Louvian method for desired number of communityFix the seed for the community module in Python that uses networkx moduleSpreading dynamic with community structureCommunity detection on a very large graphDetecting community with python and networkxNetworkx Statistical Inferencetop-down community detection in a networkSingle-community detection algorithmpython - community detection(clustering) using kernighan-lin algorithm in networkx packageHow to do community detection in a edge weighted network/graph?Measuring inter-community interactivity in a network
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Louvian method for desired number of community
Fix the seed for the community module in Python that uses networkx moduleSpreading dynamic with community structureCommunity detection on a very large graphDetecting community with python and networkxNetworkx Statistical Inferencetop-down community detection in a networkSingle-community detection algorithmpython - community detection(clustering) using kernighan-lin algorithm in networkx packageHow to do community detection in a edge weighted network/graph?Measuring inter-community interactivity in a network
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
Louvian method's build-in version (import community) divides the network into communities which gives the maximum modularity. I want to divide my network into two clusters only.
I am passing the graph to the community module and getting 27 clusters but I want two only so that can be used for classification.
How can I do so?.
networkx social-networking
asked Mar 28 at 5:20
anniebeniwalanniebeniwal
32 bronze badges
add a comment |
Louvian method's build-in version (import community) divides the network into communities which gives the maximum modularity. I want to divide my network into two clusters only.
I am passing the graph to the community module and getting 27 clusters but I want two only so that can be used for classification.
How can I do so?.
networkx social-networking
asked Mar 28 at 5:20
anniebeniwalanniebeniwal
32 bronze badges
add a comment |
Louvian method's build-in version (import community) divides the network into communities which gives the maximum modularity. I want to divide my network into two clusters only.
I am passing the graph to the community module and getting 27 clusters but I want two only so that can be used for classification.
How can I do so?.
networkx social-networking
asked Mar 28 at 5:20
anniebeniwalanniebeniwal
32 bronze badges
Louvian method's build-in version (import community) divides the network into communities which gives the maximum modularity. I want to divide my network into two clusters only.
I am passing the graph to the community module and getting 27 clusters but I want two only so that can be used for classification.
How can I do so?.
networkx social-networking
networkx social-networking
asked Mar 28 at 5:20
anniebeniwalanniebeniwal
32 bronze badges
asked Mar 28 at 5:20
anniebeniwalanniebeniwal
32 bronze badges
asked Mar 28 at 5:20
anniebeniwalanniebeniwal
32 bronze badges
asked Mar 28 at 5:20
anniebeniwalanniebeniwal
32 bronze badges
asked Mar 28 at 5:20
anniebeniwalanniebeniwal
32 bronze badges
32 bronze badges
add a comment |
add a comment |
1 Answer
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There are methods designed to return two clusters, sometimes called bisection, for example the Kernighan-Lin algorithm (implemented in NetworkX).
The Louvain algorithm is a bottom-up method which starts by consider each node as its own community. The method merges communities if doing so increases the modularity of the overall partition. The method stops if no such merge is possible. This often happens before only 2 communities are left, so the Louvain algorithm is not a good way to get a bisection. If you insist, you might try to tune the resolution parameter, which governs the size of communities found by Louvain. I think Kernighan-Lin is a much better idea though.
answered Mar 28 at 12:52
Johannes WachsJohannes Wachs
7654 silver badges13 bronze badges
Sir I'm trying to do text document clustering by using community detection algorithms. Like I was trying to do so by using Louvian method but it's dividing words into 8 communities, so I took top 2 and manually checked the accuracy it's giving not that good accuracy. But when I merged all those in 2 and checked accuracy I got above 90 % accuracy.
– anniebeniwal
Mar 29 at 10:39
for example I have a corpus of sports and polities documents so here I want two cluster of words only(one containing words related to sports and other related to polities), so here Kernighan-Lin algorithm may word fine. But Sir what should I do when I have three or more kind of documents in the corpus??.
– anniebeniwal
Mar 29 at 10:39
Moreover Louvian approach (O(n log n)). is faster than Kernighan-Lin method(O(n2 log n)).
– anniebeniwal
Mar 29 at 10:46
If you would like to specify the number of communities, have a look at stochastic block modeling.
– Johannes Wachs
Mar 29 at 22:54
Thank you Sir :)
– anniebeniwal
Apr 8 at 14:41
add a comment |
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There are methods designed to return two clusters, sometimes called bisection, for example the Kernighan-Lin algorithm (implemented in NetworkX).
