Experimental performance study of a user intensive and large-scale digital library framework

2006 ◽  
Author(s):  
Xiangwen Liao ◽  
Binxing Fang ◽  
Weihua Luo ◽  
Bin Wang
Keyword(s):  
Digital Library ◽  
Large Scale ◽  
Performance Study ◽  
Experimental Performance

Experimental performance of a large‐scale cryosorption pump

1987 ◽  
Vol 5 (4) ◽  
pp. 2563-2567 ◽  
Author(s):  
J. R. Coupland ◽  
D. P. Hammond ◽  
W. Bächler ◽  
H. H. Klein
Keyword(s):  
Large Scale ◽  
Experimental Performance

Experimental performance study on a dual-mode CO2 heat pump system with thermal storage

2017 ◽  
Vol 115 ◽  
pp. 393-405 ◽  
Author(s):  
Fang Liu ◽  
Weiquan Zhu ◽  
Yang Cai ◽  
Eckhard A. Groll ◽  
Jianxing Ren ◽  
...  
Keyword(s):  
Heat Pump ◽  
Thermal Storage ◽  
Performance Study ◽  
Dual Mode ◽  
Pump System ◽  
Heat Pump System ◽  
Experimental Performance

An Early Performance Study of Large-Scale POWER8 SMP Systems

2016 ◽  
Author(s):  
Xing Liu ◽  
Daniele Buono ◽  
Fabio Checconi ◽  
Jee W. Choi ◽  
Xinyu Que ◽  
...  
Keyword(s):  
Large Scale ◽  
Performance Study ◽  
Early Performance

Performance Study of Large Scale Network Slice Deployment in a 5G Core Testbed

2021 ◽  
Author(s):  
Shwetha Vittal ◽  
Aditya Chilukuri ◽  
Sourav Sarkar ◽  
Akshitha Shinde ◽  
Antony Franklin A
Keyword(s):  
Large Scale ◽  
Performance Study ◽  
Large Scale Network ◽  
Scale Network

Design and performance study on a large-scale hybrid CPV/T system based on unsteady-state thermal model

Solar Energy ◽  
2019 ◽  
Vol 177 ◽  
pp. 427-439 ◽  
Author(s):  
Zexin Wang ◽  
Jinjia Wei ◽  
Gaoming Zhang ◽  
Huling Xie ◽  
Muhammad Khalid
Keyword(s):  
Thermal Model ◽  
Large Scale ◽  
Unsteady State ◽  
Performance Study ◽  
And Performance ◽  
T System

scholarly journals Local memory boosts label propagation for community detection

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Antonio Maria Fiscarelli ◽  
Matthias R. Brust ◽  
Grégoire Danoy ◽  
Pascal Bouvry
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Linear Time ◽  
Topological Analysis ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Performance Study ◽  
Global Knowledge ◽  
Local Optima ◽  
Propagation Algorithm

Abstract The objective of a community detection algorithm is to group similar nodes that are more connected to each other than with the rest of the network. Several methods have been proposed but many are of high complexity and require global knowledge of the network, which makes them less suitable for large-scale networks. The Label Propagation Algorithm initially assigns a distinct label to each node that iteratively updates its label with the one of the majority of its neighbors, until consensus is reached among all nodes in the network. Nodes sharing the same label are then grouped into communities. It runs in near linear time and is decentralized, but it gets easily stuck in local optima and often returns a single giant community. To overcome these problems we propose MemLPA, a variation of the classical Label Propagation Algorithm where each node implements a memory mechanism that allows them to “remember” about past states of the network and uses a decision rule that takes this information into account. We demonstrate through extensive experiments, on the Lancichinetti-Fortunato-Radicchi benchmark and a set of real-world networks, that MemLPA outperforms other existing label propagation algorithms that implement memory and some of the well-known community detection algorithms. We also perform a topological analysis to extend the performance study and compare the topological properties of the communities found to the ground-truth community structure.

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