Latest Articles for Graph Theory

Exploring the intersection of quantum computing and optimization through Quantum Max Cut.

New method reduces memory usage in managing temporal graphs.

Understanding how consistency-checking impacts machine learning and sample complexity.

A look into the properties and significance of clique graphs in hexagonal lattices.

A look into vertex and edge colouring in graph theory.

Examining fixation time and its impact on species evolution.

Graph Neural SDEs enhance model accuracy by incorporating randomness into data analysis.

Discover the Heawood graph's role in map coloring and its mathematical implications.

Researchers improve methods for finding Ramsey number counterexamples using new algorithms.

A new method improves the efficiency of quantum search in complex graphs.

A new method for finding the shortest paths in graphs with matrix inversion.

Exploring coloring techniques in hypergraphs and their mathematical implications.

Exploring Size-Ramsey Numbers and their impact on graph theory and sparse structures.

A look into graph relationships through matchings and Turan numbers.

This article examines min-k-planar drawings and their significance in graph representation.

i-Align enhances knowledge graph alignment through clear explanations and efficient processing.

A look into mapping class groups and their impact on surfaces.

This work presents a method to improve node embeddings using random walks.

Exploring the significance of convex minorant trees in analyzing random graphs.

A new approach to retain knowledge in graph data amidst continuous updates.

An overview of vector partition functions and their applications in mathematics.

Learn how to address issues of data imbalance in graph analysis.

Exploring various complexities and types of domino snake problems in combinatorial group theory.

An accessible look at Dirac operators and their significance on graphs.

Innovative approach speeds up training of temporal interaction graphs on GPUs.

A study on how spectral independence affects system stability and mixing time.

New methods improve the efficiency of estimating latent vectors in random graphs.

New techniques improve performance of Graph Neural Networks in handling large data structures.

Research enhances coding techniques for better data transmission and error correction.

Learn methods for quick query evaluation using Common Graph structures.

Learn how graph embeddings simplify data relationships for machine learning applications.

Analyzing the connection between C-I graphs, chordal graphs, and cographs.

Exploring the relationship between eigenvalues and independence complexes in graph theory.

A new method for counting graphlets in changing networks.

Examining the relationship between ultragraphs and algebraic structures in mathematics.

This study reveals new insights into hypergraph colorability and weak chromatic numbers.

A look at segment and line cover numbers in graph representation.

A framework to identify sparse matrix structures using limited data.

Addressing over-squashing can improve the performance of Graph Neural Networks.

A new method for efficiently finding maximum 2-packing sets in graphs.