Standard Network Analysis: organization---organization

Standard Network Analysis: organization---organization

Input data: organization---organization

Start time: Tue Oct 18 12:09:18 2011

Return to table of contents

Network Level Measures

MeasureValue
Row count38.000
Column count38.000
Link count28.000
Density0.019
Components of 1 node (isolates)17
Components of 2 nodes (dyadic isolates)1
Components of 3 or more nodes2
Reciprocity0.087
Characteristic path length2.653
Clustering coefficient0.136
Network levels (diameter)8.000
Network fragmentation0.855
Krackhardt connectedness0.145
Krackhardt efficiency0.905
Krackhardt hierarchy0.957
Krackhardt upperboundedness0.500
Degree centralization0.039
Betweenness centralization0.007
Closeness centralization0.001
Eigenvector centralization1.091
Reciprocal (symmetric)?No (8% of the links are reciprocal)

Node Level Measures

MeasureMinMaxAvgStddev
Total degree centrality0.0000.0420.0050.008
Total degree centrality [Unscaled]0.00022.0002.3954.252
In-degree centrality0.0000.0450.0050.009
In-degree centrality [Unscaled]0.00012.0001.3162.341
Out-degree centrality0.0000.0640.0050.012
Out-degree centrality [Unscaled]0.00017.0001.3163.188
Eigenvector centrality0.0001.1130.0790.215
Eigenvector centrality [Unscaled]0.0000.7870.0560.152
Eigenvector centrality per component0.0000.2900.0290.057
Closeness centrality0.0040.0050.0040.000
Closeness centrality [Unscaled]0.0000.0000.0000.000
In-Closeness centrality0.0040.0040.0040.000
In-Closeness centrality [Unscaled]0.0000.0000.0000.000
Betweenness centrality0.0000.0080.0010.002
Betweenness centrality [Unscaled]0.00010.0000.9212.310
Hub centrality0.0001.2890.0580.222
Authority centrality0.0001.1330.0740.217
Information centrality0.0000.1720.0260.045
Information centrality [Unscaled]0.0003.9510.6051.033
Clique membership count0.0004.0000.3950.875
Simmelian ties0.0000.0000.0000.000
Simmelian ties [Unscaled]0.0000.0000.0000.000
Clustering coefficient0.0001.0000.1360.253

Key Nodes

This chart shows the Organization that is repeatedly top-ranked in the measures listed below. The value shown is the percentage of measures for which the Organization was ranked in the top three.

Total degree centrality

The Total Degree Centrality of a node is the normalized sum of its row and column degrees. Individuals or organizations who are "in the know" are those who are linked to many others and so, by virtue of their position have access to the ideas, thoughts, beliefs of many others. Individuals who are "in the know" are identified by degree centrality in the relevant social network. Those who are ranked high on this metrics have more connections to others in the same network. The scientific name of this measure is total degree centrality and it is calculated on the agent by agent matrices.

Input network: organization---organization (size: 38, density: 0.0193906)

RankOrganizationValueUnscaledContext*
1hamas0.04222.0001.006
2islamic_jihad0.02513.0000.240
3al-qaeda0.0137.000-0.271
4hezbollah0.0137.000-0.271
5militari0.0137.000-0.271
6network0.0105.000-0.441
7al-aksa0.0105.000-0.441
8palestinian_authority0.0084.000-0.526
9artilleri0.0063.000-0.611
10guerrilla0.0042.000-0.697

* Number of standard deviations from the mean of a random network of the same size and density

Mean: 0.005Mean in random network: 0.019
Std.dev: 0.008Std.dev in random network: 0.022

Back to top

In-degree centrality

The In Degree Centrality of a node is its normalized in-degree. For any node, e.g. an individual or a resource, the in-links are the connections that the node of interest receives from other nodes. For example, imagine an agent by knowledge matrix then the number of in-links a piece of knowledge has is the number of agents that are connected to. The scientific name of this measure is in-degree and it is calculated on the agent by agent matrices.

Input network(s): organization---organization

RankOrganizationValueUnscaled
1hamas0.04512.000
2militari0.0267.000
3islamic_jihad0.0195.000
4al-aksa0.0154.000
5network0.0113.000
6artilleri0.0113.000
7militia0.0082.000
8headquart0.0082.000
9al-qaeda0.0082.000
10fbi0.0041.000

Back to top

Out-degree centrality

For any node, e.g. an individual or a resource, the out-links are the connections that the node of interest sends to other nodes. For example, imagine an agent by knowledge matrix then the number of out-links an agent would have is the number of pieces of knowledge it is connected to. The scientific name of this measure is out-degree and it is calculated on the agent by agent matrices. Individuals or organizations who are high in most knowledge have more expertise or are associated with more types of knowledge than are others. If no sub-network connecting agents to knowledge exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by knowledge matrices. Individuals or organizations who are high in "most resources" have more resources or are associated with more types of resources than are others. If no sub-network connecting agents to resources exists, then this measure will not be calculated. The scientific name of this measure is out degree centrality and it is calculated on agent by resource matrices.

