Network Level Measures
Measure Value Row count 39.000 Column count 39.000 Link count 158.000 Density 0.213 Components of 1 node (isolates) 0 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 1 Reciprocity 1.000 Characteristic path length 2.043 Clustering coefficient 0.458 Network levels (diameter) 4.000 Network fragmentation 0.000 Krackhardt connectedness 1.000 Krackhardt efficiency 0.829 Krackhardt hierarchy 0.000 Krackhardt upperboundedness 1.000 Degree centralization 0.441 Betweenness centralization 0.199 Closeness centralization 0.419 Eigenvector centralization 0.334 Reciprocal (symmetric)? Yes
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.026 0.632 0.213 0.126 Total degree centrality [Unscaled] 1.000 24.000 8.103 4.771 In-degree centrality 0.026 0.632 0.213 0.126 In-degree centrality [Unscaled] 1.000 24.000 8.103 4.771 Out-degree centrality 0.026 0.632 0.213 0.126 Out-degree centrality [Unscaled] 1.000 24.000 8.103 4.771 Eigenvector centrality 0.007 0.516 0.199 0.108 Eigenvector centrality [Unscaled] 0.005 0.365 0.141 0.077 Eigenvector centrality per component 0.005 0.365 0.141 0.077 Closeness centrality 0.311 0.704 0.502 0.076 Closeness centrality [Unscaled] 0.008 0.019 0.013 0.002 In-Closeness centrality 0.311 0.704 0.502 0.076 In-Closeness centrality [Unscaled] 0.008 0.019 0.013 0.002 Betweenness centrality 0.000 0.222 0.028 0.041 Betweenness centrality [Unscaled] 0.000 155.888 19.821 28.830 Hub centrality 0.007 0.516 0.199 0.108 Authority centrality 0.007 0.516 0.199 0.108 Information centrality 0.007 0.037 0.026 0.007 Information centrality [Unscaled] 0.690 3.483 2.418 0.683 Clique membership count 0.000 27.000 5.744 5.319 Simmelian ties 0.000 0.632 0.204 0.130 Simmelian ties [Unscaled] 0.000 24.000 7.744 4.944 Clustering coefficient 0.000 1.000 0.458 0.259
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: KAPFTS1 (size: 39, density: 0.213225)
Rank Agent Value Unscaled Context* 1 CHISOKONE 0.632 24.000 6.379 2 MUKUBWA 0.447 17.000 3.570 3 LYASHI 0.395 15.000 2.768 4 ZULU 0.368 14.000 2.366 5 HENRY 0.368 14.000 2.366 6 MUBANGA 0.368 14.000 2.366 7 ABRAHAM 0.342 13.000 1.965 8 IBRAHIM 0.289 11.000 1.163 9 HASTINGS 0.263 10.000 0.761 10 JOSEPH 0.263 10.000 0.761 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.213 Mean in random network: 0.213 Std.dev: 0.126 Std.dev in random network: 0.066 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): KAPFTS1
Rank Agent Value Unscaled 1 CHISOKONE 0.632 24.000 2 MUKUBWA 0.447 17.000 3 LYASHI 0.395 15.000 4 ZULU 0.368 14.000 5 HENRY 0.368 14.000 6 MUBANGA 0.368 14.000 7 ABRAHAM 0.342 13.000 8 IBRAHIM 0.289 11.000 9 HASTINGS 0.263 10.000 10 JOSEPH 0.263 10.000 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): KAPFTS1
Rank Agent Value Unscaled 1 CHISOKONE 0.632 24.000 2 MUKUBWA 0.447 17.000 3 LYASHI 0.395 15.000 4 ZULU 0.368 14.000 5 HENRY 0.368 14.000 6 MUBANGA 0.368 14.000 7 ABRAHAM 0.342 13.000 8 IBRAHIM 0.289 11.000 9 HASTINGS 0.263 10.000 10 JOSEPH 0.263 10.000 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: KAPFTS1 (size: 39, density: 0.213225)
Rank Agent Value Unscaled Context* 1 CHISOKONE 0.516 0.365 -0.138 2 MUKUBWA 0.416 0.294 -0.489 3 LYASHI 0.349 0.247 -0.725 4 MUBANGA 0.344 0.243 -0.743 5 HENRY 0.331 0.234 -0.789 6 ZULU 0.329 0.233 -0.796 7 ABRAHAM 0.302 0.214 -0.891 8 HASTINGS 0.265 0.188 -1.021 9 JOSEPH 0.263 0.186 -1.028 10 IBRAHIM 0.262 0.186 -1.031 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.199 Mean in random network: 0.555 Std.dev: 0.108 Std.dev in random network: 0.284 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): KAPFTS1
