Latest Articles for Graph Theory

New quantum algorithm promises faster solutions for complex graph problems.

This article examines the role of stress-linked pairs in graph rigidity.

A look into toroidal maps and hypermaps in mathematics.

A new GPU-based method for finding maximal bicliques in large graphs.

A new method to represent flows using cell complexes.

Study the dynamics of flows in signed graphs and their significance.

A look at how isogeny graphs reveal relationships in elliptic curves.

Research extends the applications of matroids and polynomials in complex graph structures.

Exploring subgroup membership in virtually free groups using graph theory and algorithms.

Learn how controllability backbone aids in managing networked agents effectively.

This article discusses new findings in the Merlin-Arthur communication model and its applications.

Learn how colimits and zig-zags connect different categories in a simple way.

Exploring the boundaries of choiceless computation with hypercubes and preorders.

An overview of polynomials' behavior and their relationship with Julia sets.

This method enhances our understanding of network communities through Edge Random Walk Centrality.

Exploring strategies in the Cops and Robbers game with 1-planar graphs.

This study examines ear anonymity and its implications for directed graphs.

Examining the link between finite state automorphisms and regular rooted trees in group theory.

Explore how hidden orders in mathematics reveal deeper connections.

A look at algorithms for maintaining minimum cuts in changing graphs.

Research focuses on optimizing graph connections through terminals and Steiner nodes.

A look into the significance of posets and their polytopes in mathematics.

Research on multidimensional circulant graphs boosts quantum error correction methods.

GSANs improve data processing in complex structures like graphs and simplicial complexes.

A new framework improves accuracy in node classification with limited labeled data.

A new model that improves graph learning by focusing on node pairs.

A simple breakdown of weighted chordal graphs and their significance.

A new model tackles challenges in graph neural networks using power series.

This work presents methods for generating trees to model graphs effectively.

A framework for analyzing complex data using graph structures.

Unraveling the concept of resistance distance in connected networks.

This article studies maximal cliques across various random network models.

A look at vector spaces, special bases, and their role in group theory.

Exploring the blend of quantum and classical methods for solving the Max-Cut problem.

Discover new methods for analyzing signals on graphs effectively.

A new model enhances community search by integrating multi-dimensional edge attributes.

New techniques improve diameter calculations for unit-disk graphs, aiding efficient network analysis.

DeepRicci enhances graph neural networks by improving structure and node features.

A new method to color edges in specific sparse graphs with minimal colors efficiently.

Recent improvements in k-Clique algorithms enhance efficiency in identifying connected groups.