Network Level Measures
Measure Value Row count 39.000 Column count 39.000 Link count 147.000 Density 0.099 Components of 1 node (isolates) 2 Components of 2 nodes (dyadic isolates) 0 Components of 3 or more nodes 2 Reciprocity 0.547 Characteristic path length 2.719 Clustering coefficient 0.271 Network levels (diameter) 7.000 Network fragmentation 0.239 Krackhardt connectedness 0.761 Krackhardt efficiency 0.887 Krackhardt hierarchy 0.215 Krackhardt upperboundedness 1.000 Degree centralization 0.353 Betweenness centralization 0.206 Closeness centralization 0.097 Eigenvector centralization 0.483 Reciprocal (symmetric)? No (54% of the links are reciprocal)
Node Level Measures
Measure Min Max Avg Stddev Total degree centrality 0.000 0.434 0.099 0.081 Total degree centrality [Unscaled] 0.000 33.000 7.538 6.176 In-degree centrality 0.000 0.316 0.099 0.071 In-degree centrality [Unscaled] 0.000 12.000 3.769 2.712 Out-degree centrality 0.000 0.553 0.099 0.102 Out-degree centrality [Unscaled] 0.000 21.000 3.769 3.873 Eigenvector centrality 0.000 0.634 0.177 0.142 Eigenvector centrality [Unscaled] 0.000 0.449 0.125 0.100 Eigenvector centrality per component 0.000 0.391 0.112 0.084 Closeness centrality 0.026 0.156 0.110 0.047 Closeness centrality [Unscaled] 0.001 0.004 0.003 0.001 In-Closeness centrality 0.026 0.105 0.082 0.022 In-Closeness centrality [Unscaled] 0.001 0.003 0.002 0.001 Betweenness centrality 0.000 0.232 0.031 0.046 Betweenness centrality [Unscaled] 0.000 326.608 44.000 64.873 Hub centrality 0.000 0.795 0.149 0.171 Authority centrality 0.000 0.503 0.175 0.144 Information centrality 0.000 0.033 0.026 0.013 Information centrality [Unscaled] 0.000 0.000 0.000 0.000 Clique membership count 0.000 21.000 2.949 4.082 Simmelian ties 0.000 0.316 0.049 0.069 Simmelian ties [Unscaled] 0.000 12.000 1.846 2.627 Clustering coefficient 0.000 1.000 0.271 0.263
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: KAPFTI2 (size: 39, density: 0.0991903)
Rank Agent Value Unscaled Context* 1 LYASHI 0.434 33.000 6.999 2 ABRAHAM 0.263 20.000 3.426 3 CHIPATA 0.224 17.000 2.601 4 NKOLOYA 0.197 15.000 2.051 5 CHISOKONE 0.184 14.000 1.776 6 IBRAHIM 0.184 14.000 1.776 7 MUBANGA 0.171 13.000 1.501 8 LWANGA 0.132 10.000 0.677 9 MUKUBWA 0.132 10.000 0.677 10 JOHN 0.118 9.000 0.402 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.099 Mean in random network: 0.099 Std.dev: 0.081 Std.dev in random network: 0.048 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): KAPFTI2
Rank Agent Value Unscaled 1 LYASHI 0.316 12.000 2 ABRAHAM 0.263 10.000 3 CHIPATA 0.211 8.000 4 NKOLOYA 0.211 8.000 5 IBRAHIM 0.211 8.000 6 JOHN 0.184 7.000 7 MUBANGA 0.184 7.000 8 ANGEL 0.158 6.000 9 LWANGA 0.132 5.000 10 MPUNDU 0.132 5.000 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): KAPFTI2
Rank Agent Value Unscaled 1 LYASHI 0.553 21.000 2 ABRAHAM 0.263 10.000 3 CHISOKONE 0.263 10.000 4 CHIPATA 0.237 9.000 5 NKOLOYA 0.184 7.000 6 MUKUBWA 0.184 7.000 7 IBRAHIM 0.158 6.000 8 MESHAK 0.158 6.000 9 MUBANGA 0.158 6.000 10 KAMWEFU 0.132 5.000 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: KAPFTI2 (size: 39, density: 0.0991903)
Rank Agent Value Unscaled Context* 1 LYASHI 0.634 0.449 0.730 2 ABRAHAM 0.425 0.301 -0.057 3 CHIPATA 0.379 0.268 -0.229 4 NKOLOYA 0.344 0.243 -0.363 5 MUKUBWA 0.339 0.240 -0.380 6 IBRAHIM 0.330 0.234 -0.413 7 CHISOKONE 0.323 0.228 -0.442 8 LWANGA 0.278 0.197 -0.610 9 JOHN 0.265 0.187 -0.661 10 MUBANGA 0.261 0.185 -0.673 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.177 Mean in random network: 0.440 Std.dev: 0.142 Std.dev in random network: 0.266 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): KAPFTI2
Rank Agent Value 1 LYASHI 0.391 2 ABRAHAM 0.262 3 CHIPATA 0.234 4 NKOLOYA 0.212 5 MUKUBWA 0.209 6 IBRAHIM 0.204 7 CHISOKONE 0.199 8 LWANGA 0.171 9 JOHN 0.163 10 MUBANGA 0.161 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: KAPFTI2 (size: 39, density: 0.0991903)
