Latest Articles

## BlockQuicksort: Avoiding Branch Mispredictions in Quicksort

It is well known that Quicksort -- which is commonly considered as one of the fastest in-place sorting algorithms -- suffers in an essential way from branch mispredictions. We present a novel approach to addressing this problem by partially decoupling control from dataflow: in order to perform the partitioning, we split the input into... (more)

## SimBa: An Efficient Tool for Approximating Rips-filtration Persistence via <underline>Sim</underline>plicial <underline>Ba</underline>tch Collapse

In topological data analysis, a point cloud data P extracted from a metric space is often analyzed by computing the persistence diagram or barcodes of a sequence of Rips complexes built on P indexed by a scale parameter. Unfortunately, even for input of moderate size, the size of the Rips complex may become prohibitively large as the scale... (more)

## Fully Dynamic 2-Hop Cover Labeling

The 2-hop Cover labeling of a graph is currently the best data structure for answering shortest-path distance queries on large-scale networks, since it combines low query times, affordable space occupancy, and reasonable preprocessing effort. Its main limit resides in not being suited for dynamic networks since, after a network change, (1) queries... (more)

## Strong Steiner Tree Approximations in Practice

In this experimental study, we consider Steiner tree approximation algorithms that guarantee a constant approximation ratio smaller than 2. The considered greedy algorithms and approaches based on linear programming involve the incorporation of k-restricted full components for some k ≥ 3. For most of the algorithms, their strongest theoretical... (more)

### JEA joins RCR: Replicated Computational Results Initiative

http://jea.acm.org/rcr_initiative.cfm

### In Memoriam: David S. Johnson

http://dl.acm.org/citation.cfm?id=2907073

The Journal of Experimental Algorithmics (ISSN 1084-6654) is a high-quality journal devoted to the study of discrete algorithms and data structures from an empirical perspective. The journal welcomes original submissions that focus on design, implementation, and performance evaluation through a combination of experimentation and classical techniques.

We present KADABRA, a new algorithm to approximate betweenness centrality in directed and undirected graphs, which significantly outperforms all previous approaches on real-world complex networks. The efficiency of the new algorithm relies on two new theoretical contribution, of independent interest. The first contribution focuses on sampling shortest paths, a subroutine used by most algorithms that approximate betweenness centrality. We show that, on realistic random graph models, we can perform this task in time $|E|^{\frac{1}{2}+o(1)}$ with high probability, obtaining a significant speedup with respect to the $\Theta(|E|)$ worst-case performance. We experimentally show that this new technique achieves similar speedups on real-world complex networks, as well. The second contribution is a new rigorous application of the adaptive sampling technique. This approach decreases the total number of shortest paths that need to be sampled to compute all betweenness centralities with a given absolute error, and it also handles more general problems, such as computing the $k$ most central nodes. Furthermore, our analysis is general, and it might be extended to other settings.