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- research-articleDecember 2023
SILVAN: Estimating Betweenness Centralities with Progressive Sampling and Non-uniform Rademacher Bounds
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 3Article No.: 52, Pages 1–55https://doi.org/10.1145/3628601“Sim Sala Bim!” —Silvan,
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, ...
- research-articleMarch 2023
Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 17, Issue 6Article No.: 78, Pages 1–47https://doi.org/10.1145/3577021“[A]llain Gersten, Hopfen, und Wasser” — 1516 Reinheitsgebot
We present Bavarian, a collection of sampling-based algorithms for approximating the Betweenness Centrality (BC) of all vertices in a graph. Our algorithms use Monte-Carlo Empirical Rademacher ...
- research-articleJuly 2022
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 6Article No.: 124, Pages 1–29https://doi.org/10.1145/3532187“I’m an MC still as honest” – Eminem, Rap God
We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in ...
- research-articleSeptember 2020
Probabilistic Modeling for Frequency Vectors Using a Flexible Shifted-Scaled Dirichlet Distribution Prior
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 6Article No.: 69, Pages 1–35https://doi.org/10.1145/3406242Burstiness and overdispersion phenomena of count vectors pose significant challenges in modeling such data accurately. While the dependency assumption of the multinomial distribution causes its failure to model frequency vectors in several machine ...
- research-articleJune 2020
MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 5Article No.: 56, Pages 1–31https://doi.org/10.1145/3385653We present MiSoSouP, a suite of algorithms for extracting high-quality approximations of the most interesting subgroups, according to different popular interestingness measures, from a random sample of a transactional dataset. We describe a new ...
- research-articleMarch 2020
Linearization of Dependency and Sampling for Participation-based Betweenness Centrality in Very Large B-hypergraphs
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 14, Issue 3Article No.: 25, Pages 1–41https://doi.org/10.1145/3375399A B-hypergraph consisting of nodes and directed hyperedges is a generalization of the directed graph. A directed hyperedge in the B-hypergraph represents a relation from a set of source nodes to a single destination node. We suggest one possible ...
- research-articleSeptember 2019
Bayesian Model Selection Approach to Multiple Change-Points Detection with Non-Local Prior Distributions
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 5Article No.: 48, Pages 1–17https://doi.org/10.1145/3340804We propose a Bayesian model selection (BMS) boundary detection procedure using non-local prior distributions for a sequence of data with multiple systematic mean changes. By using the non-local priors in the BMS framework, the BMS method can effectively ...
- research-articleJuly 2018
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 12, Issue 5Article No.: 61, Pages 1–38https://doi.org/10.1145/3208351ABPA Ξ AΣ (ABRAXAS): Gnostic word of mystic meaning.
We present ABRA, a suite of algorithms to compute and maintain probabilistically guaranteed high-quality approximations of the betweenness centrality of all nodes (or edges) on both static and fully ...
- research-articleJanuary 2018
De-anonymizing Clustered Social Networks by Percolation Graph Matching
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 12, Issue 2Article No.: 21, Pages 1–39https://doi.org/10.1145/3127876Online social networks offer the opportunity to collect a huge amount of valuable information about billions of users. The analysis of this data by service providers and unintended third parties are posing serious treats to user privacy. In particular, ...
- research-articleJune 2017
TRIÈST: Counting Local and Global Triangles in Fully Dynamic Streams with Fixed Memory Size
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 4Article No.: 43, Pages 1–50https://doi.org/10.1145/3059194“Ogni lassada xe persa.”1-- Proverb from Trieste, Italy.
We present trièst, a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local (i.e., incident to each vertex) number of ...
- research-articleApril 2017
A Randomized Rounding Algorithm for Sparse PCA
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 3Article No.: 38, Pages 1–26https://doi.org/10.1145/3046948We present and analyze a simple, two-step algorithm to approximate the optimal solution of the sparse PCA problem. In the proposed approach, we first solve an ℓ1-penalized version of the NP-hard sparse PCA optimization problem and then we use a ...
