In this work, we use algebraic methods for studying distance computation and subgraph detection tasks in the congested clique model. Specifically, we adapt parallel matrix multiplication implementations to the congested clique, obtaining an O(n 1−2/ω ) round matrix multiplication algorithm, where ω < 2.3728639 is the exponent of matrix multiplication. In conjunction with known techniques from centralised algorithmics, this gives significant improvements over previous best upper bounds in the congested clique model. The highlight results include: -triangle and 4-cycle counting in O(n 0.158 ) rounds, improving upon the O(n 1/3 ) algorithm of Dolev et al. [DISC 2012], -a (1 + o(1))-approximation of all-pairs shortest paths in O(n 0.158 ) rounds, improving upon theÕ(n 1/2 )-round (2 + o(1))-approximation algorithm of Nanongkai [STOC 2014], and computing the girth in O(n 0.158 ) rounds, which is the first non-trivial solution in this model.In addition, we present a novel constant-round combinatorial algorithm for detecting 4-cycles.

Summary: MOODS (MOtif Occurrence Detection Suite) is a software package for matching position weight matrices against DNA sequences. MOODS implements state-of-the-art online matching algorithms, achieving considerably faster scanning speed than with a simple brute-force search. MOODS is written in C++, with bindings for the popular BioPerl and Biopython toolkits. It can easily be adapted for different purposes and integrated into existing workflows. It can also be used as a C++ library.Availability: The package with documentation and examples of usage is available at http://www.cs.helsinki.fi/group/pssmfind. The source code is also available under the terms of a GNU General Public License (GPL).Contact: janne.h.korhonen@helsinki.fi

We design fast deterministic algorithms for distance computation in the Congested Clique model. Our key contributions include:A $$(2+\epsilon )$$(2+ϵ)-approximation for all-pairs shortest paths in $$O(\log ^2{n} / \epsilon )$$O(log2n/ϵ) rounds on unweighted undirected graphs. With a small additional additive factor, this also applies for weighted graphs. This is the first sub-polynomial constant-factor approximation for APSP in this model.A $$(1+\epsilon )$$(1+ϵ)-approximation for multi-source shortest paths from $$O(\sqrt{n})$$O(n) sources in $$O(\log ^2{n} / \epsilon )$$O(log2n/ϵ) rounds on weighted undirected graphs. This is the first sub-polynomial algorithm obtaining this approximation for a set of sources of polynomial size. Our main techniques are new distance tools that are obtained via improved algorithms for sparse matrix multiplication, which we leverage to construct efficient hopsets and shortest paths. Furthermore, our techniques extend to additional distance problems for which we improve upon the state-of-the-art, including diameter approximation, and an exact single-source shortest paths algorithm for weighted undirected graphs in $$\tilde{O}(n^{1/6})$$O~(n1/6) rounds.