Sparsification reduces the size of networks while preserving structural and statistical properties of interest. Various sparsifying algorithms have been proposed in different …
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 …
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 …
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 …
R Wickman, X Zhang, W Li - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and …
YY Jo, SW Kim, DH Bae - Proceedings of the 24th ACM International on …, 2015 - dl.acm.org
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 …
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 …
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 …
The interconnectedness and interdependence of modern graphs are growing ever more complex, causing enormous resources for processing, storage, communication, and …