Book《Mastering Gephi Network Visulization》
Gephi is the tool which I used five years ago during my master study from a course named Complexed Network and Web.
I think I should go through these concepts which I ve learnt before, therefore I can do some meaningful projects lately.
1. Path: The set of connections required for one node to interact with another node;shortest distance between nodes;or shortest route to reach a distct cluster of nodes
shortest path——geodesic path
simple path——a path not repeat any nodes
2. Cycles:
+minimum of three edges and the first and last nodes are the same.
+All other nodes must be distinct
+Cycle cannot traverse any of these nodes more than once.
So in the above case, the node F cannot be a part of cycle.
3. Connectivity(a measurement of the robustness of a graph)——beta,alpha,gamma indexes
Connectivity is defined as the degree of connectedness of a graph
Networks with low conenectivity are inherently fragile —— the removal of a small number of edges serves to weaken the network——disconnect some members from the components of the graph
3.1 beta index: # edges / # nodes: e/v
(more complex, densely connected graphs will exceed a value of one, and migth go much higher. Simple networks will have a score less than one)
3.2 alpha index:# cycles / # possible cycles [0:1]
Tree network—zero alpha index
Perfectly connected network would have a alpha index score of one(impractical and inefficient)
3.3 gamma index:# actual or observed links/ # possible links [0:1]
Typically used to assess temporal (time-base) changes in a network (different equations for planar/non-planar graphs)
4.Centrality
Assume we are examining a subset of a network rather than its entirety.
4.1 closeness centrality
A node with strong closeness centrality would typically have very short paths to all other nodes within the network. (traversing the graph very simple)
Generally Speaking: we would expect this type of node to lie at or near the physical center of graph.
In any event, this category of node is very prominent within the graph and is also likely to have strong degree centrality
4.2 betweenness centrality–often different result than the other centrality measurments
Find nodes that are highly influential in connecting otherwise remote regions of graph
These nodes form a bridge between parts of the graph.
From the above graph, D becomes the bridge between BAF and CEG clusters
4.3 eigenvector centrality