MaNIACS: Approximate Mining of Frequent Subgraph Patterns through Sampling
G Preti, G De Francisci Morales… - ACM Transactions on …, 2023 - dl.acm.org
We present MaNIACS, a sampling-based randomized algorithm for computing high-quality
approximations of the collection of the subgraph patterns that are frequent in a single, large …
approximations of the collection of the subgraph patterns that are frequent in a single, large …
Statistically-sound Knowledge Discovery from Data: Challenges and Directions
M Riondato - 2023 IEEE 5th International Conference on …, 2023 - ieeexplore.ieee.org
We describe Statistically-sound Knowledge Discovery from Data (StatKDD), a
groundbreaking change of paradigm that shifts the focus of the KDD pipeline from the …
groundbreaking change of paradigm that shifts the focus of the KDD pipeline from the …
Mining Frequent Geo-Subgraphs in a Knowledge Graph
Frequent subgraph mining aims to find all subgraphs that occur frequently in a graph
database or in a single large graph. It finds applications in social networks, citation networks …
database or in a single large graph. It finds applications in social networks, citation networks …
[PDF][PDF] Frequent Subgraph Mining via Sampling with Rigorous Guarantees
P Pellizzoni - 2022 - thesis.unipd.it
Frequent subgraph mining is a fundamental task in the analysis of collections of graphs that
aims at finding all the subgraphs that appear with more than a user-specified frequency in …
aims at finding all the subgraphs that appear with more than a user-specified frequency in …