Network Level Measures
Measure Value Row count 39.000 Column count 39.000 Link count 109.000 Density 0.074 Components of 1 node (isolates) 4 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 0.434 Characteristic path length 3.289 Clustering coefficient 0.188 Network levels (diameter) 8.000 Network fragmentation 0.197 Krackhardt connectedness 0.803 Krackhardt efficiency 0.925 Krackhardt hierarchy 0.264 Krackhardt upperboundedness 0.998 Degree centralization 0.172 Betweenness centralization 0.164 Closeness centralization 0.078 Eigenvector centralization 0.463 Reciprocal (symmetric)? No (43% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.237 0.074 0.061 Total degree centrality [Unscaled] 0.000 18.000 5.590 4.634 In-degree centrality 0.000 0.237 0.074 0.056 In-degree centrality [Unscaled] 0.000 9.000 2.795 2.127 Out-degree centrality 0.000 0.316 0.074 0.079 Out-degree centrality [Unscaled] 0.000 12.000 2.795 3.014 Eigenvector centrality 0.000 0.607 0.168 0.152 Eigenvector centrality [Unscaled] 0.000 0.429 0.119 0.107 Eigenvector centrality per component 0.000 0.385 0.107 0.096 Closeness centrality 0.026 0.135 0.098 0.035 Closeness centrality [Unscaled] 0.001 0.004 0.003 0.001 In-Closeness centrality 0.026 0.123 0.091 0.029 In-Closeness centrality [Unscaled] 0.001 0.003 0.002 0.001 Betweenness centrality 0.000 0.203 0.043 0.062 Betweenness centrality [Unscaled] 0.000 285.015 60.333 86.842 Hub centrality 0.000 0.710 0.137 0.180 Authority centrality 0.000 0.562 0.167 0.153 Information centrality 0.000 0.045 0.026 0.014 Information centrality [Unscaled] 0.000 0.825 0.471 0.251 Clique membership count 0.000 11.000 1.769 2.537 Simmelian ties 0.000 0.132 0.019 0.034 Simmelian ties [Unscaled] 0.000 5.000 0.718 1.300 Clustering coefficient 0.000 1.000 0.188 0.239
Key Nodes
This chart shows the Agent that is repeatedly top-ranked in the measures listed below. The value shown is the percentage of measures for which the Agent 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: KAPFTI1 (size: 39, density: 0.0735493)
Rank Agent Value Unscaled Context* 1 CHISOKONE 0.237 18.000 3.907 2 ABRAHAM 0.224 17.000 3.592 3 LYASHI 0.224 17.000 3.592 4 MUKUBWA 0.184 14.000 2.647 5 HENRY 0.158 12.000 2.018 6 ZULU 0.145 11.000 1.703 7 IBRAHIM 0.118 9.000 1.074 8 MUBANGA 0.105 8.000 0.759 9 CHILWA 0.092 7.000 0.444 10 LWANGA 0.092 7.000 0.444 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.074 Mean in random network: 0.074 Std.dev: 0.061 Std.dev in random network: 0.042 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): KAPFTI1
Rank Agent Value Unscaled 1 ABRAHAM 0.237 9.000 2 LYASHI 0.211 8.000 3 HENRY 0.184 7.000 4 CHISOKONE 0.158 6.000 5 CHILWA 0.132 5.000 6 IBRAHIM 0.132 5.000 7 ZULU 0.105 4.000 8 MPUNDU 0.105 4.000 9 JOHN 0.105 4.000 10 JOSEPH 0.105 4.000 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): KAPFTI1
Rank Agent Value Unscaled 1 CHISOKONE 0.316 12.000 2 MUKUBWA 0.316 12.000 3 LYASHI 0.237 9.000 4 ABRAHAM 0.211 8.000 5 ZULU 0.184 7.000 6 LWANGA 0.132 5.000 7 HENRY 0.132 5.000 8 NKOLOYA 0.105 4.000 9 IBRAHIM 0.105 4.000 10 MUBANGA 0.105 4.000 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: KAPFTI1 (size: 39, density: 0.0735493)
Rank Agent Value Unscaled Context* 1 CHISOKONE 0.607 0.429 0.858 2 MUKUBWA 0.528 0.373 0.569 3 ABRAHAM 0.463 0.327 0.332 4 LYASHI 0.443 0.313 0.260 5 ZULU 0.425 0.301 0.195 6 CHILWA 0.325 0.230 -0.172 7 IBRAHIM 0.287 0.203 -0.309 8 LWANGA 0.277 0.196 -0.347 9 ANGEL 0.231 0.163 -0.515 10 HENRY 0.220 0.156 -0.554 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.168 Mean in random network: 0.372 Std.dev: 0.152 Std.dev in random network: 0.274 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): KAPFTI1
Rank Agent Value 1 CHISOKONE 0.385 2 MUKUBWA 0.335 3 ABRAHAM 0.294 4 LYASHI 0.281 5 ZULU 0.270 6 CHILWA 0.206 7 IBRAHIM 0.182 8 LWANGA 0.176 9 ANGEL 0.146 10 HENRY 0.140 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: KAPFTI1 (size: 39, density: 0.0735493)