Input network(s): organization---organization

RankOrganizationValueUnscaled
1hamas0.06417.000
2islamic_jihad0.0308.000
3al-qaeda0.0236.000
4hezbollah0.0236.000
5palestinian_authority0.0154.000
6network0.0082.000
7guerrilla0.0041.000
8united_nations0.0041.000
9treasuridepart0.0041.000
10deleg0.0041.000

Back to top

Eigenvector centrality

Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Leaders of strong cliques are individuals who or organizations who are collected to others that are themselves highly connected to each other. In other words, if you have a clique then the individual most connected to others in the clique and other cliques, is the leader of the clique. Individuals or organizations who are connected to many otherwise isolated individuals or organizations will have a much lower score in this measure then those that are connected to groups that have many connections themselves. The scientific name of this measure is eigenvector centrality and it is calculated on agent by agent matrices.

Input network: organization---organization (size: 38, density: 0.0193906)

RankOrganizationValueUnscaledContext*
1hamas1.1130.787-0.007
2islamic_jihad0.6530.462-1.205
3militari0.3750.265-1.931
4palestinian_authority0.3090.219-2.101
5al-aksa0.2640.187-2.219
6treasuridepart0.1170.083-2.602
7headquart0.1170.083-2.602
8al-qaeda0.0380.027-2.807
9network0.0100.007-2.881
10united_nations0.0030.002-2.898

* Number of standard deviations from the mean of a random network of the same size and density

Mean: 0.079Mean in random network: 1.116
Std.dev: 0.215Std.dev in random network: 0.384

Back to top

Eigenvector centrality per component

Calculates the principal eigenvector of the network. A node is central to the extent that its neighbors are central. Each component is extracted as a separate network, Eigenvector Centrality is computed on it and scaled according to the component size. The scores are then combined into a single result vector.

Input network(s): organization---organization

RankOrganizationValue
1hamas0.290
2islamic_jihad0.170
3militari0.098
4hezbollah0.091
5palestinian_authority0.081
6artilleri0.076
7al-aksa0.069
8committe0.037
9plo0.037
10militia0.035

Back to top

Closeness centrality

The average closeness of a node to the other nodes in a network (also called out-closeness). Loosely, Closeness is the inverse of the average distance in the network from the node to all other nodes.

Input network: organization---organization (size: 38, density: 0.0193906)

RankOrganizationValueUnscaledContext*
1deleg0.0050.000917.086
2united_nations0.0040.000916.722
3palestinian_authority0.0040.000916.709
4hamas0.0040.000916.382
5islamic_jihad0.0040.000916.380
6al-qaeda0.0040.000916.378
7hezbollah0.0040.000916.056
8guerrilla0.0040.000916.052
9network0.0040.000915.454
10treasuridepart0.0040.000915.453

* Number of standard deviations from the mean of a random network of the same size and density

Mean: 0.004Mean in random network: -0.360
Std.dev: 0.000Std.dev in random network: 0.000

Back to top

In-Closeness centrality

The average closeness of a node from the other nodes in a network. Loosely, Closeness is the inverse of the average distance in the network to the node and from all other nodes.

Input network(s): organization---organization

RankOrganizationValueUnscaled
1militari0.0040.000
2headquart0.0040.000
3al-aksa0.0040.000
4fbi0.0040.000
5hawala0.0040.000
6militia0.0040.000
7administr0.0040.000
8treasuridepart0.0040.000
9network0.0040.000
10al-qaeda0.0040.000

Back to top

Betweenness centrality

The Betweenness Centrality of node v in a network is defined as: across all node pairs that have a shortest path containing v, the percentage that pass through v. Individuals or organizations that are potentially influential are positioned to broker connections between groups and to bring to bear the influence of one group on another or serve as a gatekeeper between groups. This agent occurs on many of the shortest paths between other agents. The scientific name of this measure is betweenness centrality and it is calculated on agent by agent matrices.

