RDF Graph Measures for the Analysis of RDF Graphs

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Data and Resources

Measures

Notation Description Value
m graph volume (no. of edges) 3,548,975
n graph size (no. of vertices) 1,080,261
dmax max degree 303,207
d+max max in-degree 303,207
d-max max out-degree 5,820
z mean total degree 6.571
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+.
336
h h-index, respecting total degree 607
pmu fill, respecting unique edges only 0
p fill, respecting overall edges 0
mp
Effective measure!Score: 0.045

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

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.
1,154,743
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.
2,394,232
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.
1
PR max pagerank value 0.01
Cd+ max in-degree centrality 0.281
Cd- max out-degree centrality 0.005
Cd max degree centrality 0.281
α powerlaw exponent, degree distribution 2.255
dminα dmin for α 25
α+ powerlaw exponent, in-degree distribution 2.006
dminα+ dmin for α+ 33
σ+ standard deviation, in-degree distribution 426.225
σ- standard deviation, out-degree distribution 27.543
cv+ coefficient variation, in-degree distribution 12,973.7
cv-
Effective measure!Score: 0.047

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

coefficient variation, out-degree distribution 838.378
σ2+ variance, in-degree distribution 181,667.976
σ2- variance, out-degree distribution 758.627
C+d graph centralization 0.281
z- mean out-degree 8.102
deg(G) max partial out-degree 2,831
deg(G) mean partial out-degree 1.866
degL(G)
Effective measure!Score: 0.099

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

max labelled out-degree 11
degL(G) mean labelled out-degree 4.341
degD(G) max direct out-degree 729
degD(G) mean direct out-degree 5.466
z+ mean in-degree 3.5
deg++(G) max partial in-degree 303,207
deg++(G) mean partial in-degree 3.176
degL+(G) max labelled in-degree 4
degL+(G) mean labelled in-degree 1.102
degD+(G) max direct in-degree 232,365
degD+(G)
Effective measure!Score: 0.128

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

mean direct in-degree 2.362
degP(G) max predicate degree 888,370
degP(G) mean predicate degree 101,399.286
degP+(G) max predicate in-degree 438,016
degP+(G) mean predicate in-degree 54,328.886
degP(G) max predicate out-degree 176,710
degP(G) mean predicate out-degree 31,930.486
so(G) subject-object ratio 0.344
rL(G) ratio of repreated predicate lists 0.989
degPL(G)
Effective measure!Score: 0.047

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

max predicate list degree 117,981
degPL(G)
Effective measure!Score: 0.129

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

mean predicate list degree 91.426
CG
Effective measure!Score: 0.06

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

distinct classes 9
SGC all different typed subjects 438,016
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