Learn about the most effective data structures and algorithms for working with graphs, and how they can help you solve real-world problems in various domains and applications.

Sparse graphs are often easier to handle than dense ones. Most graph algorithms run faster, sometimes by orders of magnitude, when there are fewer edges, and the graph itself can be stored more ...

Graph partitioning is a fundamental problem in several scientific and engineering applications. In this paper, we describe heuristics that improve the state-of-the-art practical algorithms used in ...

Many important applications are organized around long-lived, irregular sparse graphs (e.g., data and knowledge bases, CAD optimization, numerical problems, simulations). The graph structures are large ...

3. On the PubMed dataset, LGNN3 performs better than other versions, especially when dealing with large-scale and sparse graph data. This may be because the model design and feature learning ability ...

Sparse graph and graph search algorithms implementation for Unity with C#. It includes graph and directed graph impelementations with adjacency list representation. Since I am using it in Unity, there ...

The official SuiteSparse library: a suite of sparse matrix algorithms authored or co-authored by Tim Davis, Texas A&M University. Implementation of various Data Structures and algorithms - Linked List ...

Spectral algorithms are widely applied to data clustering problems, including finding communities or partitions in graphs and networks. We propose a way of encoding sparse data using a ...

The graph below shows the total number of publications each year in Sparse Fourier Transform Algorithms. References [1] On Performance of Sparse Fast Fourier Transform Algorithms Using the Flat ...