The Louvain algorithm is a bottom-up method which starts by consider each node as its own community. The method merges communities if doing so increases the modularity of the overall partition. The method stops if no such merge is possible. This often happens before only 2 communities are left, so the Louvain algorithm is not a good way to get a bisection. If you insist, you might try to tune the resolution parameter, which governs the size of communities found by Louvain. I think Kernighan-Lin is a much better idea though.
answered Mar 28 at 12:52
Johannes WachsJohannes Wachs
7654 silver badges13 bronze badges
Sir I'm trying to do text document clustering by using community detection algorithms. Like I was trying to do so by using Louvian method but it's dividing words into 8 communities, so I took top 2 and manually checked the accuracy it's giving not that good accuracy. But when I merged all those in 2 and checked accuracy I got above 90 % accuracy.
– anniebeniwal
Mar 29 at 10:39
for example I have a corpus of sports and polities documents so here I want two cluster of words only(one containing words related to sports and other related to polities), so here Kernighan-Lin algorithm may word fine. But Sir what should I do when I have three or more kind of documents in the corpus??.
– anniebeniwal
Mar 29 at 10:39
Moreover Louvian approach (O(n log n)). is faster than Kernighan-Lin method(O(n2 log n)).
– anniebeniwal
Mar 29 at 10:46
If you would like to specify the number of communities, have a look at stochastic block modeling.
– Johannes Wachs
Mar 29 at 22:54
Thank you Sir :)
– anniebeniwal
Apr 8 at 14:41
add a comment |
There are methods designed to return two clusters, sometimes called bisection, for example the Kernighan-Lin algorithm (implemented in NetworkX).
The Louvain algorithm is a bottom-up method which starts by consider each node as its own community. The method merges communities if doing so increases the modularity of the overall partition. The method stops if no such merge is possible. This often happens before only 2 communities are left, so the Louvain algorithm is not a good way to get a bisection. If you insist, you might try to tune the resolution parameter, which governs the size of communities found by Louvain. I think Kernighan-Lin is a much better idea though.
answered Mar 28 at 12:52
Johannes WachsJohannes Wachs
7654 silver badges13 bronze badges
Sir I'm trying to do text document clustering by using community detection algorithms. Like I was trying to do so by using Louvian method but it's dividing words into 8 communities, so I took top 2 and manually checked the accuracy it's giving not that good accuracy. But when I merged all those in 2 and checked accuracy I got above 90 % accuracy.
– anniebeniwal
Mar 29 at 10:39
for example I have a corpus of sports and polities documents so here I want two cluster of words only(one containing words related to sports and other related to polities), so here Kernighan-Lin algorithm may word fine. But Sir what should I do when I have three or more kind of documents in the corpus??.
– anniebeniwal
Mar 29 at 10:39
Moreover Louvian approach (O(n log n)). is faster than Kernighan-Lin method(O(n2 log n)).
– anniebeniwal
Mar 29 at 10:46
If you would like to specify the number of communities, have a look at stochastic block modeling.
– Johannes Wachs
Mar 29 at 22:54
Thank you Sir :)
– anniebeniwal
Apr 8 at 14:41
add a comment |
There are methods designed to return two clusters, sometimes called bisection, for example the Kernighan-Lin algorithm (implemented in NetworkX).
The Louvain algorithm is a bottom-up method which starts by consider each node as its own community. The method merges communities if doing so increases the modularity of the overall partition. The method stops if no such merge is possible. This often happens before only 2 communities are left, so the Louvain algorithm is not a good way to get a bisection. If you insist, you might try to tune the resolution parameter, which governs the size of communities found by Louvain. I think Kernighan-Lin is a much better idea though.
answered Mar 28 at 12:52
Johannes WachsJohannes Wachs
7654 silver badges13 bronze badges
There are methods designed to return two clusters, sometimes called bisection, for example the Kernighan-Lin algorithm (implemented in NetworkX).
