RDF Graph Measures for the Analysis of RDF Graphs

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basque-eurowordnet-lemon-lexicon-3-0

Data and Resources

Measures

Notation Description Value
m graph volume (no. of edges) 1,215,583
n graph size (no. of vertices) 314,069
dmax max degree 50,000
d+max max in-degree 50,041
d-max
Effective measure!Score: 0.04

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

max out-degree 27,235
z mean total degree 7.74
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+.
182
h h-index, respecting total degree 185
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.
27,556
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.
1,188,027
y reciprocity 0.143
δ
Effective measure!Score: 0.237

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.
20
PR max pagerank value 0.004
Cd+ max in-degree centrality 0.159
Cd- max out-degree centrality 0.087
Cd max degree centrality 0.159
α powerlaw exponent, degree distribution 4.777
dminα dmin for α 13
α+ powerlaw exponent, in-degree distribution 1.596
dminα+ dmin for α+ 44
σ+ standard deviation, in-degree distribution 195.078
σ- standard deviation, out-degree distribution 48.797
cv+ coefficient variation, in-degree distribution 5,040.2
cv- coefficient variation, out-degree distribution 1,260.77
σ2+ variance, in-degree distribution 38,055.25
σ2- variance, out-degree distribution 2,381.19
C+d graph centralization 0.159
z-
Effective measure!Score: 0.174

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

mean out-degree 7.367
deg(G)
Effective measure!Score: 0.168

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

max partial out-degree 27,222
deg(G) mean partial out-degree 1.334
degL(G)
Effective measure!Score: 0.098

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

max labelled out-degree 13
degL(G) mean labelled out-degree 5.523
degD(G)
Effective measure!Score: 0.037

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

max direct out-degree 27,234
degD(G) mean direct out-degree 7.2
z+ mean in-degree 4.283
deg++(G) max partial in-degree 50,041
deg++(G) mean partial in-degree 2.918
degL+(G) max labelled in-degree 9
degL+(G) mean labelled in-degree 1.468
degD+(G) max direct in-degree 50,041
degD+(G)
Effective measure!Score: 0.045

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

mean direct in-degree 4.186
degP(G) max predicate degree 275,525
degP(G) mean predicate degree 22,510.796
degP+(G) max predicate in-degree 165,012
degP+(G) mean predicate in-degree 16,878.333
degP(G) max predicate out-degree 77,264
degP(G) mean predicate out-degree 7,714.463
so(G) subject-object ratio 0.429
rL(G) ratio of repreated predicate lists 0.976
degPL(G) max predicate list degree 30,263
degPL(G) mean predicate list degree 42.354
CG distinct classes 7
SGC all different typed subjects 165,012
rT(G) ratio of typed subjects 1

Plots

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