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pro vyhledávání: '"Cruciani, Antonio"'
We study robust and efficient distributed algorithms for building and maintaining distributed data structures in dynamic Peer-to-Peer (P2P) networks. P2P networks are characterized by a high level of dynamicity with abrupt heavy node \emph{churn} (no
Externí odkaz:
http://arxiv.org/abs/2409.10235
Autor:
Cruciani, Antonio
In this work, we present a new algorithm to approximate the percolation centrality of every node in a graph. Such a centrality measure quantifies the importance of the vertices in a network during a contagious process. In this paper, we present a ran
Externí odkaz:
http://arxiv.org/abs/2408.02389
Autor:
Cruciani, Antonio
We present MANTRA, a framework for approximating the temporal betweenness centrality of all nodes in a temporal graph. Our method can compute probabilistically guaranteed high-quality temporal betweenness estimates (of nodes and temporal edges) under
Externí odkaz:
http://arxiv.org/abs/2304.08356
We propose PROPAGATE, a fast approximation framework to estimate distance-based metrics on very large graphs such as the (effective) diameter, the (effective) radius, or the average distance within a small error. The framework assigns seeds to nodes
Externí odkaz:
http://arxiv.org/abs/2301.06499
Autor:
Cruciani, Antonio
We present a collection of sampling-based algorithms for approximating the temporal betweenness centrality of all nodes in a temporal graph. Our methods can compute probabilistically guaranteed high-quality temporal betweenness estimates (of nodes an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65545992bcc8b9640de84352fbe46cd2
Identifying influential nodes in a network is arguably one of the most important tasks in graph mining and network analysis. A large variety of centrality measures, all aiming at correctly quantifying a node’s importance in the network, have been f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6590a0a5561093c200d1667ffe15362d