RDF Graph Measures for the Analysis of RDF Graphs

Home / Publications / rkb-explorer-risks

rkb-explorer-risks

Data and Resources

Measures

Notation Description Value
m graph volume (no. of edges) 302,977
n graph size (no. of vertices) 89,905
dmax max degree 26,500
d+max max in-degree 26,541
d-max max out-degree 2,847
z mean total degree 6.74
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+.
159
h h-index, respecting total degree 189
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.
88,278
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.
214,699
y reciprocity 0
δ
Effective measure!Score: 0.09

Datasets in this domain can be very well described by means of this particular measure.

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.
1
PR max pagerank value 0.004
Cd+ max in-degree centrality 0.295
Cd- max out-degree centrality 0.032
Cd max degree centrality 0.295
α powerlaw exponent, degree distribution 2.359
dminα dmin for α 21
α+ powerlaw exponent, in-degree distribution 2.428
dminα+ dmin for α+ 3
σ+ standard deviation, in-degree distribution 146.766
σ- standard deviation, out-degree distribution 13.76
cv+ coefficient variation, in-degree distribution 4,355.13
cv- coefficient variation, out-degree distribution 408.324
σ2+ variance, in-degree distribution 21,540.418
σ2- variance, out-degree distribution 189.348
C+d graph centralization 0.295
z-
Effective measure!Score: 0.052

Datasets in this domain can be very well described by means of this particular measure.

mean out-degree 9.701
$$deg^{--}(G)$$ max partial out-degree 950
$$\overline{deg^{--}}(G)$$ mean partial out-degree 1.588
$$deg^-_L(G)$$
Effective measure!Score: 0.055

Datasets in this domain can be very well described by means of this particular measure.

max labelled out-degree 9
$$\overline{deg^-_L}(G)$$ mean labelled out-degree 6.107
$$deg^-_D(G)$$
Effective measure!Score: 0.063

Datasets in this domain can be very well described by means of this particular measure.

max direct out-degree 71
$$\overline{deg^-_D}(G)$$ mean direct out-degree 6.874
z+ mean in-degree 4.356
$$deg^{++}(G)$$ max partial in-degree 26,541
$$\overline{deg^{++}}(G)$$ mean partial in-degree 4.35
$$deg^+_L(G)$$ max labelled in-degree 4
$$\overline{deg^+_L}(G)$$ mean labelled in-degree 1.001
$$deg^+_D(G)$$ max direct in-degree 20,116
$$\overline{deg^+_D}(G)$$ mean direct in-degree 3.087
$$deg_P(G)$$ max predicate degree 59,985
$$\overline{deg_P}(G)$$ mean predicate degree 14,427.476
$$deg^+_P(G)$$ max predicate in-degree 31,231
$$\overline{deg^+_P}(G)$$ mean predicate in-degree 9,082.238
$$deg^-_P(G)$$ max predicate out-degree 22,356
$$\overline{deg^-_P}(G)$$ mean predicate out-degree 3,316.476
$$\propto_{s-o}(G)$$ subject-object ratio 0.121
$$r_L(G)$$ ratio of repreated predicate lists 0.981
$$deg_{PL}(G)$$
Effective measure!Score: 0.062

Datasets in this domain can be very well described by means of this particular measure.

max predicate list degree 8,753
$$\overline{deg_{PL}}(G)$$
Effective measure!Score: 0.231

Datasets in this domain can be very well described by means of this particular measure.

mean predicate list degree 52.755
$$C_G$$
Effective measure!Score: 0.048

Datasets in this domain can be very well described by means of this particular measure.

distinct classes 5
$$S^C_G$$ all different typed subjects 31,231
$$r_T(G)$$ ratio of typed subjects 1

Plots

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