Orange: Network Analysis - Revision history

Onnowpurbo: /* Contoh */

2020-03-12T02:03:29Z

Contoh

← Older revision		Revision as of 02:03, 12 March 2020
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	==Contoh==		==Contoh==

	Contoh workflow ini menunjukkan bagaimana Network Analysis dapat memperkaya workflow. Kita telah menggunakan data lastfm.net sebagai input network dari Network File dan mengirimkannya ke Network Analysis. Kita dapat memutuskan untuk menghitung derajat, derajat sentralitas, dan sentralitas kedekatan pada node-level.		Contoh workflow ini menunjukkan bagaimana Network Analysis dapat memperkaya workflow. Kita telah menggunakan data lastfm.net sebagai input network dari Widget Network File dan mengirimkannya ke Widget Network Analysis. Kita dapat memutuskan untuk menghitung derajat, derajat sentralitas, dan sentralitas kedekatan pada node-level.

	Kita kemudian dapat memvisualisasikan network di Network Explorer. Di widget Network Explorer memberi warna dengan tag terbaik, seperti default untuk dataset ini. Tapi sekarang kita juga dapat mengatur ukuran node agar sesuai dengan sentralitas derajat hasil perhitungan. Ini adalah cara yang baik untuk memvisualisasikan properti network.		Kita kemudian dapat memvisualisasikan network di Widget Network Explorer. Di widget Network Explorer memberi warna dengan tag terbaik, seperti default untuk dataset ini. Tapi sekarang kita juga dapat mengatur ukuran node agar sesuai dengan sentralitas derajat hasil perhitungan. Ini adalah cara yang baik untuk memvisualisasikan properti network.

	[[File:Network-analysis-example.png\|center\|200px\|thumb]]		[[File:Network-analysis-example.png\|center\|200px\|thumb]]

Onnowpurbo: /* Contoh */

2020-03-12T01:53:16Z

Contoh

← Older revision		Revision as of 01:53, 12 March 2020
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	==Contoh==		==Contoh==

	Contoh ~~sederhana~~ ini menunjukkan bagaimana Network Analysis dapat memperkaya workflow. Kita telah menggunakan lastfm.net sebagai input network dari Network File dan mengirimkannya ke Network Analysis. Kita ~~telah~~ memutuskan untuk menghitung derajat, derajat sentralitas, dan sentralitas kedekatan pada node-level.		Contoh workflow ini menunjukkan bagaimana Network Analysis dapat memperkaya workflow. Kita telah menggunakan data lastfm.net sebagai input network dari Network File dan mengirimkannya ke Network Analysis. Kita dapat memutuskan untuk menghitung derajat, derajat sentralitas, dan sentralitas kedekatan pada node-level.

	Kita dapat memvisualisasikan network di Network Explorer. Di widget ~~kita~~ memberi warna dengan tag terbaik, seperti default untuk dataset ini. Tapi sekarang kita juga dapat mengatur ukuran node agar sesuai dengan sentralitas derajat hasil perhitungan. Ini adalah cara yang baik untuk memvisualisasikan properti network.		Kita kemudian dapat memvisualisasikan network di Network Explorer. Di widget Network Explorer memberi warna dengan tag terbaik, seperti default untuk dataset ini. Tapi sekarang kita juga dapat mengatur ukuran node agar sesuai dengan sentralitas derajat hasil perhitungan. Ini adalah cara yang baik untuk memvisualisasikan properti network.

	[[File:Network-analysis-example.png\|center\|200px\|thumb]]		[[File:Network-analysis-example.png\|center\|200px\|thumb]]

Onnowpurbo: /* Output */

2020-03-12T01:11:58Z

Output

← Older revision		Revision as of 01:11, 12 March 2020
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	Items: New properties of a network file.		Items: New properties of a network file.