Input network: organization---organization (size: 38, density: 0.0193906)

RankOrganizationValueUnscaledContext*
1al-qaeda0.00810.000-0.764
2hamas0.0057.000-0.787
3network0.0056.000-0.795
4united_nations0.0056.000-0.795
5hezbollah0.0023.000-0.818
6al-aksa0.0022.000-0.826
7treasuridepart0.0011.000-0.833

* Number of standard deviations from the mean of a random network of the same size and density

Mean: 0.001Mean in random network: 0.082
Std.dev: 0.002Std.dev in random network: 0.098

Back to top

Hub centrality

A node is hub-central to the extent that its out-links are to nodes that have many in-links. Individuals or organizations that act as hubs are sending information to a wide range of others each of whom has many others reporting to them. Technically, an agent is hub-central if its out-links are to agents that have many other agents sending links to them. The scientific name of this measure is hub centrality and it is calculated on agent by agent matrices.

Input network(s): organization---organization

RankOrganizationValue
1hamas1.289
2islamic_jihad0.458
3palestinian_authority0.354
4al-qaeda0.043
5treasuridepart0.033
6al-aksa0.014
7united_nations0.000
8hezbollah0.000
9shia0.000
10network0.000

Back to top

Authority centrality

A node is authority-central to the extent that its in-links are from nodes that have many out-links. Individuals or organizations that act as authorities are receiving information from a wide range of others each of whom sends information to a large number of others. Technically, an agent is authority-central if its in-links are from agents that have are sending links to many others. The scientific name of this measure is authority centrality and it is calculated on agent by agent matrices.

Input network(s): organization---organization

RankOrganizationValue
1hamas1.133
2islamic_jihad0.664
3militari0.373
4al-aksa0.316
5headquart0.134
6treasuridepart0.133
7palestinian_authority0.036
8network0.013
9al-qaeda0.005
10administr0.004

Back to top

Information centrality

Calculate the Stephenson and Zelen information centrality measure for each node.

Input network(s): organization---organization

RankOrganizationValueUnscaled
1hamas0.1723.951
2islamic_jihad0.1353.096
3al-qaeda0.1262.897
4hezbollah0.1162.658
5palestinian_authority0.1002.307
6network0.0671.540
7united_nations0.0441.006
8al-aksa0.0430.989
9deleg0.0420.977
10treasuridepart0.0420.966

Back to top

Clique membership count

The number of distinct cliques to which each node belongs. Individuals or organizations who are high in number of cliques are those that belong to a large number of distinct cliques. A clique is defined as a group of three or more actors that have many connections to each other and relatively fewer connections to those in other groups. The scientific name of this measure is clique count and it is calculated on the agent by agent matrices.

Input network(s): organization---organization

RankOrganizationValue
1hamas4.000
2al-aksa3.000
3islamic_jihad2.000
4militia1.000
5treasuridepart1.000
6headquart1.000
7hezbollah1.000
8militari1.000
9shia1.000

Back to top

Simmelian ties

The normalized number of Simmelian ties of each node.

Input network(s): organization---organization

RankOrganizationValueUnscaled
1All nodes have this value0.000

Back to top

Clustering coefficient

Measures the degree of clustering in a network by averaging the clustering coefficient of each node, which is defined as the density of the node's ego network.

Input network(s): organization---organization

RankOrganizationValue
1palestinian_authority1.000
2administr1.000
3treasuridepart0.500
4headquart0.500
5militari0.444
6islamic_jihad0.333
7al-aksa0.313
8militia0.250
9united_nations0.250
10shia0.250

Back to top

Key Nodes Table

This shows the top scoring nodes side-by-side for selected measures.

RankBetweenness centralityCloseness centralityEigenvector centralityEigenvector centrality per componentIn-degree centralityIn-Closeness centralityOut-degree centralityTotal degree centrality
1al-qaedadeleghamashamashamasmilitarihamashamas
2hamasunited_nationsislamic_jihadislamic_jihadmilitariheadquartislamic_jihadislamic_jihad
3networkpalestinian_authoritymilitarimilitariislamic_jihadal-aksaal-qaedaal-qaeda
4united_nationshamaspalestinian_authorityhezbollahal-aksafbihezbollahhezbollah
5hezbollahislamic_jihadal-aksapalestinian_authoritynetworkhawalapalestinian_authoritymilitari
6al-aksaal-qaedatreasuridepartartilleriartillerimilitianetworknetwork
7treasurideparthezbollahheadquartal-aksamilitiaadministrguerrillaal-aksa
8fbiguerrillaal-qaedacommitteheadquarttreasuridepartunited_nationspalestinian_authority
9agencnetworknetworkploal-qaedanetworktreasuridepartartilleri
10polictreasuridepartunited_nationsmilitiafbial-qaedadelegguerrilla