Summary

Total colouring addresses the problem of assigning colours simultaneously to the vertices and edges of a graph so that no two adjacent or incident elements share the same colour. Introduced in the 1960s, this concept extends classic vertex and edge colouring, encapsulating both in a unified framework. The central parameter is the total chromatic number, χ″(G), defined as the smallest number of colours required for a proper total colouring of a graph G. A long-standing open question, known as the Total Coloring Conjecture, posits that χ″(G) does not exceed Δ(G)+2, where Δ(G) denotes the maximum degree of G. Although exact values of χ″(G) are known for small or highly structured families—complete graphs, cycles, bipartite graphs and certain sparse graphs—the problem remains NP-hard in general. Advances in the past decade have focused on specialised graph classes, improvements to upper and lower bounds, algorithmic heuristics for approximate colourings, and variants such as neighbour-distinguishing total colourings, which impose additional constraints by sums or colour sets. Beyond theoretical interest, total colourings find application in scheduling, frequency assignment in wireless networks and resource allocation, where both nodes and connections require interference-free labels.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Total Colorings in Graph Theory publication trend

The graph below shows the total number of articles in total colorings in graph theory across all publications each year (not limited to Nature Index journals).

Technical terms

Total colouring: Assignment of colours to all vertices and edges of a graph so that no incident or adjacent elements share a colour.

Total chromatic number (χ″(G)): The minimum number of colours needed for a proper total colouring of G.

Proper total colouring: A colouring in which adjacent vertices, adjacent edges and each edge–vertex pair receive distinct colours.

Maximum degree (Δ(G)): The largest number of edges incident to any vertex in G.

Neighbour sum distinguishing total colouring: A proper total colouring in which the sum of colours at each vertex and its incident edges differs for every pair of adjacent vertices.

Maximum average degree: The highest average degree over all non-empty subgraphs of G, used to gauge graph sparsity.

References

  1. On the Total-Neighbor-Distinguishing Index by Sums. Graphs and Combinatorics (2013).
  2. Neighbor Distinguishing Colorings of Graphs with the Restriction for Maximum Average Degree. Axioms (2023).
  3. Total Colouring of New Classes of Subcubic graphs. Theory and Applications of Graphs (2022).

About these summaries

This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.

Nature Strategy Reports
Turn complex research questions into confident strategic decisions 

When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.

  • Benchmark your performance against global peers using robust, methodologically sound analysis.

  • Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.

  • Gain tailored, decision-ready recommendations aligned to your strategic priorities.

Talk to us to learn more about our data dashboards and bespoke strategy reports.

Nature Masterclasses
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.

Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:

  • Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.

  • Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.

  • Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.

Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.