Rank Agent Value 1 CHISOKONE 0.365 2 MUKUBWA 0.294 3 LYASHI 0.247 4 MUBANGA 0.243 5 HENRY 0.234 6 ZULU 0.233 7 ABRAHAM 0.214 8 HASTINGS 0.188 9 JOSEPH 0.186 10 IBRAHIM 0.186 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: KAPFTS1 (size: 39, density: 0.213225)
Rank Agent Value Unscaled Context* 1 CHISOKONE 0.704 0.019 4.626 2 MUKUBWA 0.633 0.017 3.156 3 LYASHI 0.603 0.016 2.526 4 MUBANGA 0.594 0.016 2.329 5 ZULU 0.585 0.015 2.138 6 HENRY 0.576 0.015 1.953 7 ABRAHAM 0.559 0.015 1.599 8 KALAMBA 0.551 0.014 1.430 9 IBRAHIM 0.551 0.014 1.430 10 WILLIAM 0.551 0.014 1.430 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.502 Mean in random network: 0.482 Std.dev: 0.076 Std.dev in random network: 0.048 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): KAPFTS1
Rank Agent Value Unscaled 1 CHISOKONE 0.704 0.019 2 MUKUBWA 0.633 0.017 3 LYASHI 0.603 0.016 4 MUBANGA 0.594 0.016 5 ZULU 0.585 0.015 6 HENRY 0.576 0.015 7 ABRAHAM 0.559 0.015 8 KALAMBA 0.551 0.014 9 IBRAHIM 0.551 0.014 10 WILLIAM 0.551 0.014 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: KAPFTS1 (size: 39, density: 0.213225)
Rank Agent Value Unscaled Context* 1 CHISOKONE 0.222 155.888 8.377 2 ZULU 0.097 68.363 2.796 3 MUKUBWA 0.095 67.087 2.714 4 MUBANGA 0.078 54.646 1.921 5 BEN 0.063 44.604 1.281 6 LYASHI 0.061 43.116 1.186 7 KALUNDWE 0.057 40.002 0.987 8 HENRY 0.053 37.030 0.798 9 IBRAHIM 0.036 25.515 0.063 10 ABRAHAM 0.036 25.333 0.052 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.028 Mean in random network: 0.035 Std.dev: 0.041 Std.dev in random network: 0.022 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): KAPFTS1
Rank Agent Value 1 CHISOKONE 0.516 2 MUKUBWA 0.416 3 LYASHI 0.349 4 MUBANGA 0.344 5 HENRY 0.331 6 ZULU 0.329 7 ABRAHAM 0.302 8 HASTINGS 0.265 9 JOSEPH 0.263 10 IBRAHIM 0.262 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): KAPFTS1
Rank Agent Value 1 CHISOKONE 0.516 2 MUKUBWA 0.416 3 LYASHI 0.349 4 MUBANGA 0.344 5 HENRY 0.331 6 ZULU 0.329 7 ABRAHAM 0.302 8 HASTINGS 0.265 9 JOSEPH 0.263 10 IBRAHIM 0.262 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): KAPFTS1
Rank Agent Value Unscaled 1 CHISOKONE 0.037 3.483 2 MUKUBWA 0.035 3.289 3 LYASHI 0.033 3.132 4 MUBANGA 0.033 3.114 5 HENRY 0.033 3.103 6 ZULU 0.033 3.099 7 ABRAHAM 0.032 3.018 8 IBRAHIM 0.031 2.932 9 WILLIAM 0.030 2.860 10 HASTINGS 0.030 2.850 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): KAPFTS1
Rank Agent Value 1 CHISOKONE 27.000 2 MUKUBWA 17.000 3 LYASHI 14.000 4 MUBANGA 13.000 5 HENRY 11.000 6 ABRAHAM 10.000 7 ZULU 10.000 8 JOHN 9.000 9 IBRAHIM 8.000 10 WILLIAM 8.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): KAPFTS1
Rank Agent Value Unscaled 1 CHISOKONE 0.632 24.000 2 MUKUBWA 0.447 17.000 3 LYASHI 0.395 15.000 4 HENRY 0.368 14.000 5 ABRAHAM 0.342 13.000 6 ZULU 0.342 13.000 7 MUBANGA 0.342 13.000 8 IBRAHIM 0.289 11.000 9 HASTINGS 0.263 10.000 10 JOSEPH 0.263 10.000 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): KAPFTS1
Rank Agent Value 1 NKUMBULA 1.000 2 ENOCH 1.000 3 ADRIAN 1.000 4 DONALD 0.933 5 ANGEL 0.933 6 KAMWEFU 0.833 7 SEAMS 0.611 8 CHIPATA 0.600 9 NKOLOYA 0.600 10 CHILUFYA 0.556
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 CHISOKONE CHISOKONE CHISOKONE CHISOKONE CHISOKONE CHISOKONE CHISOKONE CHISOKONE 2 ZULU MUKUBWA MUKUBWA MUKUBWA MUKUBWA MUKUBWA MUKUBWA MUKUBWA 3 MUKUBWA LYASHI LYASHI LYASHI LYASHI LYASHI LYASHI LYASHI 4 MUBANGA MUBANGA MUBANGA MUBANGA ZULU MUBANGA ZULU ZULU 5 BEN ZULU HENRY HENRY HENRY ZULU HENRY HENRY 6 LYASHI HENRY ZULU ZULU MUBANGA HENRY MUBANGA MUBANGA 7 KALUNDWE ABRAHAM ABRAHAM ABRAHAM ABRAHAM ABRAHAM ABRAHAM ABRAHAM 8 HENRY KALAMBA HASTINGS HASTINGS IBRAHIM KALAMBA IBRAHIM IBRAHIM 9 IBRAHIM IBRAHIM JOSEPH JOSEPH HASTINGS IBRAHIM HASTINGS HASTINGS 10 ABRAHAM WILLIAM IBRAHIM IBRAHIM JOSEPH WILLIAM JOSEPH JOSEPH