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Extract edge and communities from the list of nodes

I have a dataset that has more than 50 thousand nodes, and I'm trying to extract possible edges and communities from them. I tried using some graphical tools like gephi, cytoscape, socnet, nodexl and so on to visualize and identify edges and communities, but the list of nodes is too long for these tools. Therefore, I am trying to write a script to gain an edge and community. Other columns are the start and end time of the connection to GPS locations.

Input data:

Id, start_time, end time, GPS1, GPS2

0022d9064bc,1073260801,1073260803,819251,440006
00022d9064bc,1073260803,1073260810,819213,439954
00904b4557d3,1073260803,1073261920,817526,439458
00022de73863,1073260804,1073265410,817558,439525
00904b14b494,1073260804,1073262625,817558,439525
00904b14b494,1073260804,1073265163,817558,439525
00904b14b494,1073260804,1073263786,817558,439525
00022d1406df,1073260807,1073260809,820428,438735
00022d1406df,1073260807,1073260878,820428,438735
00022d623dfe,1073260810,1073276346,819251,440006
00022d7317d7,1073260810,1073276155,819251,440006
00022d9064bc,1073260810,1073272525,819251,440006
00022d9064bc,1073260810,1073260999,819251,440006
00022d9064bc,1073260810,1073260857,819251,440006
0030650c9eda,1073260811,1073260813,820356,439224
00022d0e0cec,1073260813,1073262843,820187,439271
00022d176cf3,1073260813,1073260962,817721,439564
000c30d8d2e8,1073260813,1073260902,817721,439564
00904b243bc4,1073260813,1073260962,817721,439564
00904b2fc34d,1073260813,1073260962,817721,439564
00904b52b839,1073260813,1073260962,817721,439564
00904b9a5a51,1073260813,1073260962,817721,439564
00904ba8b682,1073260813,1073260962,817721,439564
00022d3be9cd,1073260815,1073261114,819269,439403
00022d80381f,1073260815,1073261114,819269,439403
00022dc1b09c,1073260815,1073261114,819269,439403
00022d36a6df,1073260817,1073260836,820761,438607
00022d36a6df,1073260817,1073260845,820761,438607
003065d2d8b6,1073260817,1073267560,817735,439757
00904b0c7856,1073260817,1073265149,817735,439757
00022de73863,1073260825,1073260879,817558,439525
00904b14b494,1073260825,1073260879,817558,439525
00904b312d9e,1073260825,1073260879,817558,439525
00022d15b1c7,1073260826,1073260966,820353,439280
00022dcbe817,1073260826,1073260966,820353,439280

I am trying to implement an undirected weighted / unweighted graph.

+6
1

Pandas, node , , . networkx .

, , :

  • , gps1 gps2.
  • " " . " 5- " . groupby, , .

, start end datetime dtype:

df.start = pd.to_datetime(df.start, unit="s")
df.end = pd.to_datetime(df.end, unit="s")

df.start.describe()
count                      35
unique                     11
top       2004-01-05 00:00:13
freq                        8
first     2004-01-05 00:00:01
last      2004-01-05 00:00:26
Name: start, dtype: object

df.head()
             ID               start                 end    gps1    gps2
0   0022d9064bc 2004-01-05 00:00:01 2004-01-05 00:00:03  819251  440006
1  00022d9064bc 2004-01-05 00:00:03 2004-01-05 00:00:10  819213  439954
2  00904b4557d3 2004-01-05 00:00:03 2004-01-05 00:18:40  817526  439458
3  00022de73863 2004-01-05 00:00:04 2004-01-05 01:16:50  817558  439525
4  00904b14b494 2004-01-05 00:00:04 2004-01-05 00:30:25  817558  439525

,

groupby :

edges = (df.groupby(["gps1",
                     "gps2",
                     pd.Grouper(key="start", 
                                freq=near, 
                                closed="right", 
                                label="right")], 
                   as_index=False)
           .agg({"ID":','.join,
                 "start":"min",
                 "end":"max"})
            .reset_index()
            .rename(columns={"index":"edge",
                             "start":"start_min", 
                             "end":"end_max"})
        )

edges.ID = edges.ID.str.split(",")

edges.head():

   edge    gps1    gps2                                                 ID  \
0     0  817526  439458                                     [00904b4557d3]   
1     1  817558  439525  [00022de73863, 00904b14b494, 00904b14b494, 009...   
2     2  817558  439525         [00022de73863, 00904b14b494, 00904b312d9e]   
3     3  817721  439564  [00022d176cf3, 000c30d8d2e8, 00904b243bc4, 009...   
4     4  817735  439757                       [003065d2d8b6, 00904b0c7856]   

            start_min             end_max  
0 2004-01-05 00:00:03 2004-01-05 00:18:40  
1 2004-01-05 00:00:04 2004-01-05 01:16:50  
2 2004-01-05 00:00:25 2004-01-05 00:01:19  
3 2004-01-05 00:00:13 2004-01-05 00:02:42  
4 2004-01-05 00:00:17 2004-01-05 01:52:40

. ID - , . node -pairs; . , Pandas -fu :

. singleton node None . , if not len(combos): ....

pairs = []
idx = 0
for e in edges.edge.values:
    nodes = edges.loc[edges.edge==e, "ID"].values[0]
    attrs = edges.loc[edges.edge==e, ["gps1","gps2","start_min","end_max"]]
    combos = list(combinations(nodes, 2))
    if not len(combos):
        pair = [e, nodes[0], None]
        pair.extend(attrs.values[0])
        pairs.append(pair)
        idx += 1
    else:
        for combo in combos:
            pair = [e, combo[0], combo[1]]
            pair.extend(attrs.values[0])
            pairs.append(pair)
            idx += 1
cols = ["edge","nodeA","nodeB","gps1","gps2","start_min","end_max"]
pairs_df = pd.DataFrame(pairs, columns=cols)

pairs_df.head():

   edge         nodeA         nodeB    gps1    gps2           start_min  \
0     0  00904b4557d3          None  817526  439458 2004-01-05 00:00:03   
1     1  00022de73863  00904b14b494  817558  439525 2004-01-05 00:00:04   
2     1  00022de73863  00904b14b494  817558  439525 2004-01-05 00:00:04   
3     1  00022de73863  00904b14b494  817558  439525 2004-01-05 00:00:04   
4     1  00904b14b494  00904b14b494  817558  439525 2004-01-05 00:00:04   

              end_max  
0 2004-01-05 00:18:40  
1 2004-01-05 01:16:50  
2 2004-01-05 01:16:50  
3 2004-01-05 01:16:50  
4 2004-01-05 01:16:50

networkx:

import networkx as nx

g = nx.from_pandas_dataframe(pairs_df, "nodeA", "nodeB", edge_attr=True)

# access edge attributes by node pairing:
test_A = "00022de73863"
test_B = "00904b14b494"
g[test_A][test_B]["start_min"]
# output:
Timestamp('2004-01-05 00:00:25')

. , , networkx.

, , . , , - , .

+7

Source: https://habr.com/ru/post/1017396/

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