RDF Graph Measures for the Analysis of RDF Graphs

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universal-dependencies-treebank-japanese-ktc

Data and Resources

Measures

Notation Description Value
m graph volume (no. of edges) 641,336
n graph size (no. of vertices) 83,998
dmax max degree 77,700
d+max max in-degree 77,688
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 9
z mean total degree 15.3
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+.
210
h h-index, respecting total degree 210
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.
89,838
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.
551,498
y reciprocity 0.075
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.
2,836
PR max pagerank value 0.018
Cd+ max in-degree centrality 0.925
Cd- max out-degree centrality 0
Cd max degree centrality 0.925
α powerlaw exponent, degree distribution 6.495
dminα dmin for α 11
α+ powerlaw exponent, in-degree distribution 3.385
dminα+ dmin for α+ 4
σ+ standard deviation, in-degree distribution 365.005
σ- standard deviation, out-degree distribution 1.991
cv+ coefficient variation, in-degree distribution 4,780.6
cv- coefficient variation, out-degree distribution 26.08
σ2+ variance, in-degree distribution 133,228.876
σ2- variance, out-degree distribution 3.965
C+d graph centralization 0.925
z-
Effective measure!Score: 0.174

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

mean out-degree 7.964
$$deg^{--}(G)$$
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
$$\overline{deg^{--}}(G)$$ mean partial out-degree 1
$$deg^-_L(G)$$
Effective measure!Score: 0.098

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

max labelled out-degree 9
$$\overline{deg^-_L}(G)$$ mean labelled out-degree 7.964
$$deg^-_D(G)$$
Effective measure!Score: 0.037

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

max direct out-degree 8
$$\overline{deg^-_D}(G)$$ mean direct out-degree 6.849
z+ mean in-degree 7.778
$$deg^{++}(G)$$ max partial in-degree 77,688
$$\overline{deg^{++}}(G)$$ mean partial in-degree 5.677
$$deg^+_L(G)$$ max labelled in-degree 2
$$\overline{deg^+_L}(G)$$ mean labelled in-degree 1.37
$$deg^+_D(G)$$ max direct in-degree 77,688
$$\overline{deg^+_D}(G)$$
Effective measure!Score: 0.045

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

mean direct in-degree 6.688
$$deg_P(G)$$ max predicate degree 80,525
$$\overline{deg_P}(G)$$ mean predicate degree 64,133.6
$$deg^+_P(G)$$ max predicate in-degree 80,525
$$\overline{deg^+_P}(G)$$ mean predicate in-degree 64,133.6
$$deg^-_P(G)$$ max predicate out-degree 74,851
$$\overline{deg^-_P}(G)$$ mean predicate out-degree 11,296.9
$$\propto_{s-o}(G)$$ subject-object ratio 0.94
$$r_L(G)$$ ratio of repreated predicate lists 1
$$deg_{PL}(G)$$ max predicate list degree 57,857
$$\overline{deg_{PL}}(G)$$ mean predicate list degree 13,420.833
$$C_G$$ distinct classes 2
$$S^C_G$$ all different typed subjects 80,525
$$r_T(G)$$ 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