Structure-preserving sparsification of social networks
G Lindner, CL Staudt, M Hamann… - Proceedings of the …, 2015 - dl.acm.org
Sparsification reduces the size of networks while preserving structural and statistical
properties of interest. Various sparsifying algorithms have been proposed in different …
properties of interest. Various sparsifying algorithms have been proposed in different …
Structure-preserving sparsification methods for social networks
M Hamann, G Lindner, H Meyerhenke… - Social Network Analysis …, 2016 - Springer
Sparsification reduces the size of networks while preserving structural and statistical
properties of interest. Various sparsifying algorithms have been proposed in different …
properties of interest. Various sparsifying algorithms have been proposed in different …
Single-and multi-level network sparsification by algebraic distance
E John, I Safro - Journal of Complex Networks, 2017 - academic.oup.com
Network sparsification methods play an important role in modern network analysis when fast
estimation of computationally expensive properties (such as the diameter, centrality indices …
estimation of computationally expensive properties (such as the diameter, centrality indices …
An efficient algorithm for unweighted spectral graph sparsification
DG Anderson, M Gu, C Melgaard - arXiv preprint arXiv:1410.4273, 2014 - arxiv.org
Spectral graph sparsification has emerged as a powerful tool in the analysis of large-scale
networks by reducing the overall number of edges, while maintaining a comparable graph …
networks by reducing the overall number of edges, while maintaining a comparable graph …
Detection of top-k central nodes in social networks: A compressive sensing approach
H Mahyar - Proceedings of the 2015 IEEE/ACM International …, 2015 - dl.acm.org
In analysing the structural organization of a social network, identifying important nodes has
been a fundamental problem. The concept of network centrality deals with the assessment of …
been a fundamental problem. The concept of network centrality deals with the assessment of …
A Generic Graph Sparsification Framework using Deep Reinforcement Learning
The interconnectedness and interdependence of modern graphs are growing ever more
complex, causing enormous resources for processing, storage, communication, and …
complex, causing enormous resources for processing, storage, communication, and …
Efficient sparse matrix multiplication on gpu for large social network analysis
As a number of social network services appear online recently, there have been many
attempts to analyze social networks for extracting valuable information. Most existing …
attempts to analyze social networks for extracting valuable information. Most existing …
Triangle-aware spectral sparsifiers and community detection
K Sotiropoulos, CE Tsourakakis - Proceedings of the 27th ACM SIGKDD …, 2021 - dl.acm.org
Triangle-aware graph partitioning has proven to be a successful approach to finding
communities in real-world data [8, 40, 51, 54]. But how can we explain its empirical success …
communities in real-world data [8, 40, 51, 54]. But how can we explain its empirical success …
Nonparametric sparsification of complex multiscale networks
NJ Foti, JM Hughes, DN Rockmore - PloS one, 2011 - journals.plos.org
Many real-world networks tend to be very dense. Particular examples of interest arise in the
construction of networks that represent pairwise similarities between objects. In these cases …
construction of networks that represent pairwise similarities between objects. In these cases …
A Generic Graph Sparsification Framework using Deep Reinforcement Learning
The interconnectedness and interdependence of modern graphs are growing ever more
complex, causing enormous resources for processing, storage, communication, and …
complex, causing enormous resources for processing, storage, communication, and …