Graph processingCUDAParallel processingThere is the significant interest nowadays in developing the frameworks for parallelizing the processing of large graphs such as social networks, web graphs, etc. The work has been proposed to parallelize the graph processing on clusters (distributed memory), multi...
a. X-Stream incorporates an autotuner that picks good streaming partition sizes. The autotuner however is biased towards running efficiently from disk and does not do a good job of squeezing things into memory. The following parameters can sometime force the graph to run from memory and avoid ...
X-Stream competes favorably with existing systems for graph processing. Besides sequential access, we identify as one of the main contributors to better performance the fact that X-Stream does not need to sort edge lists during preprocessing. 展开 关键词: Graph Processing Storage Streaming ...
关键词: brain connectome graph theory link communities network DOI: 10.1098/rstb.2013.0527 被引量: 24 年份: 2014 收藏 引用 批量引用 报错 分享 全部来源 免费下载 求助全文 国家科技图书文献中心 (权威机构) 万方医学 doi.org 掌桥科研 dx.doi.org 查看更多 ...
The thresholding problem and variability in the EEG graph network parameters Article Open access 04 November 2022 Introduction Functional connectivity (FC) refers to patterns of statistical dependence in brain activity, such as the blood oxygen level-dependent (BOLD) signal measured via functional magne...
Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009). CAS PubMed Google Scholar Rubinov, M. & Sporns, O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069 (2010)....
The proposed approach uses machine learning and graph theory to develop a new metric called Inverse Square Metric and edge-centric multi-view networks to predict the performance of a lineup in a given situation. The edge-centric approach provides a deep analysis of any condition between two ...
In Ref. [9], a novel graph-based multi-cell scheduling framework is proposed to mitigate the downlink inter-cell interference, in which a dynamic clustering method using channel-aware resource allocation is proposed to provide tunable quality of service. Although network-centric methods have been ...
However, the existing works on graphlet counting obtain the graphlet counts for the entire network as a whole. These works capture the key graphical patterns that prevail in a given network but they fail to meet the demand of the majority of real-life graph related prediction tasks such as ...
A GPU-based, edge-centric graph processing framework called WolfGraph is developed. • The data structure and graph partition in WolfGraph are carefully crafted so as to minimize the graph pre-processing. • A new method is developed to process a graph that is bigger than the global memory...