Rank Agent Value Unscaled Context* 1 LYASHI 0.156 0.004 -2.788 2 CHIPATA 0.148 0.004 -2.908 3 ABRAHAM 0.147 0.004 -2.925 4 CHISOKONE 0.146 0.004 -2.942 5 NKOLOYA 0.146 0.004 -2.951 6 IBRAHIM 0.145 0.004 -2.959 7 MUKUBWA 0.144 0.004 -2.967 8 KAMWEFU 0.144 0.004 -2.976 9 ZULU 0.143 0.004 -2.984 10 LWANGA 0.143 0.004 -2.992 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.110 Mean in random network: 0.341 Std.dev: 0.047 Std.dev in random network: 0.066 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): KAPFTI2
Rank Agent Value Unscaled 1 KALONGA 0.105 0.003 2 WILLIAM 0.099 0.003 3 ZAKEYO 0.097 0.003 4 CHILUFYA 0.094 0.002 5 IBRAHIM 0.093 0.002 6 LYASHI 0.092 0.002 7 ABRAHAM 0.092 0.002 8 JOHN 0.092 0.002 9 JOSEPH 0.091 0.002 10 CHIPATA 0.091 0.002 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: KAPFTI2 (size: 39, density: 0.0991903)
Rank Agent Value Unscaled Context* 1 LYASHI 0.232 326.608 4.887 2 CHISOKONE 0.119 167.685 1.899 3 IBRAHIM 0.094 132.435 1.236 4 CHIPATA 0.086 120.870 1.018 5 JOHN 0.085 119.160 0.986 6 MUBANGA 0.083 116.952 0.945 7 ABRAHAM 0.082 115.039 0.909 8 KALAMBA 0.065 91.005 0.457 9 ENOCH 0.049 68.433 0.032 10 HENRY 0.049 68.260 0.029 * Number of standard deviations from the mean of a random network of the same size and density
Mean: 0.031 Mean in random network: 0.047 Std.dev: 0.046 Std.dev in random network: 0.038 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): KAPFTI2
Rank Agent Value 1 LYASHI 0.795 2 CHISOKONE 0.427 3 ABRAHAM 0.423 4 CHIPATA 0.391 5 MUKUBWA 0.389 6 NKOLOYA 0.345 7 ZULU 0.283 8 MUBANGA 0.277 9 LWANGA 0.276 10 KAMWEFU 0.258 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): KAPFTI2
Rank Agent Value 1 LYASHI 0.503 2 ABRAHAM 0.491 3 NKOLOYA 0.435 4 CHIPATA 0.401 5 IBRAHIM 0.397 6 JOHN 0.332 7 LWANGA 0.303 8 JOSEPH 0.283 9 ANGEL 0.270 10 MUBANGA 0.268 Information centrality
Calculate the Stephenson and Zelen information centrality measure for each node.
Input network(s): KAPFTI2
Rank Agent Value Unscaled 1 LYASHI 0.033 0.000 2 MUKUBWA 0.033 0.000 3 MESHAK 0.033 0.000 4 KAMWEFU 0.033 0.000 5 CHIPATA 0.033 0.000 6 HASTINGS 0.033 0.000 7 CHISOKONE 0.033 0.000 8 ZULU 0.033 0.000 9 NKUMBULA 0.033 0.000 10 ABRAHAM 0.033 0.000 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): KAPFTI2
Rank Agent Value 1 LYASHI 21.000 2 ABRAHAM 12.000 3 CHIPATA 11.000 4 CHISOKONE 8.000 5 NKOLOYA 6.000 6 MUKUBWA 6.000 7 IBRAHIM 5.000 8 JOHN 5.000 9 KAMWEFU 3.000 10 NKUMBULA 3.000 Simmelian ties
The normalized number of Simmelian ties of each node.
Input network(s): KAPFTI2
Rank Agent Value Unscaled 1 LYASHI 0.316 12.000 2 ABRAHAM 0.211 8.000 3 NKOLOYA 0.184 7.000 4 CHIPATA 0.132 5.000 5 MUBANGA 0.132 5.000 6 LWANGA 0.105 4.000 7 IBRAHIM 0.105 4.000 8 ZULU 0.079 3.000 9 HASTINGS 0.079 3.000 10 HENRY 0.079 3.000 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): KAPFTI2
Rank Agent Value 1 CHIPALO 1.000 2 PAULOS 1.000 3 CHILWA 0.667 4 JOSEPH 0.650 5 HENRY 0.550 6 ZULU 0.500 7 HASTINGS 0.500 8 SIGN 0.500 9 WILLIAM 0.500 10 KAMWEFU 0.450
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 LYASHI LYASHI LYASHI LYASHI LYASHI KALONGA LYASHI LYASHI 2 CHISOKONE CHIPATA ABRAHAM ABRAHAM ABRAHAM WILLIAM ABRAHAM ABRAHAM 3 IBRAHIM ABRAHAM CHIPATA CHIPATA CHIPATA ZAKEYO CHISOKONE CHIPATA 4 CHIPATA CHISOKONE NKOLOYA NKOLOYA NKOLOYA CHILUFYA CHIPATA NKOLOYA 5 JOHN NKOLOYA MUKUBWA MUKUBWA IBRAHIM IBRAHIM NKOLOYA CHISOKONE 6 MUBANGA IBRAHIM IBRAHIM IBRAHIM JOHN LYASHI MUKUBWA IBRAHIM 7 ABRAHAM MUKUBWA CHISOKONE CHISOKONE MUBANGA ABRAHAM IBRAHIM MUBANGA 8 KALAMBA KAMWEFU LWANGA LWANGA ANGEL JOHN MESHAK LWANGA 9 ENOCH ZULU JOHN JOHN LWANGA JOSEPH MUBANGA MUKUBWA 10 HENRY LWANGA MUBANGA MUBANGA MPUNDU CHIPATA KAMWEFU JOHN