Rank Agent Value Unscaled Context* 1 MESHAK 0.135 0.004 -2.533 2 CHISOKONE 0.131 0.003 -2.626 3 MUKUBWA 0.131 0.003 -2.626 4 LYASHI 0.128 0.003 -2.693 5 ABRAHAM 0.127 0.003 -2.722 6 ZULU 0.126 0.003 -2.731 7 NKOLOYA 0.124 0.003 -2.776 8 ANGEL 0.123 0.003 -2.812 9 HASTINGS 0.121 0.003 -2.846 10 CHILWA 0.118 0.003 -2.920 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.098 Mean in random network: 0.250 Std.dev: 0.035 Std.dev in random network: 0.045 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): KAPFTI1
Rank Agent Value Unscaled 1 SIGN 0.123 0.003 2 KALUNDWE 0.122 0.003 3 HENRY 0.111 0.003 4 JOSEPH 0.108 0.003 5 WILLIAM 0.107 0.003 6 ANGEL 0.107 0.003 7 ABRAHAM 0.106 0.003 8 IBRAHIM 0.106 0.003 9 MUBANGA 0.105 0.003 10 MPUNDU 0.105 0.003 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: KAPFTI1 (size: 39, density: 0.0735493)
Rank Agent Value Unscaled Context* 1 MUKUBWA 0.203 285.015 0.892 2 ABRAHAM 0.196 275.480 0.851 3 HENRY 0.191 269.193 0.824 4 ANGEL 0.166 233.529 0.670 5 CHISOKONE 0.153 214.757 0.589 6 LYASHI 0.126 177.801 0.430 7 IBRAHIM 0.110 155.169 0.332 8 MUBANGA 0.091 128.229 0.216 9 NYIRENDA 0.066 92.380 0.061 10 JOHN 0.045 62.600 -0.067 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.043 Mean in random network: 0.056 Std.dev: 0.062 Std.dev in random network: 0.165 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): KAPFTI1
Rank Agent Value 1 MUKUBWA 0.710 2 CHISOKONE 0.625 3 LYASHI 0.511 4 ABRAHAM 0.504 5 ZULU 0.485 6 HASTINGS 0.274 7 NKOLOYA 0.259 8 CHILWA 0.198 9 NYIRENDA 0.168 10 LWANGA 0.138 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): KAPFTI1
Rank Agent Value 1 LYASHI 0.562 2 CHISOKONE 0.515 3 ABRAHAM 0.495 4 CHILWA 0.450 5 ZULU 0.441 6 ANGEL 0.290 7 IBRAHIM 0.287 8 LWANGA 0.251 9 NYIRENDA 0.236 10 MPUNDU 0.213 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): KAPFTI1
Rank Agent Value Unscaled 1 CHISOKONE 0.045 0.825 2 MUKUBWA 0.045 0.825 3 MUBANGA 0.045 0.820 4 ZULU 0.042 0.766 5 LWANGA 0.041 0.761 6 LYASHI 0.040 0.739 7 NKOLOYA 0.039 0.719 8 ABRAHAM 0.038 0.692 9 HENRY 0.037 0.675 10 HASTINGS 0.036 0.653 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): KAPFTI1
Rank Agent Value 1 MUKUBWA 11.000 2 CHISOKONE 10.000 3 LYASHI 5.000 4 IBRAHIM 5.000 5 ABRAHAM 4.000 6 ZULU 4.000 7 LWANGA 4.000 8 NYIRENDA 3.000 9 MPUNDU 3.000 10 HENRY 3.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): KAPFTI1
Rank Agent Value Unscaled 1 LYASHI 0.132 5.000 2 ABRAHAM 0.079 3.000 3 ZULU 0.079 3.000 4 CHISOKONE 0.079 3.000 5 JOSEPH 0.079 3.000 6 HENRY 0.079 3.000 7 NKOLOYA 0.053 2.000 8 CHILWA 0.053 2.000 9 MUBANGA 0.053 2.000 10 ANGEL 0.053 2.000 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): KAPFTI1
Rank Agent Value 1 NKUMBULA 1.000 2 CHILWA 0.750 3 HASTINGS 0.667 4 ENOCH 0.500 5 JOSEPH 0.500 6 WILLIAM 0.500 7 ANGEL 0.417 8 BEN 0.333 9 ZULU 0.304 10 SEAMS 0.250
Key Nodes Table
This shows the top scoring nodes side-by-side for selected measures.
Rank Betweenness centrality Closeness centrality Eigenvector centrality Eigenvector centrality per component In-degree centrality In-Closeness centrality Out-degree centrality Total degree centrality 1 MUKUBWA MESHAK CHISOKONE CHISOKONE ABRAHAM SIGN CHISOKONE CHISOKONE 2 ABRAHAM CHISOKONE MUKUBWA MUKUBWA LYASHI KALUNDWE MUKUBWA ABRAHAM 3 HENRY MUKUBWA ABRAHAM ABRAHAM HENRY HENRY LYASHI LYASHI 4 ANGEL LYASHI LYASHI LYASHI CHISOKONE JOSEPH ABRAHAM MUKUBWA 5 CHISOKONE ABRAHAM ZULU ZULU CHILWA WILLIAM ZULU HENRY 6 LYASHI ZULU CHILWA CHILWA IBRAHIM ANGEL LWANGA ZULU 7 IBRAHIM NKOLOYA IBRAHIM IBRAHIM ZULU ABRAHAM HENRY IBRAHIM 8 MUBANGA ANGEL LWANGA LWANGA MPUNDU IBRAHIM NKOLOYA MUBANGA 9 NYIRENDA HASTINGS ANGEL ANGEL JOHN MUBANGA IBRAHIM CHILWA 10 JOHN CHILWA HENRY HENRY JOSEPH MPUNDU MUBANGA LWANGA