Lightweight Label Propagation for Large-Scale Network Data

2019 ◽  
pp. 1-1
Author(s):  
Yu-Feng Li ◽  
De-Ming Liang
Keyword(s):  
Large Scale ◽  
Label Propagation ◽  
Network Data ◽  
Large Scale Network ◽  
Scale Network

Label propagation algorithm based on node similarity driven by local information

2019 ◽  
Vol 33 (30) ◽  
pp. 1950363
Author(s):  
Chen Song ◽  
Guoyan Huang ◽  
Bo Yin ◽  
Bing Zhang ◽  
Xinqian Liu
Keyword(s):  
Large Scale ◽  
Linear Time ◽  
Local Information ◽  
Label Propagation ◽  
Selection Scheme ◽  
Original Algorithm ◽  
Large Scale Network ◽  
Propagation Algorithm ◽  
Node Similarity ◽  
Scale Network

Label propagation algorithm (LPA) attracts wide attention in community detection field for its near linear time complexity in large scale network. However, the algorithm adopts a random selection scheme in label updating strategy, which results in unstable division and poor accuracy. In this paper, five different indicators of node similarity are introduced based on network local information to distinguish nodes and a new label updating method is proposed. When there are multiple maximum neighbor labels in the propagation process, the maximum label corresponding to the most similar node is selected for updating instead of a random one. Five different forms of improved LPA are proposed which are named as SAL-LPA, SOR-LPA, JAC-LPA, SOR-LPA, HDI-LPA and HPI-LPA. The experiment results on real-world and artificial benchmark networks show that the improved LPA greatly improves the performance of the original algorithm, among which HPI-LPA is the best.

scholarly journals Lightweight Label Propagation for Large-Scale Network Data

2018 ◽  
Author(s):  
De-Ming Liang ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Label Propagation ◽  
Stochastic Gradient Descent ◽  
Small Scale ◽  
Warm Start ◽  
Large Scale Data ◽  
Large Scale Network ◽  
Memory Overhead ◽  
Scale Network ◽  
Scale Data

Label propagation spreads the soft labels from few labeled data to a large amount of unlabeled data according to the intrinsic graph structure. Nonetheless, most label propagation solutions work under relatively small-scale data and fail to cope with many real applications, such as social network analysis, where graphs usually have millions of nodes. In this paper, we propose a novel algorithm named \algo to deal with large-scale data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to reduce memory overhead and accelerate the solving process. We also give a theoretical analysis on the necessity of the warm-start technique for label propagation. Experiments show that our algorithm can handle million-scale graphs in few seconds while achieving highly competitive performance with existing algorithms.

scholarly journals Using Trellis software to enhance high-quality large-scale network data collection in the field

2021 ◽  
Vol 66 ◽  
pp. 171-184
Author(s):  
Alina Lungeanu ◽  
Mark McKnight ◽  
Rennie Negron ◽  
Wolfgang Munar ◽  
Nicholas A. Christakis ◽  
...  
Keyword(s):  
Data Collection ◽  
Large Scale ◽  
Network Data ◽  
High Quality ◽  
Large Scale Network ◽  
Scale Network

scholarly journals Network in Belle II

2020 ◽  
Vol 245 ◽  
pp. 07019
Author(s):  
Silvio Pardi
Keyword(s):  
Large Scale ◽  
Data Transfer ◽  
Network Data ◽  
Phase 3 ◽  
Raw Data ◽  
Large Scale Network ◽  
The Status ◽  
Scale Network ◽  
Belle Ii ◽  
Data Centres

Belle II has started the Phase 3 data taking with a fully equipped detector. The data flow at the maximum luminosity is expected to be 12PB of data/year and will be analysed by a cutting-edge computing infrastructure spread over 26 Countries. Several of the major computing centres for HEP in Europe, USA and Canada will store the second copy of RAW data. In this scenario, the international network infrastructure for research plays a key role in supporting and orchestrating all the activities of data analysis and replication. The large-scale network data challenge will also take advantage from LHCONE VRF service and the support of network experts of KEKCC, Belle II sites and NREN. The program of major upgrade in 2019 empowered the connection among Japan, Europe and USA over a 100Gb geographic ring. In this work, we summarize the network requirements needed to accomplish all the tasks provided by the Belle II computing model. We also highlight the status of the major network links that support and advance Belle II. Lastly, we present the results of the last Network Data Challenge campaign performed between KEK and the main RAW data centres with the additional usage of the Data Transfer Node service provided by GÉANT.

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network
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