RDF Graph Measures for the Analysis of RDF Graphs

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Measures

Notation Description Value
m graph volume (no. of edges) 113,049
n graph size (no. of vertices) 46,727
dmax max degree 5,358
d+max max in-degree 5,358
d-max max out-degree 24
z mean total degree 4.839
h+
h-index, respecting in-degree
Known from citation networks, this measure is an indicator for the importance of a vertex in the graph, similar to a centrality measure. A value of h means that for the number of h vertices the degree of these vertices is greater or equal to h. A high value of h could be an indicator for a "dense" graph and that its vertices are more "prestigious". The value is computed by respecting the in-degree distribution of the graph, denoted as h+.
61
h h-index, respecting total degree 68
pmu fill, respecting unique edges only 0
p fill, respecting overall edges 0
mp
parallel edges
Based on the measure mu, this is the number of parallel edges, i.e., the total number of edges that share the same pair of source and target vertices. It is computed by subtracting mu from the total number of edges m, i.e. mp = m – mu.
15
mu
unique edges
In RDF, a pair of subject and object resources may be described with more than one predicate. Hence, in the graphs, there may exist a fraction of all edges that share the same pair of (subject and object) vertices. The value for mu represents the total number of edges without counting these multiple edges between a pair of vertices.
113,034
y reciprocity 0
δ
diameter (approximated)
The diameter is the longest shortest path between a pair of two vertices in the graph (as there can be more than one path for the pair of vertices). As this requires all possible paths to be computed, this is a very computational intensive measure. We used the pseudo_diameter-algorithm provided by graph-tool, which is an approximation method for the diameter of the graph. As the graph can have many components, this algorithm very often returns the value of 1. If this should be the case for this graph, we compute the diameter for the largest connecting component.
5
PR max pagerank value 0.002
Cd+ max in-degree centrality 0.115
Cd- max out-degree centrality 0.001
Cd max degree centrality 0.115
α powerlaw exponent, degree distribution 1.681
dminα dmin for α 41
α+ powerlaw exponent, in-degree distribution 1.667
dminα+ dmin for α+ 43
σ+ standard deviation, in-degree distribution 53.047
σ- standard deviation, out-degree distribution 4.865
cv+ coefficient variation, in-degree distribution 2,192.63
cv- coefficient variation, out-degree distribution 201.071
σ2+ variance, in-degree distribution 2,814.021
σ2- variance, out-degree distribution 23.664
C+d graph centralization 0.115
z- mean out-degree 10.081
$$deg^{--}(G)$$ max partial out-degree 6
$$\overline{deg^{--}}(G)$$ mean partial out-degree 1.2
$$deg^-_L(G)$$ max labelled out-degree 17
$$\overline{deg^-_L}(G)$$ mean labelled out-degree 8.398
$$deg^-_D(G)$$ max direct out-degree 24
$$\overline{deg^-_D}(G)$$ mean direct out-degree 10.08
z+ mean in-degree 2.747
$$deg^{++}(G)$$ max partial in-degree 5,358
$$\overline{deg^{++}}(G)$$ mean partial in-degree 2.742
$$deg^+_L(G)$$ max labelled in-degree 4
$$\overline{deg^+_L}(G)$$ mean labelled in-degree 1.002
$$deg^+_D(G)$$ max direct in-degree 5,358
$$\overline{deg^+_D}(G)$$ mean direct in-degree 2.747
$$deg_P(G)$$ max predicate degree 21,914
$$\overline{deg_P}(G)$$ mean predicate degree 2,055.436
$$deg^+_P(G)$$ max predicate in-degree 11,200
$$\overline{deg^+_P}(G)$$ mean predicate in-degree 1,712.382
$$deg^-_P(G)$$ max predicate out-degree 21,911
$$\overline{deg^-_P}(G)$$ mean predicate out-degree 749.491
$$\propto_{s-o}(G)$$ subject-object ratio 0.121
$$r_L(G)$$ ratio of repreated predicate lists 0.998
$$deg_{PL}(G)$$ max predicate list degree 5,356
$$\overline{deg_{PL}}(G)$$ mean predicate list degree 509.727
$$C_G$$ distinct classes 22
$$S^C_G$$ all different typed subjects 11,200
$$r_T(G)$$ ratio of typed subjects 0.999

Plots

Degree distribution shown here
In-degree distribution shown here
Last update of this page: 25 March 2020 13:38:39 CET