2 Search Results for "Borassi, Michele"

Document
DOI: 10.4230/LIPIcs.SEA.2025.17
Computing the Exact Radius of Large Graphs

Authors: Stefan Funke, Claudius Proissl, and Sabine Storandt

Published in: LIPIcs, Volume 338, 23rd International Symposium on Experimental Algorithms (SEA 2025)

Abstract
The radius of a graph is an important structural parameter which plays a key role in social network analysis and related applications. It measures the minimum shortest path distance that is required to reach all nodes in the graph from a single node. A node from which all other nodes are within a distance equal to the radius is called a center of the graph. In a graph with n nodes and m edges, the center and the radius can be determined in Õ(nm) by computing shortest path distances between all pairs of nodes. Fine-grained complexity results suggest that asymptotically faster algorithms are unlikely to exist. In this paper, we describe a novel randomized algorithm for exact radius computation in weighted digraphs with an expected running time in Õ(d³m) where d is the so-called combinatorial dimension. Our methodology is inspired by Clarkson’s algorithm for LP-type problems. The value of d denotes the size of a basis, which is a smallest subset of nodes which enforce the same radius as the whole node set. While we show that there exist graphs with d ∈ Θ(n), our empirical analysis reveals that even large real-world graphs have small combinatorial dimension. This allows us to compute the radius in near-linear time on such instances. The significantly improved scalability can be clearly observed in our experimental evaluation on a diverse set of benchmark graphs.
Cite as

Stefan Funke, Claudius Proissl, and Sabine Storandt. Computing the Exact Radius of Large Graphs. In 23rd International Symposium on Experimental Algorithms (SEA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 338, pp. 17:1-17:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{funke_et_al:LIPIcs.SEA.2025.17,
  author =	{Funke, Stefan and Proissl, Claudius and Storandt, Sabine},
  title =	{{Computing the Exact Radius of Large Graphs}},
  booktitle =	{23rd International Symposium on Experimental Algorithms (SEA 2025)},
  pages =	{17:1--17:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-375-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{338},
  editor =	{Mutzel, Petra and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2025.17},
  URN =		{urn:nbn:de:0030-drops-232555},
  doi =		{10.4230/LIPIcs.SEA.2025.17},
  annote =	{Keywords: Radius, Graph Center, LP-type, Combinatorial Dimension}
}
Document
DOI: 10.4230/LIPIcs.ESA.2016.20
KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation

Authors: Michele Borassi and Emanuele Natale

Published in: LIPIcs, Volume 57, 24th Annual European Symposium on Algorithms (ESA 2016)

Abstract
We present KADABRA, a new algorithm to approximate betweenness centrality in directed and undirected graphs, which significantly outperforms all previous approaches on real-world complex networks. The efficiency of the new algorithm relies on two new theoretical contributions, of independent interest. The first contribution focuses on sampling shortest paths, a subroutine used by most algorithms that approximate betweenness centrality. We show that, on realistic random graph models, we can perform this task in time |E|^{1/2+o(1)} with high probability, obtaining a significant speedup with respect to the Theta(|E|) worst-case performance. We experimentally show that this new technique achieves similar speedups on real-world complex networks, as well. The second contribution is a new rigorous application of the adaptive sampling technique. This approach decreases the total number of shortest paths that need to be sampled to compute all betweenness centralities with a given absolute error, and it also handles more general problems, such as computing the k most central nodes. Furthermore, our analysis is general, and it might be extended to other settings, as well.
Cite as

Michele Borassi and Emanuele Natale. KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation. In 24th Annual European Symposium on Algorithms (ESA 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 57, pp. 20:1-20:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

Copy BibTex To Clipboard

@InProceedings{borassi_et_al:LIPIcs.ESA.2016.20,
  author =	{Borassi, Michele and Natale, Emanuele},
  title =	{{KADABRA is an ADaptive Algorithm for Betweenness via Random Approximation}},
  booktitle =	{24th Annual European Symposium on Algorithms (ESA 2016)},
  pages =	{20:1--20:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-015-6},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{57},
  editor =	{Sankowski, Piotr and Zaroliagis, Christos},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2016.20},
  URN =		{urn:nbn:de:0030-drops-63712},
  doi =		{10.4230/LIPIcs.ESA.2016.20},
  annote =	{Keywords: Betweenness centrality, shortest path algorithm, graph mining, sampling, network analysis}
}
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