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The graph is directed. This means that a comment left on someone else’s blog doesn’t mean that the comment also appears on the commenter’s blog. Just like Twitter, you can follow someone without being followed back.The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm. There are various layout options available, but this one makes the information easier to read. The vertex colours are based on in-degree values. A vertex represents an individual (usually the name of the blog, where the commenter can’t be associated with a blog, it will be the name of the commenter only). In-degree is the number of comments left on a blog. The vertex sizes are based on in-degree values.Overall Graph Metrics:Vertices: 874 In this graph, probably the number of individuals represented, but it’s possible that some of the blog titles and commenters are one and the same.Unique Edges: 612 An edge is the link between vertices. In 612 instances, the links are unique i.e. they occur only once between blog x and commenter y.Edges With Duplicates: 395 In 395 instances, blog x and commenter y are at each end of an edge (or link).Total Edges: 1007Self-Loops: 137In 137 instances, blog x left a comment on their own blog post.Reciprocated Vertex Pair Ratio: 0.00142857142857143 This will be a figure between 0 and 1. If all edges were connected, the value would be 1. As it is, the figure is close to 0 suggesting that this network isn’t very connected. Duplicate edges and self-loops are ignored.Reciprocated Edge Ratio: 0.00285306704707561 The number of edges that are reciprocated divided by the total number of edges. Duplicate edges and self-loops are ignored.Connected Components: 188A set of vertices that are connected to each other but not to the rest of the graph.Single-Vertex Connected Components: 6The number of connected components that have only one vertex.Maximum Vertices in a Connected Component: 160The number of vertices in the connected component that has the most vertices.Maximum Edges in a Connected Component: 271The number of edges in the connected component that has the most edges.Maximum Geodesic Distance (Diameter): 16The maximum geodesic distance among all vertex pairs, where geodesic distance is the distance between two vertices along the shortest path between them.Average Geodesic Distance: 5.922148Graph Density: 0.000918739400420969This is a ratio that compares the number of edges in the graph with the maximum number of edges the graph would have if all the vertices were connected to each other. Duplicate edges and self-loops are ignored. Again, as this figure is very close to 0, it suggests that this graph isn’t very dense i.e. not all possible edges are fulfilled.Other metrics that can be calculated for individuals (vertices) within the graph are as follows:Betweenness centrality: How far apart are the people represented in the graph? “The notion of paths is central to the study of networks. Perhaps one of the most natural questions to ask about any two people in a network it is “How far apart are they?” This distance is measured simply: the distance between people who are not neighbors is measured by the smallest number of neighbor-to-neighbor hops from one to the other. For instance, people who are not your neighbors, but are your neighbors’ neighbors, are a distance 2 from you, and so on. The shortest path between two people is called the “geodesic distance” and is used in many centrality metrics. For example, betweenness centrality is a measure of how often a given vertex lies on the shortest path between two other vertices. This can be thought of as a kind of “bridge” score, a measure of how much removing a person would disrupt the connections between other people in the network. The idea of brokering is often captured in the measure of betweenness centrality. “A vertex with high betweenness centrality has a large influence on the transfer of items through the network.Closeness centrality: “…a low closeness centrality means that a person is directly connected or “just a hop away” from most others in the network.”Eigenvector centrality: A measure of the influence of a vertex in a network. “Eigenvector centrality is a more sophisticated view of centrality: a person with few connections could have a very high eigenvector centrality if those few connections were themselves very well connected. Eigenvector centrality allows for connections to have a variable value, so that connecting to some vertices has more benefit than connecting to others. The PageRank algorithm used by Google’s search engine is a variant of Eigenvector Centrality.”Clustering coefficient: If the people you know also know each other, your clustering coefficient will be high. The formula gives a result of between 0 and 1, where 1 means all possible connections between you and your ‘friends’ are realised. The highest score in this graph is 0.67.NodeXL Version: 1.0.1.359 ................
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