RDF Graph Measures for the Analysis of RDF Graphs

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Measures

Notation Description Value
m graph volume (no. of edges) 3,306,719
n graph size (no. of vertices) 1,561,235
dmax max degree 766,000
d+max max in-degree 766,417
d-max max out-degree 2,405
z mean total degree 4.24
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+.
12
h h-index, respecting total degree 639
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.
9,151
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.
3,297,568
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.
2
PR max pagerank value 0.032
Cd+ max in-degree centrality 0.491
Cd- max out-degree centrality 0.002
Cd max degree centrality 0.491
α powerlaw exponent, degree distribution 24.918
dminα dmin for α 4
α+ powerlaw exponent, in-degree distribution 45.424
dminα+ dmin for α+ 2
σ+ standard deviation, in-degree distribution 617.268
σ- standard deviation, out-degree distribution 22.886
cv+ coefficient variation, in-degree distribution 29,143.7
cv- coefficient variation, out-degree distribution 1,080.54
σ2+ variance, in-degree distribution 381,019.623
σ2- variance, out-degree distribution 523.765
C+d graph centralization 0.491
z-
Effective measure!Score: 0.052

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

mean out-degree 3.919
deg(G) max partial out-degree 2,401
deg(G) mean partial out-degree 1.853
degL(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 7
degL(G) mean labelled out-degree 2.115
degD(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 2,404
degD(G) mean direct out-degree 3.908
z+ mean in-degree 2.228
deg++(G) max partial in-degree 766,417
deg++(G) mean partial in-degree 2.228
degL+(G) max labelled in-degree 26
degL+(G) mean labelled in-degree 1
degD+(G) max direct in-degree 766,417
degD+(G) mean direct in-degree 2.222
degP(G) max predicate degree 1,532,834
degP(G) mean predicate degree 80,651.683
degP+(G) max predicate in-degree 766,417
degP+(G) mean predicate in-degree 43,532.707
degP(G) max predicate out-degree 766,417
degP(G) mean predicate out-degree 36,193.854
so(G) subject-object ratio 0.491
rL(G) ratio of repreated predicate lists 0.999
degPL(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 766,417
degPL(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 738.818
CG
Effective measure!Score: 0.048

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

distinct classes 1
SGC all different typed subjects 77,278
rT(G) ratio of typed subjects 0.092

Plots

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