70 citations · 207 across the 23 of their papers we have counts for
7 papers · 1 filter
SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds
Leonardo Pellegrina, Fabio Vandin
Betweenness centrality is a popular centrality measure with applications in several domains, and whose exact computation is impractical for modern-sized networks. We present SILVAN…
Scalable Distributed Approximation of Internal Measures for Clustering Evaluation
Federico Altieri, Andrea Pietracaprina, Geppino Pucci +1
The most widely used internal measure for clustering evaluation is the silhouette coefficient, whose naive computation requires a quadratic number of distance calculations, which i…
Clustering Uncertain Graphs
Matteo Ceccarello, Carlo Fantozzi, Andrea Pietracaprina +2
An uncertain graph can be viewed as a probability space whose outcomes (referred to as \emph{possible worlds}) are subgraphs of $\ma…
Communication Lower Bounds for Distributed-Memory Computations
Michele Scquizzato, Francesco Silvestri
We give lower bounds on the communication complexity required to solve several computational problems in a distributed-memory parallel machine, namely standard matrix multiplicatio…
Space-Efficient Parallel Algorithms for Combinatorial Search Problems
Andrea Pietracaprina, Geppino Pucci, Francesco Silvestri +1
We present space-efficient parallel strategies for two fundamental combinatorial search problems, namely, backtrack search and branch-and-bound, both involving the visit of an -…
Mining Top-K Frequent Itemsets Through Progressive Sampling
Andrea Pietracaprina, Matteo Riondato, Eli Upfal +1
We study the use of sampling for efficiently mining the top-K frequent itemsets of cardinality at most w. To this purpose, we define an approximation to the top-K frequent itemsets…
MADMX: A Novel Strategy for Maximal Dense Motif Extraction
Roberto Grossi, Andrea Pietracaprina, Nadia Pisanti +3
We develop, analyze and experiment with a new tool, called MADMX, which extracts frequent motifs, possibly including don't care characters, from biological sequences. We introduce…