Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Sanders, Geoffrey"'
Timestamped relational datasets consisting of records between pairs of entities are ubiquitous in data and network science. For applications like peer-to-peer communication, email, social network interactions, and computer network security, it makes
Externí odkaz:
http://arxiv.org/abs/2311.10337
Given an edge-weighted graph and a set of known seed vertices, a network scientist often desires to understand the graph relationships to explain connections between the seed vertices. When the seed set is 3 or larger Steiner minimal tree - min-weigh
Externí odkaz:
http://arxiv.org/abs/2205.14503
Autor:
Steil, Trevor, Reza, Tahsin, Iwabuchi, Keita, Priest, Benjamin W., Sanders, Geoffrey, Pearce, Roger
Understanding the higher-order interactions within network data is a key objective of network science. Surveys of metadata triangles (or patterned 3-cycles in metadata-enriched graphs) are often of interest in this pursuit. In this work, we develop T
Externí odkaz:
http://arxiv.org/abs/2107.12330
The unsupervised learning of community structure, in particular the partitioning vertices into clusters or communities, is a canonical and well-studied problem in exploratory graph analysis. However, like most graph analyses the introduction of immen
Externí odkaz:
http://arxiv.org/abs/2007.12669
Currently, the dominating constraint in many high performance computing applications is data capacity and bandwidth, in both inter-node communications and even more-so in on-node data motion. A new approach to address this limitation is to make use o
Externí odkaz:
http://arxiv.org/abs/2003.02324
Publikováno v:
ACM Transactions on Parallel Computing (TOPC) 2020
Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: they do not scale to process large graphs and/or support only a restricted set of search templates or usage scena
Externí odkaz:
http://arxiv.org/abs/1912.08453
Although mixed precision arithmetic has recently garnered interest for training dense neural networks, many other applications could benefit from the speed-ups and lower storage cost if applied appropriately. The growing interest in employing mixed p
Externí odkaz:
http://arxiv.org/abs/1912.06217
Publikováno v:
In Journal of Parallel and Distributed Computing November 2023 181
Compression of floating-point data will play an important role in high-performance computing as data bandwidth and storage become dominant costs. Lossy compression of floating-point data is powerful, but theoretical results are needed to bound its er
Externí odkaz:
http://arxiv.org/abs/1805.00546
On Large-Scale Graph Generation with Validation of Diverse Triangle Statistics at Edges and Vertices
Researchers developing implementations of distributed graph analytic algorithms require graph generators that yield graphs sharing the challenging characteristics of real-world graphs (small-world, scale-free, heavy-tailed degree distribution) with e
Externí odkaz:
http://arxiv.org/abs/1803.09021