我正在使用igraph R包进行一些网络分析。
我必须操纵定向加权邻接矩阵（从 igraph 对象中提取函数_as_adjacency_matrix（...）_，以获得不同的矩阵，考虑两个节点彼此共享的传入链接的数量和权重。
假设4个节点：节点 A 连接到节点 C ，节点 B 连接到 A ，节点 C 连接到节点 A 和 B ，节点 D 连接到 A 所有链接都将被指示。
在此设置中， A 和 B 共享来自 C 的内向链接，但没有其他节点共享任何传入链接。
因此，我想创建一个能够从原始列表中创建有向加权邻接列表的例程，其中每个条目[i，j]表示传入边缘值的总和节点i，j彼此共享。
结果必须是一个对称的逻辑矩阵（只有真/假值），位于结果＆＃34;公共链接的前面。相反指向的邻接矩阵。
回到我的例子，只有条目[ A ， B ]和[ B ， A ]应该有一个非零值，等于共享连接节点的内边缘值（[ A ， B ]应该包含[ C < / em> - ＆gt; A ]值，而[ B ， A ]应该包含[ C - ＆gt; B ]值）。
非常感谢任何有关它的建议
答案 0 :(得分：0)
有趣但有点模糊定义的问题。我在理解你想要的输出时遇到了一些麻烦，但我想我明白了。我在下面的第一次尝试中留下了代码。您提供的示例数据我称之为g。
无论哪种方式，我认为你可以从这个代码示例中继续下去。我非常愿意以更聪明的方式做到这一点而没有速度循环，但这是我能想到的最教学的代码，因为我不确定所需的输出。
如果我正确理解了您的问题，您要求的内容将在列表ul中输出，其中ul[[x]][[3]]包含图表的E（），其中边缘从节点i（ul[[x]][[1]]）到i和j中的每个节点共享图g中的传入链接。
library(igraph)
# Assume 4 nodes:
# - node A is connected to node C,
# - node B is connected to A,
# - node C is connected to node A and B,
# - node D connected to A
m <- matrix(ncol=4,c(0,0,1,0,
1,0,0,0,
1,1,0,0,
1,0,0,0), byrow=T)
colnames(m) <- rownames(m) <- c("A","B","C","D")
# Uncomment this stuff to use random network instead
# g <- erdos.renyi.game(n=12, 16, type="gnm", directed=TRUE, loops=FALSE)
# m <- as.matrix(as_adjacency_matrix(g))
# Check that the data is ok
graph_from_adjacency_matrix(m, mode="directed")
g <- graph_from_adjacency_matrix(m, mode="directed")
# Directed weighted adjacency list from the original one, where
# each entry [i,j] represents the sum of the value of incoming
# edge that node i,j share with each other.
# I first missunderstood your question and wrote this output
# This output will be an edgelist containing node-pairs i and j and
# the strength related to the number of other nodes whith which they
# share incoming links.
el <- matrix(ncol=3, nrow=0)
colnames(el) <- c("i","j","strength")
# I then reread your question and made this output containing a list
# wehre every node-pair which share incoming links from the same nodes
# contain the E()-object of igraph-edges from i to each of the nodes
# from which both i and j recieve incoming links in the graph g.
ul <- list()
# Use the empty graph like g to build edgelists
temp.g <- g %>% delete_edges(E(g)) # an empty graph
for(i in V(g)){
for(j in V(g)){
# Each node pair is i j for every node in g
if(i == j){next}
# Neighborhod() lists linked nodes, in this case at the distance
# of exactly 1 (mindist and order) for node x using the "in"-coming
# links:
in.to.i <- neighborhood(g, order=1, nodes=i, mode="in", mindist=1)
in.to.j <- neighborhood(g, order=1, nodes=j, mode="in", mindist=1)
# These are the nodes which all link to both i and j
shared.incoming <- intersect(in.to.i[[1]], in.to.j[[1]])
# Make a new graph (gg) with links from each node FROM which i and j both
# share incoming ties in g TO i.
# In the edgelist ul, each row can be read like:
# In graph g, "i" has "edges" incoming ties in common with "j"
gg <- temp.g %>% add_edges(unlist(lapply(shared.incoming, function(x) c(x,i)) ))
# E(gg) is what you want. Add it to the output-list
ul[[length(ul)+1]] <- list(i, j, as_edgelist(gg, names=T))
# how many nodes link to both i and j?
el <- rbind(el,c("i"=i, "j"=j, "edges"=length(shared.incoming) ) )
}
}
# The whole list of el contains all possible pairs
el
# Strip entries in the edgelist where a pair of nodes don't share any
# in-linking nodes at all
el <- el[el[,'strength'] != 0, ]
# Since nodes that share in-linking nodes are ALWAYS structually equivilent
# in that they both share in-links from the same other nodes, there is never
# any idea to have this edge-list directed.
# Make the edge-list one-directed by deleting duplicate pairs
el <- el[el[,'i'] < el[,'j'], ]
# In graph g, these node-pairs share the number of [strength] incoming links
# from the same other nodes.
(el)
# The whole list of ul contains all possible pairs
ul
# You only wanted the pairs which actually contained any shared incoming nodes
keep.from.ul <- unlist(lapply(ul, function(x) ifelse( nrow(x[[3]]) > 0, TRUE, FALSE) ))
ul <- ul[keep.from.ul]