Orange: Network Clustering - Revision history

Onnowpurbo: /* Output */

2020-03-12T02:45:52Z

Output

Onnowpurbo: /* Contoh */

2020-03-12T02:29:16Z

Contoh

Onnowpurbo at 03:52, 22 February 2020

2020-02-22T03:52:34Z

Onnowpurbo at 05:12, 29 January 2020

2020-01-29T05:12:53Z

Onnowpurbo at 04:16, 25 January 2020

2020-01-25T04:16:36Z

Onnowpurbo: Created page with "Sumber: https://orange.biolab.si/widget-catalog/networks/networkclustering/ Detect clusters in a network. Inputs Network: An instance of Network Graph. Outputs..."

2020-01-20T02:35:11Z

Created page with "Sumber: https://orange.biolab.si/widget-catalog/networks/networkclustering/ Detect clusters in a network. Inputs Network: An instance of Network Graph. Outputs..."

New page

Sumber: https://orange.biolab.si/widget-catalog/networks/networkclustering/

Detect clusters in a network.

Inputs

Network: An instance of Network Graph.

Outputs

Network: An instance of Network Graph with clustering information appended.

Network Clustering widget finds clusters in a network. Clustering works with two algorithms, one from Raghavan et al. (2007), which uses label propagation to find appropriate clusters, and one from Leung et al. (2009), which builds upon the work from Raghavan and adds hop attenuation as a parameters for cluster formation.

Max. iterations: maximum number of iteration allowed for the algorithm to run (can converge before reaching the maximum).
Clustering method:
Label propagation clustering (Raghavan et al., 2007)
Label propagation clustering (Leung et al., 2009) with hop attenuation.
Above appears the information on the number of clusters found. If Auto-commit is ticked, results will be automatically sent to the output. Alternatively, press Commit.

Example

Network Clustering can help you uncover cliques and highly connected groups in a network. First, we will use Network File to load lastfm.net data set. Then we will pass the network through Network Clustering. The widget found 79 clusters in a network. To visualize the results, use Network Explorer and set Color attribute to Cluster. This will color network nodes with the corresponding cluster color - this is a great way to visualize highly connected groups in dense networks.

Keep in mind that Network Explorer will color the 10 largest clusters and color the rest as ‘Other’.

==Referensi==

* https://orange.biolab.si/widget-catalog/networks/networkclustering/

==Pranala Menarik==

* [[Orange]]

← Older revision		Revision as of 02:45, 12 March 2020
Line 13:		Line 13:
	Network: An instance of Network Graph with clustering information appended.		Network: An instance of Network Graph with clustering information appended.

−	Widget Network Clustering menemukan cluster di network. Clustering bekerja dengan dua algoritma, satu dari Raghavan et al. (2007), yang menggunakan label propagation untuk menemukan cluster yang sesuai, dan satu dari Leung et al. (2009), yang dibangun di atas karya Raghavan dan menambahkan hop attenuation sebagai parameter untuk pembentukan cluster.	+	Widget Network Clustering berusaha menemukan cluster di network. Network Clustering bekerja dengan dua algoritma, satu dari Raghavan et al. (2007), yang menggunakan label propagation untuk menemukan cluster yang sesuai, dan satu dari Leung et al. (2009), yang dibangun di atas karya Raghavan dan menambahkan hop attenuation sebagai parameter untuk pembentukan cluster.

@@ Line 26: / Line 26: @@
 ==Contoh==
-Network Clustering dapat membantu kita mengungkap kumpulan dan highly connected group dalam suatu network. Pertama, kita akan menggunakan Network File untuk me-load dataset lastfm.net. Kemudian kita akan mengirimkan network tersebut ke  Network Clustering. Widget menemukan 79 cluster dalam network. Untuk memvisualisasikan hasil, gunakan Network Explorer dan atur atribut Color ke Cluster. Ini akan mewarnai node network dengan warna cluster yang sesuai - ini adalah cara yang bagus untuk memvisualisasikan highly connected group dalam network yang padat.
+Widget Network Clustering dapat membantu kita mengungkap kumpulan dan highly connected group dalam suatu network. Pertama, kita akan menggunakan Widget Network File untuk me-load dataset lastfm.net. Kemudian kita akan mengirimkan network tersebut ke Widget Network Clustering. Widget Network Clustering menemukan 79 cluster dalam network. Untuk memvisualisasikan hasil, kita mengunakan Widget Network Explorer dan atur atribut Color ke Cluster. Ini akan mewarnai node network dengan warna cluster yang sesuai - ini adalah cara yang baik untuk memvisualisasikan highly connected group dalam network yang padat.
-Perlu diingat bahwa Network Explorer akan mewarnai 10 cluster terbesar dan mewarnai sisanya sebagai 'Other'.
+Perlu diingat / di perhatikan bahwa Widget Network Explorer disini akan mewarnai 10 cluster terbesar dan mewarnai sisanya sebagai 'Other'.
 [[File:Network-clustering-example.png|center|200px|thumb]]
 ==Referensi==