The Louvain algorithm is a bottom-up method which starts by consider each node as its own community. The method merges communities if doing so increases the modularity of the overall partition. The method stops if no such merge is possible. This often happens before only 2 communities are left, so the Louvain algorithm is not a good way to get a bisection. If you insist, you might try to tune the resolution parameter, which governs the size of communities found by Louvain. I think Kernighan-Lin is a much better idea though.
answered Mar 28 at 12:52
Johannes WachsJohannes Wachs
7654 silver badges13 bronze badges
answered Mar 28 at 12:52
Johannes WachsJohannes Wachs
7654 silver badges13 bronze badges
answered Mar 28 at 12:52
Johannes WachsJohannes Wachs
7654 silver badges13 bronze badges
answered Mar 28 at 12:52
Johannes WachsJohannes Wachs
7654 silver badges13 bronze badges
7654 silver badges13 bronze badges
Sir I'm trying to do text document clustering by using community detection algorithms. Like I was trying to do so by using Louvian method but it's dividing words into 8 communities, so I took top 2 and manually checked the accuracy it's giving not that good accuracy. But when I merged all those in 2 and checked accuracy I got above 90 % accuracy.
– anniebeniwal
Mar 29 at 10:39
for example I have a corpus of sports and polities documents so here I want two cluster of words only(one containing words related to sports and other related to polities), so here Kernighan-Lin algorithm may word fine. But Sir what should I do when I have three or more kind of documents in the corpus??.
– anniebeniwal
Mar 29 at 10:39
Moreover Louvian approach (O(n log n)). is faster than Kernighan-Lin method(O(n2 log n)).
– anniebeniwal
Mar 29 at 10:46
If you would like to specify the number of communities, have a look at stochastic block modeling.
– Johannes Wachs
Mar 29 at 22:54
Thank you Sir :)
– anniebeniwal
Apr 8 at 14:41
add a comment |
Sir I'm trying to do text document clustering by using community detection algorithms. Like I was trying to do so by using Louvian method but it's dividing words into 8 communities, so I took top 2 and manually checked the accuracy it's giving not that good accuracy. But when I merged all those in 2 and checked accuracy I got above 90 % accuracy.
– anniebeniwal
Mar 29 at 10:39
for example I have a corpus of sports and polities documents so here I want two cluster of words only(one containing words related to sports and other related to polities), so here Kernighan-Lin algorithm may word fine. But Sir what should I do when I have three or more kind of documents in the corpus??.
– anniebeniwal
Mar 29 at 10:39
Moreover Louvian approach (O(n log n)). is faster than Kernighan-Lin method(O(n2 log n)).
– anniebeniwal
Mar 29 at 10:46
If you would like to specify the number of communities, have a look at stochastic block modeling.
– Johannes Wachs
Mar 29 at 22:54
Thank you Sir :)
– anniebeniwal
Apr 8 at 14:41
Sir I'm trying to do text document clustering by using community detection algorithms. Like I was trying to do so by using Louvian method but it's dividing words into 8 communities, so I took top 2 and manually checked the accuracy it's giving not that good accuracy. But when I merged all those in 2 and checked accuracy I got above 90 % accuracy.
– anniebeniwal
Mar 29 at 10:39
Sir I'm trying to do text document clustering by using community detection algorithms. Like I was trying to do so by using Louvian method but it's dividing words into 8 communities, so I took top 2 and manually checked the accuracy it's giving not that good accuracy. But when I merged all those in 2 and checked accuracy I got above 90 % accuracy.
– anniebeniwal
Mar 29 at 10:39
for example I have a corpus of sports and polities documents so here I want two cluster of words only(one containing words related to sports and other related to polities), so here Kernighan-Lin algorithm may word fine. But Sir what should I do when I have three or more kind of documents in the corpus??.
– anniebeniwal
Mar 29 at 10:39
for example I have a corpus of sports and polities documents so here I want two cluster of words only(one containing words related to sports and other related to polities), so here Kernighan-Lin algorithm may word fine. But Sir what should I do when I have three or more kind of documents in the corpus??.
– anniebeniwal
Mar 29 at 10:39
Moreover Louvian approach (O(n log n)). is faster than Kernighan-Lin method(O(n2 log n)).
– anniebeniwal
Mar 29 at 10:46
Moreover Louvian approach (O(n log n)). is faster than Kernighan-Lin method(O(n2 log n)).
– anniebeniwal
Mar 29 at 10:46
If you would like to specify the number of communities, have a look at stochastic block modeling.
– Johannes Wachs
Mar 29 at 22:54
If you would like to specify the number of communities, have a look at stochastic block modeling.
– Johannes Wachs
Mar 29 at 22:54
Thank you Sir :)
– anniebeniwal
Apr 8 at 14:41
Thank you Sir :)
– anniebeniwal
Apr 8 at 14:41
add a comment |
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