Dec 12, 2018 · We develop an optimization framework on the basis of the incremental computing model to accelerate the convergence of processing time-evolving graphs.
An optimization framework on the basis of the incremental computing model to accelerate the convergence of processing time-evolving graphs thus achieving ...
A low-latency computing framework for time-evolving graphs ; Journal: The Journal of Supercomputing, 2018, № 7, p. 3673-3692 ; Publisher: Springer Science and ...
In this paper, we make the very first attempt to minimize the computation time of solving TSP on time-evolving graphs. By exploring parallel computing power and ...
Nov 24, 2015 · The challenges I can foresee: with a constantly updating graph, I need to process the whole graph every time someone requests information...
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In our framework, time- aware node embeddings summarizing multi-hop information are computed using only single-hop operations on the incoming edges. We evaluate ...
Jun 18, 2021 · This paper presents RisGraph, a real-time streaming system that provides low-latency analysis for each update with high throughput.
Jul 17, 2023 · We propose graph-sprints a general purpose feature extraction framework for continuous-time-dynamic-graphs (CTDGs) that has low latency and is competitive with ...
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To evaluate our ideas, we describe a new graph-streaming framework called Aspen that enables concurrent, low-latency processing of queries and updates on graphs ...
A low-latency computing framework for time-evolving graphs. Shuo Ji; Yinliang Zhao; Xiaomei Zhao. OriginalPaper 12 December 2018 Pages: 3673 - 3692. Asymmetric ...