<-subset(newdataSet,!duplicated(newdataSet$username))
gD <- set.vertex.attribute(gD, "degree", index = V(gD), value = degAll)
gD <- set.vertex.attribute(gD, "betweenness", index = V(gD), value = betAll.norm)
gD <- set.vertex.attribute(gD, "gender", index = V(gD), value = genderdata$gender)
newdataSet1 <- data.frame(username = dataSet$V1,sentiment = dataSet$V5)
newdataSet2 <- data.frame(username = dataSet$V2,sentiment = dataSet$V5)
newdataSet<-rbind(newdataSet1,newdataSet2)
sentimentdata<-subset(newdataSet,!duplicated(newdataSet$username))
gD <- set.vertex.attribute(gD, "sentiment", index = V(gD), value = sentimentdata$sentiment)
# Check the attributes
# summary(gD)
F1 <- function(x) {data.frame(V4 = dsAll[which(V(gD)$name == as.character(x$V1)), which(V(gD)$name == as.character(x$V2))])}
cl = createCluster(6, export = list("F1","dsAll","gD"), lib = list("igraph","plyr"))
system.time(dataSet.ext <- ddply(dataSet, .variables=c("V1", "V2", "V3"), function(x) data.frame(F1(x)),.parallel = TRUE))
#res = ddply(dat, .(category), bla, .parallel = TRUE)
stopCluster(cl)
gD <- set.edge.attribute(gD, "weight", index = E(gD), value = 0)
gD <- set.edge.attribute(gD, "similarity", index = E(gD), value = 0)
# The order of interactions in gD is not the same as it is in dataSet or as it is in the edge list,
# and for that reason these values cannot be assigned directly
#length(E(gD)[as.character(dataSet.ext$V1) %--% as.character(dataSet.ext$V2)]$weight )
E(gD)[as.character(dataSet.ext$V1) %--% as.character(dataSet.ext$V2)]$weight <- as.numeric(dataSet.ext$V3)
E(gD)[as.character(dataSet.ext$V1) %--% as.character(dataSet.ext$V2)]$similarity <- as.numeric(dataSet.ext$V4)
# Check the attributes
# summary(gD)
####################################
# Print network in the file format ready for Gephi
# This requires rgexf package
# Create a dataframe nodes: 1st column - node ID, 2nd column -node name
nodes_df <- data.frame(ID = c(1:vcount(gD)), NAME = V(gD)$name)
# Create a dataframe edges: 1st column - source node ID, 2nd column -target node ID
edges_df <- as.data.frame(get.edges(gD, c(1:ecount(gD))))
# Define node and edge attributes - these attributes won't be directly used for network visualization, but they
# may be useful for other network manipulations in Gephi
#
# Create a dataframe with node attributes: 1st column - attribute 1 (degree), 2nd column - attribute 2 (betweenness)
nodes_att <- data.frame(DEG = V(gD)$degree, BET = V(gD)$betweenness,gender = V(gD)$gender,sentement = V(gD)$sentiment)
#
# Create a dataframe with edge attributes: 1st column - attribute 1 (weight), 2nd column - attribute 2 (similarity)
edges_att <- data.frame(WGH = E(gD)$weight, SIM = E(gD)$similarity)
# Define node/edge visual attributes - these attributes are the ones used for network visualization
#
# Calculate node coordinate - needs to be 3D
#nodes_coord <- as.data.frame(layout.fruchterman.reingold(gD, weights = E(gD)$similarity, dim = 3, niter = 10000))
# We'll cheat here, as 2D coordinates result in a better (2D) plot than 3D coordinates
nodes_coord <- as.data.frame(layout.fruchterman.reingold(gD))
nodes_coord <- cbind(nodes_coord, rep(0, times = nrow(nodes_coord)))
#
# Calculate node size
# We'll interpolate node size based on the node betweenness centrality, using the "approx" function
uniqueNess<-unique(V(gD)$betweenness)
approxVals <- approx(c(1, 5), n = length(unique(V(gD)$betweenness)))
# And we will assign a node size for each node based on its betweenness centrality
nodes_size <- sapply(V(gD)$betweenness, function(x) approxVals$y[which(sort(unique(V(gD)$betweenness)) == x)])
#
# Define node color
# We'll interpolate node colors based on the node degree using the "colorRampPalette" function from the "grDevices" library
# This function returns a function corresponding to a collor palete of "bias" number of elements
F2 <- colorRampPalette(c("#F5DEB3", "#FF0000"), bias = length(unique(V(gD)$degree)), space = "rgb", interpolate = "linear")
# Now we'll create a color for each degree
colCodes <- F2(length(unique(V(gD)$degree)))
#################test parallel####################
cl = createCluster(6, export = list("F2","dsAll","gD","colCodes"), lib = list("igraph","plyr"))
system.time(nodes_col<-parSapply(cl,V(gD)$degree, function(x) colCodes[which(sort(unique(V(gD)$degree)) == x)]))
#res = ddply(dat, .(category), bla, .parallel = TRUE)
stopCluster(cl)
#############################
# And we will assign a color for each node based on its degree
#nodes_col <- sapply(V(gD)$degree, function(x) colCodes[which(sort(unique(V(gD)$degree)) == x)])
# Transform it into a data frame (we have to transpose it first)
nodes_col_df <- as.data.frame(t(col2rgb(nodes_col, alpha = FALSE)))
# And add alpha (between 0 and 1). The alpha from "col2rgb" function takes values from 0-255, so we cannot use it
nodes_col_df <- cbind(nodes_col_df, alpha = rep(1, times = nrow(nodes_col_df)))
# Assign visual attributes to nodes (colors have to be 4dimensional - RGBA)
nodes_att_viz <- list(color = nodes_col_df, position = nodes_coord, size = nodes_size)
# Assign visual attributes to edges using the same approach as we did for nodes
F2 <- colorRampPalette(c("#FFFF00", "#006400"), bias = length(unique(E(gD)$weight)), space = "rgb", interpolate = "linear")
colCodes <- F2(length(unique(E(gD)$weight)))
#################test parallel####################
cl = createCluster(12, export = list("F2","dsAll","gD","colCodes"), lib = list("igraph","plyr"))
system.time(edges_col<-parSapply(cl,E(gD)$weight, function(x) colCodes[which(sort(unique(E(gD)$weight)) == x)]))
stopCluster(cl)
#############################
#edges_col <- sapply(E(gD)$weight, function(x) colCodes[which(sort(unique(E(gD)$weight)) == x)])
edges_col_df <- as.data.frame(t(col2rgb(edges_col, alpha = FALSE)))
edges_col_df <- cbind(edges_col_df, alpha = rep(1, times = nrow(edges_col_df)))
edges_att_viz <-list(color = edges_col_df)
# Write the network into a gexf (Gephi) file
#write.gexf(nodes = nodes_df, edges = edges_df, nodesAtt = nodes_att, edgesWeight = E(gD)$weight, edgesAtt = edges_att, nodesVizAtt = nodes_att_viz, edgesVizAtt = edges_att_viz, defaultedgetype = "undirected", output = "lesmis.gexf")
# And without edge weights
write.gexf(nodes = nodes_df, edges = edges_df, nodesAtt = nodes_att, edgesAtt = edges_att, nodesVizAtt = nodes_att_viz, edgesVizAtt = edges_att_viz, defaultedgetype = "undirected", output = "arctic.gexf")