RECOMB 2016: 152-163 (also JCB 2017) For further details and more recent papers on this topic, check the page of our ERC Starting Grant "Safe and Complete Algorithms for Bioinformatics". String ...

Our research is focused on graph algorithms, from both a theoretical perspective, and a practical perspective motivated by real-world problems in Bioinformatics, such as genome sequencing technologies ...

Graph algorithms and sparsification techniques are essential tools in computer science, particularly in the fields of network analysis, optimization, and data processing. These techniques help ...

Dynamic graph algorithms and data structures are essential for efficiently managing and processing graphs that change over time. These algorithms allow for updates such as adding or removing ...

It’s often assumed that Dijkstra’s algorithm, or the A* graph traversal algorithm is used, but the reality is that although these pure graph theory algorithms are decidedly influential ...

Understanding potential pitfalls—and how to overcome them—can help organizations maximize the value of this technology.

Covers the fundamentals of algorithms and various algorithmic strategies, including time and space complexity, sorting algorithms, recurrence relations, divide and conquer algorithms, greedy ...

Mathematicians have long sought to develop algorithms that can compare any two graphs. In practice, many algorithms always seem to work efficiently. But in theory, there is no guarantee.

Graph mining algorithms typically focus on finding dense connections between individual pairs of points, such as two people ...