为什么在图形交叉之后缺少节点 - NetworkX，igraph，python和r(Why there are missing nodes after graph intersection - NetworkX, igraph, python and r)

 在尝试获取两个网络/图形之间的交集时，我遇到了一些奇怪的事情。 当我检查结果交叉点时，我发现缺少节点，我希望了解为什么会发生这种情况。 
 
 最初我正在使用python 3.5.2 / pandas 0.17.1。 在Linux Mint 18上，重现问题的数据集和代码在链接上： 数据集和代码 
 
 两个表（链接中附带的Test_01.ncol和Test_02.ncol）都是边缘列表。 
 
 首先，我尝试使用合并功能获得两个带有pandas的图表的交集： 
 
import pandas as pd

# Load graphs
test_01 = pd.read_csv("Test_01.ncol",sep=" ") # Load Net 1
test_02 = pd.read_csv("Test_02.ncol",sep=" ") # Load Net 2
pandas_intersect = pd.merge(test_01, test_02, how='inner', on=['i1', 'i2']) # Intersection by column

pandas_nodes = len(set(pandas_intersect['i1'].tolist() + pandas_intersect['i2'].tolist())) # Store the number of nodes
 
 然后检查合并是否没有问题我将结果的节点数与NetworkX交集的结果节点进行了比较，如下所示： 
 
# Now test with NetworkX
import networkx as nx
n1 = nx.from_pandas_dataframe(test_01, source="i1", target="i2") # Transform net 1 in NetworkX Graph
n2 = nx.from_pandas_dataframe(test_02, source="i1", target="i2") # Transform net 2 in NetworkX Graph
fn = nx.intersection(n1,n2)  # NetworkX Intersection

networkx_nodes = len(fn.nodes()) # Store the number of nodes

# The number of nodes are different!!!
pandas_nodes == networkx_nodes
 
 我认为它可能是节点顺序的东西，这在附加的表中不是规范的，但即使我按规范顺序放置两个数据集，也会丢失节点。 
 
 我的下一个假设是它可能是Pandas或NetworkX中的一个错误，所以我在R（版本3.3.2）和igraph（版本1.0.1）中尝试它： 
 
library("igraph")

# Read Tables
g1 <- read.table("Test_01.ncol",header=TRUE)
g2 <- read.table("Test_02.ncol",header=TRUE)

# Transform Tables in Graphs
g1 <- graph_from_data_frame(g1, directed=FALSE)
g2 <- graph_from_data_frame(g2, directed=FALSE)

# Create igraph interssection
gi <- graph.intersection(g1,g2)

# Save graph intersection
write.graph(gi,"Test_igraph_intersection.ncol", format="ncol")

# Reload graph intersection
gi_r <- read.graph("Test_igraph_intersection.ncol",format="ncol")

# Prepare result summary
Methods <- c("igraph_intersection","pandas_table_intersection")
Vertex_counts <- c(vcount(gi),vcount(gi_r))
Edge_counts <- c(ecount(gi),ecount(gi_r))

# Create Summary Table
info_data = data.frame(Methods, Vertex_counts, Edge_counts)
colnames(info_data) <- c("Method","Vertices","Edges")

# Check info_data
info_data
 
 但是当我看一下info_data时，结果是一样的。 
 
 我知道节点的数量可能会因为交叉过程而减少，但为什么会在我在python上再次将其转换为表格式并保存文件然后再次使用igraph加载之后发生这种情况？ 或者我做错了什么？ 
 
 如果有人可以解释在python中发生的事情或RI欣赏。 我真的需要理解为什么会这样，如果我能相信这些交叉点继续我的工作。 

I'm experiencing something strange while trying to obtain the intersection between two networks/graphs. I found missing nodes when I check the resulting intersection and I wish to understand why this is happening.
 
Originally I'm working with python 3.5.2 / pandas 0.17.1. on Linux Mint 18, and the dataset and code to reproduce the problem is on the link: Dataset and code
 
Both tables (Test_01.ncol and Test_02.ncol attached in the link) are edge lists.
 
First I try to get the intersection of two graph tables with pandas, with merge function:
 
import pandas as pd

# Load graphs
test_01 = pd.read_csv("Test_01.ncol",sep=" ") # Load Net 1
test_02 = pd.read_csv("Test_02.ncol",sep=" ") # Load Net 2
pandas_intersect = pd.merge(test_01, test_02, how='inner', on=['i1', 'i2']) # Intersection by column

pandas_nodes = len(set(pandas_intersect['i1'].tolist() + pandas_intersect['i2'].tolist())) # Store the number of nodes
 
And then to check if the merge was done without problems I compared the resulting number of nodes with the resulting nodes of NetworkX intersection as follows:
 
# Now test with NetworkX
import networkx as nx
n1 = nx.from_pandas_dataframe(test_01, source="i1", target="i2") # Transform net 1 in NetworkX Graph
n2 = nx.from_pandas_dataframe(test_02, source="i1", target="i2") # Transform net 2 in NetworkX Graph
fn = nx.intersection(n1,n2)  # NetworkX Intersection

networkx_nodes = len(fn.nodes()) # Store the number of nodes

# The number of nodes are different!!!
pandas_nodes == networkx_nodes
 
I thought it might be something with the order of nodes, which is not canonical in the tables attached, but even when I put the two dataset on canonical order there are missing nodes.
 
My next hypothesis is that it might a bug in Pandas or NetworkX, so I try it in R (version 3.3.2) and igraph (version 1.0.1):
 
library("igraph")

# Read Tables
g1 <- read.table("Test_01.ncol",header=TRUE)
g2 <- read.table("Test_02.ncol",header=TRUE)

# Transform Tables in Graphs
g1 <- graph_from_data_frame(g1, directed=FALSE)
g2 <- graph_from_data_frame(g2, directed=FALSE)

# Create igraph interssection
gi <- graph.intersection(g1,g2)

# Save graph intersection
write.graph(gi,"Test_igraph_intersection.ncol", format="ncol")

# Reload graph intersection
gi_r <- read.graph("Test_igraph_intersection.ncol",format="ncol")

# Prepare result summary
Methods <- c("igraph_intersection","pandas_table_intersection")
Vertex_counts <- c(vcount(gi),vcount(gi_r))
Edge_counts <- c(ecount(gi),ecount(gi_r))

# Create Summary Table
info_data = data.frame(Methods, Vertex_counts, Edge_counts)
colnames(info_data) <- c("Method","Vertices","Edges")

# Check info_data
info_data
 
But when I take a look into info_data the result was the same.
 
I understand that the number of nodes may decrease because of intersection procedure, but why this is happening just after I convert it to table format again on python and after save the file and then loading it again with igraph? Or I'm doing something wrong? 
 
If someone can explain whats happening in python or R I appreciate. I really need to understand why this is happening and if I can trust in these intersections to continue my work.

原文：https://stackoverflow.com/questions/42516341