Direction-optimizing breadth-first search

TL;DR: In this paper, the authors investigate the applicability of push-pull dichotomy to various algorithms and its impact on complexity, performance, and the amount of used locks, atomics, and reads/writes.

TL;DR: Several hybrid GPU implementations are presented, providing good performance on graphs of arbitrary structure rather than just scale-free graphs as was done previously, and near linear speedup and performance exceeding tens of GTEPS when running between ness centrality on 192 GPUs.

TL;DR: This work identifies lightweight re ordering techniques that improve performance even after accounting for the overhead of reordering, and addresses a major impediment to the general adoption of these reordering techniques - input-dependent speedups – by linking the speedup from lightweight reordering to structural properties of the input graph.

TL;DR: This paper presents the first practical solution for efficient LCR evaluation, leveraging landmark-based indexes for large graphs, and shows through extensive experiments that their indexes are significantly smaller than state-of-the-art LCR indexing techniques, while supporting up to orders of magnitude faster query evaluation times.

TL;DR: In this paper, the authors have crawled the entire Twittersphere and found a non-power-law follower distribution, a short effective diameter, and low reciprocity, which all mark a deviation from known characteristics of human social networks.

TL;DR: The University of Florida Sparse Matrix Collection, a large and actively growing set of sparse matrices that arise in real applications, is described and a new multilevel coarsening scheme is proposed to facilitate this task.

TL;DR: This paper examines data gathered from four popular online social networks: Flickr, YouTube, LiveJournal, and Orkut, and reports that the indegree of user nodes tends to match the outdegree; the networks contain a densely connected core of high-degree nodes; and that this core links small groups of strongly clustered, low-degree node at the fringes of the network.

TL;DR: A simple, parsimonious model, the “recursive matrix” (R-MAT) model, which can quickly generate realistic graphs, capturing the essence of each graph in only a few parameters is proposed.