scholarly journals Politician Family Networks and Electoral Outcomes: Evidence from the Philippines

2017 ◽  
Vol 107 (10) ◽  
pp. 3006-3037 ◽  
Author(s):  
Cesi Cruz ◽  
Julien Labonne ◽  
Pablo QuerubÍn
Keyword(s):  
Large Scale ◽  
The Philippines ◽  
Network Centrality ◽  
Family Network ◽  
Family Networks ◽  
Electoral Success ◽  
Large Scale Data ◽  
Electoral Outcomes ◽  
Elite Status ◽  
Scale Data

We demonstrate the importance of politician social networks for electoral outcomes. Using large-scale data on family networks from over 20 million individuals in 15,000 villages in the Philippines, we show that candidates for public office are disproportionately drawn from more central families and family network centrality contributes to higher vote shares during the elections. Consistent with our theory of political intermediation, we present evidence that family network centrality facilitates relationships of political exchange. Moreover, we show that family networks exercise an effect independent of wealth, historical elite status, or previous electoral success. (JEL D72, D85, O17, Z13)

scholarly journals Social Network Structures and the Politics of Public Goods Provision: Evidence from the Philippines

2020 ◽  
Vol 114 (2) ◽  
pp. 486-501 ◽  
Author(s):  
CESI CRUZ ◽  
JULIEN LABONNE ◽  
PABLO QUERUBÍN
Keyword(s):  
Public Goods ◽  
Large Scale ◽  
Political Competition ◽  
The Philippines ◽  
Public Goods Provision ◽  
Family Networks ◽  
Detection Algorithms ◽  
Political Incentives ◽  
Large Scale Data ◽  
The Relationship

We study the relationship between social structure and political incentives for public goods provision. We argue that when politicians—rather than communities—are responsible for the provision of public goods, social fractionalization may decrease the risk of elite capture and lead to increased public goods provision and electoral competition. We test this using large-scale data on family networks from over 20 million individuals in 15,000 villages of the Philippines. We take advantage of naming conventions to assess intermarriage links between families and use community detection algorithms to identify the relevant clans in those villages. We show that there is more public goods provision and political competition in villages with more fragmented social networks, a result that is robust to controlling for a large number of village characteristics and to alternative estimation techniques.

ProGen:Provenance database generator for large-scale data set

2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN
Keyword(s):  
Large Scale ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

COMMUNITY-CURATED DATA RESOURCES AND LARGE-SCALE DATA-MODEL SYNTHESES: THE CHILDREN OF COHMAP

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Local and global approaches of affinity propagation clustering for large scale data

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang
Keyword(s):  
Large Scale ◽  
Affinity Propagation ◽  
Large Scale Data ◽  
Affinity Propagation Clustering ◽  
Scale Data

scholarly journals A Framework for International Collaboration on ITER Using Large-Scale Data Transfer to Enable Near-Real-Time Analysis

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  
Keyword(s):  
Real Time ◽  
International Collaboration ◽  
Large Scale ◽  
Data Transfer ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

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