Fig 1.
Closeness centrality and betweenness centrality in the skills network.
Skills network with nodes colored by (a) closeness centrality rank and (b) betweenness centrality rank. Yellow indicates high centrality rank, while green indicates low centrality rank. (c) The scatter plot between both centralities shows moderate correlation between them. Some highly mentioned skills with high (red dots) and low (green dots) centrality are indicated.
Fig 2.
Optimal skill clusters at different resolutions as obtained by Markov Stability (MS) on the sparsified skills graph .
MS identifies five robust graph partitions of increasing coarseness, from 189 clusters to 4 clusters, as indicated by minima of the Block NVI (points on the purple line) [36–39]. The partitions of the skills network into 21, 7 and 4 skill clusters (with nodes coloured according to their cluster) are shown at the top. The corresponding skill clusters, and their quasi-hierarchical structure, are summarised in the Sankey diagram in Fig 3. For further details on MS, see S1A Appendix.
Fig 3.
Sankey diagram capturing the multiscale structure of the skill clusters at different levels of resolution.
The optimal MS partitions into 21, 7 and 4 skill clusters (MS21, MS7, MS4) are presented together using a Sankey diagram, with summary labels obtained from the skills using Llama 2. Note that the quasi-hierarchical structure of skill co-occurrences is not imposed by the clustering method, but emerges naturally from the intrinsic co-occurrence patterns in the data, thus revealing the consistency of broader categories of skill requirements within adverts.
Fig 4.
(a) Co-occurrene skills network coloured according to the MS21 partition into 21 skill clusters. (b) Heatmap summarising four properties (rows) for each of the 21 skill clusters (columns). Each row is normalised by its maximum. (c) For each of the 21 clusters in MS21, we show: the cluster in he skills network; a word cloud with all the skills in the cluster, where font size reflects the eigenvector centrality of each skill; and the list of the top 5 most frequent skills in the cluster.
Fig 5.
Characterisation of the skill clusters.
Boxplots for the skill cluster distributions of: (a) closeness centrality, (b) containment, and (c) within-cluster semantic similarity. The scatter plots represent (for each cluster): (d) median closeness centrality vs. containment, (e) semantic similarity vs. containment, and (f) semantic similarity a vs. closeness centrality.
Fig 6.
Coverage of the co-occurrence matrix K for the MS21 clusters.
Coverage and containment have opposite meanings: the ‘Software Development Technologies’ cluster has high self-containment (i.e., low values of its coverage), and is especially unlikely to co-occur with ‘Sales and Customer Relationship’ or ‘Hospitality and Food Industry’. The high values of the coverage for most skill clusters underscore the absence of skill silos.
Table 1.
Summary of properties of the medium resolution data-driven skill clusters (MS21).
Fig 7.
Average salary of skill clusters and network properties.
Scatter plots of average annual salary (in £) for each skill cluster vs. (from left to right) average mentions, semantic similarity, skill containment and closeness centrality.
Fig 8.
Geographic distribution of adverts according to skill clusters.
The 21 maps show the percentage of all adverts in each NUTS2 region featuring a skill from each of the MS21 skill clusters. The geographic variation reflects regional economic, occupational and industrial characteristics. Map shapefile source: Office for National Statistics licensed under the Open Government Licence v.3.0. Contains OS data © Crown copyright and database right [2024].
Fig 9.
Projection of the skill profiles of NUTS2 regions.
(a) UMAP projection of the set of MS21 percentage vectors of the NUTS2 regions, where each region is described by a 21-dimensional vector containing the percentage of adverts in the MS21 skill clusters. NUTS2 regions with similar skill profiles lie close in this projection. The dots of the NUTS2 regions are coloured a posteriori according to average salary in the region, and the size of the dot reflects population density of the region. (b) The hierarchical clustering of the NUTS2 regions based on their MS21 percentage vectors reveals regional groupings that reflect shared geographic, socio-demographic, occupational and industrial similarities. (c) The hierarchical clustering of the MS21 skill clusters based on z-scores across regions reveals skills concentrations that differentiate regions.
Fig 10.
Temporal changes of skill demand.
(a) Temporal changes in average mentions and (b) the corresponding relative changes show a general increase in the average mentions across the 21 skill clusters from 2016 to 2022.
Fig 11.
Temporal changes of skill centrality and containment.
(a) Closeness centrality generally increase while (b) skill containment decreases from 2016 to 2022 for all skill clusters indicating job adverts more frequently require skills spanning multiple skill clusters.
Fig 12.
Sankey diagram between co-occurrence skill clusters (MS21) and expert-based skill categories (Lightcast).
There is some agreement between the data-driven clusters and the LC categories in skill areas where thematic content and co-occurrence match. For each cluster in MS21, we plot a pie chart to visualise the proportions of Lightcast categories, and the corresponding ‘thematic entropy’ for the skill cluster, which indicates how thematically mixed the cluster is.
Fig 13.
From job postings to the clustering of a network of co-occurrent skills.
(a) the data preparation including the extraction of skill co-occurrence from metadata, skill matching to Lightcast taxonomy and dimensionality reduction using MCA; (b) the graph-based clustering including the sparsification of the complete cosine similarity graph and the multiscale clustering with Markov Stability; and (c) the descriptive analysis on the optimal clustering with 21 partitions using LLM, nodal containment and closeness centrality.