	Network Analysis ~~widget~~ menghitung rangkuman statistik dari node-level dan graph-level untuk network. Network Analysis ~~widget~~ akan mengeluarkan network dengan hasil komputasi statistik-nya dan sebuah extended item data table (hanya node-level index).		Widget Network Analysis menghitung rangkuman statistik dari node-level dan graph-level untuk network. Widget Network Analysis akan mengeluarkan network dengan hasil komputasi statistik-nya dan sebuah extended item data table (hanya node-level index).

	==Graph level==		==Graph level==

Onnowpurbo: /* Contoh */

2020-02-27T08:39:19Z

Contoh

← Older revision		Revision as of 08:39, 27 February 2020
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	Contoh sederhana ini menunjukkan bagaimana Network Analysis dapat memperkaya workflow. Kita telah menggunakan lastfm.net sebagai input network dari Network File dan mengirimkannya ke Network Analysis. Kita telah memutuskan untuk menghitung derajat, derajat sentralitas, dan sentralitas kedekatan pada node-level.		Contoh sederhana ini menunjukkan bagaimana Network Analysis dapat memperkaya workflow. Kita telah menggunakan lastfm.net sebagai input network dari Network File dan mengirimkannya ke Network Analysis. Kita telah memutuskan untuk menghitung derajat, derajat sentralitas, dan sentralitas kedekatan pada node-level.

	~~We can visualize the~~ network in Network Explorer. ~~In the~~ widget ~~we color by best~~ tag, ~~as is the~~ default ~~for this data set~~. ~~But now we can also set the size of the nodes to correspond to the computed Degree centrality~~. ~~This is a great way to visualize the properties of the~~ network.		Kita dapat memvisualisasikan network di Network Explorer. Di widget kita memberi warna dengan tag terbaik, seperti default untuk dataset ini. Tapi sekarang kita juga dapat mengatur ukuran node agar sesuai dengan sentralitas derajat hasil perhitungan. Ini adalah cara yang baik untuk memvisualisasikan properti network.

	[[File:Network-analysis-example.png\|center\|200px\|thumb]]		[[File:Network-analysis-example.png\|center\|200px\|thumb]]

Onnowpurbo: /* Example */

2020-02-27T08:37:53Z

Example

← Older revision		Revision as of 08:37, 27 February 2020
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	* Closeness centrality: distance to all other nodes.		* Closeness centrality: distance to all other nodes.

	==~~Example~~==		==Contoh==

	~~This simple example shows how~~ Network Analysis ~~can enrich the~~ workflow. ~~We have used~~ lastfm.net ~~as our~~ input network ~~from~~ Network File ~~and sent it to~~ Network Analysis. ~~We’ve decided to compute degree~~, ~~degree centrality and closeness centrality at~~ node level.		Contoh sederhana ini menunjukkan bagaimana Network Analysis dapat memperkaya workflow. Kita telah menggunakan lastfm.net sebagai input network dari Network File dan mengirimkannya ke Network Analysis. Kita telah memutuskan untuk menghitung derajat, derajat sentralitas, dan sentralitas kedekatan pada node-level.

	We can visualize the network in Network Explorer. In the widget we color by best tag, as is the default for this data set. But now we can also set the size of the nodes to correspond to the computed Degree centrality. This is a great way to visualize the properties of the network.		We can visualize the network in Network Explorer. In the widget we color by best tag, as is the default for this data set. But now we can also set the size of the nodes to correspond to the computed Degree centrality. This is a great way to visualize the properties of the network.

	[[File:Network-analysis-example.png\|center\|200px\|thumb]]		[[File:Network-analysis-example.png\|center\|200px\|thumb]]



	==Referensi==		==Referensi==

Onnowpurbo at 08:35, 27 February 2020

2020-02-27T08:35:44Z

← Older revision		Revision as of 08:35, 27 February 2020
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	Statistical analysis of network data.		Statistical analysis dari network data.

	==Input==		==Input==
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	Items: New properties of a network file.		Items: New properties of a network file.

	Network Analysis widget ~~computes~~ node-level ~~and~~ graph-level ~~summary statistics for the~~ network. ~~It outputs a~~ network ~~with the new computed statistics and an~~ extended item data table (node-level ~~indices only~~).		Network Analysis widget menghitung rangkuman statistik dari node-level dan graph-level untuk network. Network Analysis widget akan mengeluarkan network dengan hasil komputasi statistik-nya dan sebuah extended item data table (hanya node-level index).

