RDF Graph Measures for the Analysis of RDF Graphs

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universal-dependencies-treebank-russian

Data and Resources

Measures

Notation Description Value
m graph volume (no. of edges) 91,389
n graph size (no. of vertices) 18,887
dmax max degree 9,570
d+max max in-degree 9,573
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 10
z mean total degree 9.68
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+.
97
h h-index, respecting total degree 97
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.
5,969
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.
85,420
y reciprocity 0.03
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.
498
PR max pagerank value 0.008
Cd+ max in-degree centrality 0.507
Cd- max out-degree centrality 0.001
Cd max degree centrality 0.507
α powerlaw exponent, degree distribution 6.164
dminα dmin for α 11
α+ powerlaw exponent, in-degree distribution 2.81
dminα+ dmin for α+ 3
σ+ standard deviation, in-degree distribution 84.703
σ- standard deviation, out-degree distribution 4.73
cv+ coefficient variation, in-degree distribution 1,750.52
cv- coefficient variation, out-degree distribution 97.761
σ2+ variance, in-degree distribution 7,174.556
σ2- variance, out-degree distribution 22.376
C+d graph centralization 0.506
z-
Effective measure!Score: 0.174

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

mean out-degree 9.074
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 1
deg(G) mean partial out-degree 1
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 10
degL(G) mean labelled out-degree 9.074
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 10
degD(G) mean direct out-degree 8.481
z+ mean in-degree 4.928
deg++(G) max partial in-degree 9,573
deg++(G) mean partial in-degree 3.984
degL+(G) max labelled in-degree 3
degL+(G) mean labelled in-degree 1.237
degD+(G) max direct in-degree 9,573
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 4.606
degP(G) max predicate degree 10,072
degP(G) mean predicate degree 8,308.091
degP+(G) max predicate in-degree 10,072
degP+(G) mean predicate in-degree 8,308.091
degP(G) max predicate out-degree 9,074
degP(G) mean predicate out-degree 2,085.364
so(G) subject-object ratio 0.515
rL(G) ratio of repreated predicate lists 0.999
degPL(G) max predicate list degree 5,978
degPL(G) mean predicate list degree 1,007.2
CG distinct classes 2
SGC all different typed subjects 10,072
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