RDF Graph Measures for the Analysis of RDF Graphs

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bio2rdf-wormbase

Data and Resources

Measures

Notation Description Value
m graph volume (no. of edges) 32,994,662
n graph size (no. of vertices) 10,537,483
dmax max degree 3,156,345
d+max max in-degree 3,156,345
d-max max out-degree 802,491
z mean total degree 6.262
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+.
656
h h-index, respecting total degree 885
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.
11,018,553
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.
21,976,109
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.
10
PR max pagerank value 0.002
Cd+ max in-degree centrality 0.3
Cd- max out-degree centrality 0.076
Cd max degree centrality 0.3
α powerlaw exponent, degree distribution 1.74
dminα dmin for α 401
α+ powerlaw exponent, in-degree distribution 2.436
dminα+ dmin for α+ 62
σ+ standard deviation, in-degree distribution 1,427.442
σ- standard deviation, out-degree distribution 559.07
cv+ coefficient variation, in-degree distribution 45,588.1
cv-
Effective measure!Score: 0.256

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

coefficient variation, out-degree distribution 17,855
σ2+ variance, in-degree distribution 2,037,590.457
σ2- variance, out-degree distribution 312,559.335
C+d graph centralization 0.3
z-
Effective measure!Score: 0.096

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

mean out-degree 17.929
deg(G)
Effective measure!Score: 0.117

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

max partial out-degree 802,484
deg(G) mean partial out-degree 1.627
degL(G)
Effective measure!Score: 0.159

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

max labelled out-degree 19
degL(G) mean labelled out-degree 11.017
degD(G)
Effective measure!Score: 0.042

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

max direct out-degree 385
degD(G) mean direct out-degree 11.942
z+ mean in-degree 3.367
deg++(G) max partial in-degree 3,156,345
deg++(G) mean partial in-degree 3.346
degL+(G)
Effective measure!Score: 0.08

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

max labelled in-degree 26
degL+(G) mean labelled in-degree 1.006
degD+(G) max direct in-degree 1,840,187
degD+(G)
Effective measure!Score: 0.055

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

mean direct in-degree 2.242
degP(G) max predicate degree 6,576,783
degP(G) mean predicate degree 270,448.049
degP+(G) max predicate in-degree 1,840,199
degP+(G) mean predicate in-degree 166,183.893
degP(G) max predicate out-degree 1,840,187
degP(G) mean predicate out-degree 80,828.23
so(G) subject-object ratio 0.105
rL(G) ratio of repreated predicate lists 0.999
degPL(G) max predicate list degree 802,482
degPL(G) mean predicate list degree 1,157.414
CG distinct classes 43
SGC all different typed subjects 1,840,199
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