@@ Line 3: / Line 3: @@
-Detect clusters in a network.
+Mendeteksi cluster di sebuah network.
 ==Input==
@@ Line 13: / Line 13: @@
   Network: An instance of Network Graph with clustering information appended.
-Network Clustering widget finds clusters in a network. Clustering works with two algorithms, one from Raghavan et al. (2007), which uses label propagation to find appropriate clusters, and one from Leung et al. (2009), which builds upon the work from Raghavan and adds hop attenuation as a parameters for cluster formation.
+Widget Network Clustering menemukan cluster di network. Clustering bekerja dengan dua algoritma, satu dari Raghavan et al. (2007), yang menggunakan  label propagation untuk menemukan cluster yang sesuai, dan satu dari Leung et al. (2009), yang dibangun di atas karya Raghavan dan menambahkan hop attenuation sebagai parameter untuk pembentukan cluster.
 [[File:Network-clustering-stamped.png|center|200px|thumb]]
@@ Line 25: / Line 26: @@
 ==Contoh==
-Network Clustering can help you uncover cliques and highly connected groups in a network. First, we will use Network File to load lastfm.net data set. Then we will pass the network through Network Clustering. The widget found 79 clusters in a network. To visualize the results, use Network Explorer and set Color attribute to Cluster. This will color network nodes with the corresponding cluster color - this is a great way to visualize highly connected groups in dense networks.
+Network Clustering dapat membantu kita mengungkap kumpulan dan highly connected group dalam suatu network. Pertama, kita akan menggunakan Network File untuk me-load dataset lastfm.net. Kemudian kita akan mengirimkan network tersebut ke  Network Clustering. Widget menemukan 79 cluster dalam network. Untuk memvisualisasikan hasil, gunakan Network Explorer dan atur atribut Color ke Cluster. Ini akan mewarnai node network dengan warna cluster yang sesuai - ini adalah cara yang bagus untuk memvisualisasikan highly connected group dalam network yang padat.
-Keep in mind that Network Explorer will color the 10 largest clusters and color the rest as ‘Other’.
+Perlu diingat bahwa Network Explorer akan mewarnai 10 cluster terbesar dan mewarnai sisanya sebagai 'Other'.
 [[File:Network-clustering-example.png|center|200px|thumb]]

@@ Line 5: / Line 5: @@
 Detect clusters in a network.
-Inputs
+==Input==
-    Network: An instance of Network Graph.
+ Network: An instance of Network Graph.
-Outputs
+==Output==
-    Network: An instance of Network Graph with clustering information appended.
+ Network: An instance of Network Graph with clustering information appended.
 Network Clustering widget finds clusters in a network. Clustering works with two algorithms, one from Raghavan et al. (2007), which uses label propagation to find appropriate clusters, and one from Leung et al. (2009), which builds upon the work from Raghavan and adds hop attenuation as a parameters for cluster formation.
@@ Line 17: / Line 17: @@
 [[File:Network-clustering-stamped.png|center|200px|thumb]]
-    Max. iterations: maximum number of iteration allowed for the algorithm to run (can converge before reaching the maximum).
+* Max. iterations: maximum number of iteration allowed for the algorithm to run (can converge before reaching the maximum).
-    Clustering method:
+* Clustering method:
-        Label propagation clustering (Raghavan et al., 2007)
+** Label propagation clustering (Raghavan et al., 2007)
-        Label propagation clustering (Leung et al., 2009) with hop attenuation.
+** Label propagation clustering (Leung et al., 2009) with hop attenuation.
-    Above appears the information on the number of clusters found. If Auto-commit is ticked, results will be automatically sent to the output. Alternatively, press Commit.
+* Above appears the information on the number of clusters found. If Auto-commit is ticked, results will be automatically sent to the output. Alternatively, press Commit.
 ==Contoh==