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Data is represented as Directed Graph with Weighted Vertices.
The weight of Vertex is equal to the sum of weights of all outgoing edges. The vertices with the maximum weight are highlighted with Green color.
When you hover the mouse on the vertex, highlighted information appears about the vertex and all adjusted vertices.
Gold label displays information about selected vertex in following format:
<vertex name>: <vertex weight> (<number of the adjusted vertices>)
Green labels display information about adjusted vertices in following format:
<vertex name>: <what percentage of the weight of the selected vertex is associated with adjusted vertex> (<number of adjusted vertices>)

This view is useful to define the most active contributors and influencers.


Data is represented as Scatters for each vertex in result set. Entire time range is divided into buckets and amount of contribution is calculated for each bucked separately.

This view is useful to explore trends in contribution.
For regular members the deviation from normal amount might be considered together with development life cycles.
Sometime these deviations might be a sign of descending of engagement
It also shows a pace of inclusion in the team for the new joinees

Data is represented as Graph with Vertices weighted according to theirs Betweenness Centrality(BC) in Directed graph
Betweenness Centrality is an important property of the topology for social graphs
It is equal to the number of the shortest paths from all vertices to all other vertices that pass through that vertex

This view is useful to define culture champions and cross-domain contributors
Vertices with high score of Centrality make communication faster. If we remove the vertex with BC equals to 100 from the graph, it means that 100 paths will become longer
High score of Centrality is also a sign of the presence of bottleneck

Data is represented as Graph with Vertices weighted according to theirs Betweenness Centrality(BC) in Undirected graph
Undirected graph is built based on Directed graph with the idea of Filtering by Reciprocity
User can increase Epsilon to filter more edges.
Betweenness Centrality is an important property of the topology for social graphs
It is equal to the number of the shortest paths from all vertices to all other vertices that pass through that vertex

This view is useful to define culture champions and cross-domain contributors
Vertices with high score of Centrality make communication faster. If we remove the vertex with BC equals to 100 from the graph, it means that 100 paths will become longer

The MCL Algorithm is short for the Markov Cluster Algorithm - unsupervised learning algorithm solving undirected graph clusterization problem.
Detailed description for MCL Algorithm
Undirected graph is built based on Directed graph with the idea of Filtering by Reciprocity
User can increase Epsilon to filter more edges.
Clustering shows the real structure of the organization.