activity
20092026
most citedThe Future is Big Graphs! A Community View on Graph Processing Systems

157 citations · 421 across the 63 of their papers we have counts for

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Showing 2020Show all

14 papers · 1 filter

cs.DS2020

Recent Advances in Practical Data Reduction

Faisal Abu-Khzam, Sebastian Lamm, Matthias Mnich +3

Over the last two decades, significant advances have been made in the design and analysis of fixed-parameter algorithms for a wide variety of graph-theoretic problems. This has res…

cs.DC2020★ 157 cited

The Future is Big Graphs! A Community View on Graph Processing Systems

Sherif Sakr, Angela Bonifati, Hannes Voigt +38

Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users a…

cs.DS2020

Boosting Data Reduction for the Maximum Weight Independent Set Problem Using Increasing Transformations

Alexander Gellner, Sebastian Lamm, Christian Schulz +2

Given a vertex-weighted graph, the maximum weight independent set problem asks for a pair-wise non-adjacent set of vertices such that the sum of their weights is maximum. The branc…

cs.DS2020

O'Reach: Even Faster Reachability in Large Graphs

Kathrin Hanauer, Christian Schulz, Jonathan Trummer

One of the most fundamental problems in computer science is the reachability problem: Given a directed graph and two vertices s and t, can s reach t via a path? We revisit existing…

cs.DC2020★ 1 cited

Efficient Process-to-Node Mapping Algorithms for Stencil Computations

Sascha Hunold, Konrad von Kirchbach, Markus Lehr +2

Good process-to-compute-node mappings can be decisive for well performing HPC applications. A special, important class of process-to-node mapping problems is the problem of mapping…

cs.DS2020

Faster Parallel Multiterminal Cuts

Monika Henzinger, Alexander Noe, Christian Schulz

We give an improved branch-and-bound solver for the multiterminal cut problem, based on the recent work of Henzinger et al.. We contribute new, highly effective data reduction rule…