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Centrality measures for graphons: Accounting for uncertainty in networks
[article]
2018
arXiv
pre-print
As relational datasets modeled as graphs keep increasing in size and their data-acquisition is permeated by uncertainty, graph-based analysis techniques can become computationally and conceptually challenging. In particular, node centrality measures rely on the assumption that the graph is perfectly known -- a premise not necessarily fulfilled for large, uncertain networks. Accordingly, centrality measures may fail to faithfully extract the importance of nodes in the presence of uncertainty. To
arXiv:1707.09350v4
fatcat:mjs65googrfznhiebsoihj57ry