RDF Graph Measures for the Analysis of RDF Graphs

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apertium-rdf-oc-ca

Data and Resources

Measures

Notation Description Value
m graph volume (no. of edges) 346,353
n graph size (no. of vertices) 127,694
dmax max degree 32,000
d+max max in-degree 31,966
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 15,989
z mean total degree 5.42
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+.
11
h h-index, respecting total degree 15
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.
31,967
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.
314,386
y reciprocity 0
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.
1
PR max pagerank value 0.021
Cd+ max in-degree centrality 0.25
Cd- max out-degree centrality 0.125
Cd max degree centrality 0.25
α powerlaw exponent, degree distribution 17.016
dminα dmin for α 7
α+ powerlaw exponent, in-degree distribution 6.122
dminα+ dmin for α+ 4
σ+ standard deviation, in-degree distribution 168.009
σ- standard deviation, out-degree distribution 69.417
cv+ coefficient variation, in-degree distribution 6,194.17
cv- coefficient variation, out-degree distribution 2,559.26
σ2+ variance, in-degree distribution 28,226.939
σ2- variance, out-degree distribution 4,818.664
C+d graph centralization 0.25
z-
Effective measure!Score: 0.174

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

mean out-degree 3.421
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 15,983
deg(G) mean partial out-degree 1.204
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 6
degL(G) mean labelled out-degree 2.842
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 15,989
degD(G) mean direct out-degree 3.105
z+ mean in-degree 2.712
deg++(G) max partial in-degree 31,966
deg++(G) mean partial in-degree 1.859
degL+(G) max labelled in-degree 2
degL+(G) mean labelled in-degree 1.459
degD+(G) max direct in-degree 31,966
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 2.462
degP(G) max predicate degree 101,239
degP(G) mean predicate degree 18,229.105
degP+(G) max predicate in-degree 101,239
degP+(G) mean predicate in-degree 15,144.421
degP(G) max predicate out-degree 31,966
degP(G) mean predicate out-degree 9,805.105
so(G) subject-object ratio 0.793
rL(G) ratio of repreated predicate lists 1
degPL(G) max predicate list degree 31,966
degPL(G) mean predicate list degree 10,124
CG distinct classes 7
SGC all different typed subjects 101,239
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