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 ...

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 ...

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.

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 ...

Graph theory is an integral component of algorithm design that underlies sparse matrices, relational databases, and networks. Improving the performance of graph algorithms has direct implications to ...

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 ...