	==Graph level==		==Graph level==

Onnowpurbo at 05:12, 29 January 2020

2020-01-29T05:12:05Z

Onnowpurbo at 04:08, 25 January 2020

2020-01-25T04:08:41Z

← Older revision		Revision as of 04:08, 25 January 2020
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	Network Analysis widget computes node-level and graph-level summary statistics for the network. It outputs a network with the new computed statistics and an extended item data table (node-level indices only).		Network Analysis widget computes node-level and graph-level summary statistics for the network. It outputs a network with the new computed statistics and an extended item data table (node-level indices only).

	~~####~~Graph level		==Graph level==

			[[File:Network-Analysis-graph.png\|center\|200px\|thumb]]

	Number of nodes: number of vertices in a network.		Number of nodes: number of vertices in a network.
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	Number of weakly connected components: parts of network where replacing all of its directed edges with undirected edges produces a connected (undirected) graph (for directed graphs only).		Number of weakly connected components: parts of network where replacing all of its directed edges with undirected edges produces a connected (undirected) graph (for directed graphs only).

	~~####~~Node level		==Node level==

			[[File:Network-Analysis-nodes.png\|center\|200px\|thumb]]

	Degree: number of edges per node.		Degree: number of edges per node.
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	Closeness centrality: distance to all other nodes.		Closeness centrality: distance to all other nodes.

	Example		==Example==

	This simple example shows how Network Analysis can enrich the workflow. We have used lastfm.net as our input network from Network File and sent it to Network Analysis. We’ve decided to compute degree, degree centrality and closeness centrality at node level.		This simple example shows how Network Analysis can enrich the workflow. We have used lastfm.net as our input network from Network File and sent it to Network Analysis. We’ve decided to compute degree, degree centrality and closeness centrality at node level.
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	We can visualize the network in Network Explorer. In the widget we color by best tag, as is the default for this data set. But now we can also set the size of the nodes to correspond to the computed Degree centrality. This is a great way to visualize the properties of the network.		We can visualize the network in Network Explorer. In the widget we color by best tag, as is the default for this data set. But now we can also set the size of the nodes to correspond to the computed Degree centrality. This is a great way to visualize the properties of the network.

			[[File:Network-analysis-example.png\|center\|200px\|thumb]]

Onnowpurbo: Created page with "Sumber: https://orange.biolab.si/widget-catalog/networks/networkanalysis/ Statistical analysis of network data. Inputs Network: An instance of Network Graph. Items..."

2020-01-20T02:30:57Z

Created page with "Sumber: https://orange.biolab.si/widget-catalog/networks/networkanalysis/ Statistical analysis of network data. Inputs Network: An instance of Network Graph. Items..."

New page

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

Statistical analysis of network data.

Inputs

Network: An instance of Network Graph.
Items: Properties of a network file.

Outputs

Network: An instance of Network Graph with appended information.
Items: New properties of a network file.

Network Analysis widget computes node-level and graph-level summary statistics for the network. It outputs a network with the new computed statistics and an extended item data table (node-level indices only).

####Graph level

Number of nodes: number of vertices in a network.
Number of edges: number of connections in a network.
Average degree: average number of connections per node.
Density: ratio between actual number of edges and maximum number of edges (fully connected graph).
Diameter: maximum eccentricity of the graph.
Radius: minimum eccentricity of the graph.
Average shortest path length: expected distance between two nodes in the graph.
Number of strongly connected components: parts of network where every vertex is reachable from every other vertex (for directed graphs only).
Number of weakly connected components: parts of network where replacing all of its directed edges with undirected edges produces a connected (undirected) graph (for directed graphs only).

