ACM Journal of

Experimental Algorithmics (JEA)

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

In Memoriam: David S. Johnson

About JEA

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.

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Forthcoming Articles
Solving Graph Problems via Potential Maximal Cliques: An Experimental Evaluation of the Bouchitté--Todinca Algorithm

The BT algorithm of Bouchitt\'e and Todinca based on enumerating potential maximal cliques, originally proposed for the treewidth and minimum fill-in problems, yields improved exact exponential-time algorithms for various graph optimization problems related to optimal triangulations. While the BT algorithm has received significant attention in terms of theoretical analysis, less attention has been paid on engineering efficient implementations of the algorithm for different problems and thereby on empirical studies on its effectiveness in practice. In this work, we provide an experimental evaluation of an implementation of the BT algorithm, based on our second place winning entry in the 2nd Parameterized Algorithms and Computational Experiments Challenge (PACE 2017), extended to several related graph problems: treewidth, minimum fill-in, generalized and fractional hypertreewidth, and the total table size problem. Based on the results, we conclude that an efficient implementation of the BT algorithm yields an empirically competitive approach to each of the considered problems when compared to available implementations of alternative problem-specific algorithmic approaches.

KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

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.

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