scholarly journals Community detection in sparse networks via Grothendieck’s inequality

2015 ◽  
Vol 165 (3-4) ◽  
pp. 1025-1049 ◽  
Author(s):  
Olivier Guédon ◽  
Roman Vershynin
Keyword(s):  
Community Detection ◽  
Sparse Networks ◽  
Grothendieck’S Inequality

scholarly journals Pseudo-likelihood methods for community detection in large sparse networks

2013 ◽  
Vol 41 (4) ◽  
pp. 2097-2122 ◽  
Author(s):  
Arash A. Amini ◽  
Aiyou Chen ◽  
Peter J. Bickel ◽  
Elizaveta Levina
Keyword(s):  
Community Detection ◽  
Likelihood Methods ◽  
Pseudo Likelihood ◽  
Sparse Networks

scholarly journals Overcoming the Link Prediction Limitation in Sparse Networks using Community Detection

2021 ◽  
Vol 9 (35) ◽  
pp. 183-190
Author(s):  
Mohammad Pouya Salvati ◽  
Sadegh Sulaimany ◽  
Jamshid Bagherzadeh Mohasefi
Keyword(s):  
Community Detection ◽  
Link Prediction ◽  
Sparse Networks

Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms

2016 ◽  
Author(s):  
Ananth Kalyanaraman ◽  
Mahantesh Halappanavar ◽  
Daniel Chavarría-Miranda ◽  
Hao Lu ◽  
Karthi Duraisamy
Keyword(s):  
Community Detection

Best Neighbor Heuristic Search for Finding Minimum Paths in Transportation Networks

1998 ◽  
Vol 1651 (1) ◽  
pp. 48-52 ◽  
Author(s):  
Jeffrey L. Adler
Keyword(s):  
Heuristic Search ◽  
Transportation Networks ◽  
Transportation Network ◽  
Search Problems ◽  
A Algorithm ◽  
Running Time ◽  
Search Effort ◽  
Wide Range ◽  
Path Search ◽  
Sparse Networks

For a wide range of transportation network path search problems, the A* heuristic significantly reduces both search effort and running time when compared to basic label-setting algorithms. The motivation for this research was to determine if additional savings could be attained by further experimenting with refinements to the A* approach. We propose a best neighbor heuristic improvement to the A* algorithm that yields additional benefits by significantly reducing the search effort on sparse networks. The level of reduction in running time improves as the average outdegree of the network decreases and the number of paths sought increases.

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