####Node level

Degree: number of edges per node.
In-degree: number of incoming edges in a directed graph.
Out-degree: number of outgoing edges in a directed graph.
Average neighbor degree: average degree of neighboring nodes.
Degree centrality: ratio of other nodes connected to the node.
In-degree centrality: ratio of incoming edges to a node in a directed graph.
Out-degree centrality: ratio of outgoing edges from a node in directed graph.
Closeness centrality: distance to all other nodes.

Example

This simple example shows how Network Analysis can enrich the workflow. We have used lastfm.net as our input network from Network File and sent it to Network Analysis. We’ve decided to compute degree, degree centrality and closeness centrality at node level.

We can visualize the network in Network Explorer. In the widget we color by best tag, as is the default for this data set. But now we can also set the size of the nodes to correspond to the computed Degree centrality. This is a great way to visualize the properties of the network.

==Referensi==

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

==Pranala Menarik==

* [[Orange]]

← Older revision		Revision as of 05:12, 29 January 2020
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	Statistical analysis of network data.		Statistical analysis of network data.

	~~Inputs~~		==Input==

	Network: An instance of Network Graph.		Network: An instance of Network Graph.
	Items: Properties of a network file.		Items: Properties of a network file.

	~~Outputs~~		==Output==

	Network: An instance of Network Graph with appended information.		Network: An instance of Network Graph with appended information.
	Items: New properties of a network file.		Items: New properties of a network file.

	Network Analysis widget computes node-level and graph-level summary statistics for the network. It outputs a network with the new computed statistics and an extended item data table (node-level indices only).		Network Analysis widget computes node-level and graph-level summary statistics for the network. It outputs a network with the new computed statistics and an extended item data table (node-level indices only).
Line 20:		Line 20:
	[[File:Network-Analysis-graph.png\|center\|200px\|thumb]]		[[File:Network-Analysis-graph.png\|center\|200px\|thumb]]

	Number of nodes: number of vertices in a network.		* Number of nodes: number of vertices in a network.
	Number of edges: number of connections in a network.		* Number of edges: number of connections in a network.
	Average degree: average number of connections per node.		* Average degree: average number of connections per node.
	Density: ratio between actual number of edges and maximum number of edges (fully connected graph).		* Density: ratio between actual number of edges and maximum number of edges (fully connected graph).
	Diameter: maximum eccentricity of the graph.		* Diameter: maximum eccentricity of the graph.
	Radius: minimum eccentricity of the graph.		* Radius: minimum eccentricity of the graph.
	Average shortest path length: expected distance between two nodes in the graph.		* Average shortest path length: expected distance between two nodes in the graph.
	Number of strongly connected components: parts of network where every vertex is reachable from every other vertex (for directed graphs only).		* Number of strongly connected components: parts of network where every vertex is reachable from every other vertex (for directed graphs only).
	Number of weakly connected components: parts of network where replacing all of its directed edges with undirected edges produces a connected (undirected) graph (for directed graphs only).		* Number of weakly connected components: parts of network where replacing all of its directed edges with undirected edges produces a connected (undirected) graph (for directed graphs only).

	==Node level==		==Node level==
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	[[File:Network-Analysis-nodes.png\|center\|200px\|thumb]]		[[File:Network-Analysis-nodes.png\|center\|200px\|thumb]]

	Degree: number of edges per node.		* Degree: number of edges per node.
	In-degree: number of incoming edges in a directed graph.		* In-degree: number of incoming edges in a directed graph.
	Out-degree: number of outgoing edges in a directed graph.		* Out-degree: number of outgoing edges in a directed graph.
	Average neighbor degree: average degree of neighboring nodes.		* Average neighbor degree: average degree of neighboring nodes.
	Degree centrality: ratio of other nodes connected to the node.		* Degree centrality: ratio of other nodes connected to the node.
	In-degree centrality: ratio of incoming edges to a node in a directed graph.		* In-degree centrality: ratio of incoming edges to a node in a directed graph.
	Out-degree centrality: ratio of outgoing edges from a node in directed graph.		* Out-degree centrality: ratio of outgoing edges from a node in directed graph.
	Closeness centrality: distance to all other nodes.		* Closeness centrality: distance to all other nodes.

	==Example==		==Example==