在有向图中查找所有节点前驱

Question

I need to find all the connected elements in column ID.

for eg My main Element is 4120882840 in ID Column.

4120882840 is connected to 4120874920, 4120874720 (refer column ID2 ) likewise, 4120874920 is connected to 4121482000 which is further connected to 4121480930 and so on

finally, all the elements that are connected to 4120882840 are [4120882840, 4120874920, 4121482000, 4121480930, 4121480780, 4120874720, 4120871840, 4120871830] total 8 in the list

But I am only getting first 7 ie [4120882840, 4120874920, 4121482000, 4121480930, 4121480780, 4120874720, 4120871840]

file link https://drive.google.com/file/d/1E5_cbGjtKoB6RDSsC7ned-X2RFoF6Rad/view?usp=sharing

This is my Code

import pandas as pd
df = pd.read_csv("Test.csv")
ID = df.iloc[:,1] 
ID2 = df.iloc[:,2] 

x = [4120882840]
for i in range (len(ID)):
    for element in x:
        if element == ID2[i]:
            newID = ID[i]
            #print (newID)
            x.append (newID)
print (x)

Answer 1

It looks like you want to find all predecessors of the node in question. This becomes clearer by inspecting the corresponding component subgraph:

G = nx.from_pandas_edgelist(df, source='ID', target='ID2',
                                create_using=nx.DiGraph)

comps = nx.weakly_connected_components(G)
comp = next(comp for comp in comps if 4120882840 in comp)
H = nx.subgraph(G, comp)
plt.figure(figsize=(10,4))
nx.draw(H, node_color='lightgreen', with_labels=True, node_size=500)

We can use to find the node's predecessors. NetworkX has nx.edge_dfs , where we can set orientation='reverse' to traverse every predecessor edge in reverse order ( upstream ). Then we can just flatten the returned list of tuples to obtain the corresponding nodes:

from itertools import chain 
source = 4120882840

*n, _ = zip(*(nx.edge_dfs(G, source, orientation='reverse')))
print(set(chain.from_iterable(n)))
{4120874720, 4120871840, 4121480930, 4120874920, 4121480780, 
 4121482000, 4120871830, 4120882840}

Answer 2

Is this what you're looking for?

import networkx as nx
graph = nx.from_pandas_edgelist(df, source='ID', target='ID2',
                                create_using=nx.DiGraph)
visited = set()
to_visit = [4120882840]
while to_visit:
    dst = to_visit.pop()
    visited.add(dst)
    for parent in graph.predecessors(dst):
        if parent in visited:
            continue
        to_visit.append(parent)
print(visited)

Output

{4120874720, 4120871840, 4121480930, 4120874920, 4121480780, 4121482000, 4120871830, 4120882840}

Answer 3

You can use mask to check if the element in ID2 and you can use list(set(x)) to remove duplicate

Here is an algorithm that work:

x = [4120882840]
lastLen = 0
while lastLen != len(x):
    lastLen = len(x)
    for i in x:
        x += list(df['ID'][df['ID2'] == i])
        x = list(set(x))