5 citations · 13 across the 5 of their papers we have counts for
5 papers
Efficient Strategies for Graph Pattern Mining Algorithms on GPUs
Samuel Ferraz, Vinicius Dias, Carlos H. C. Teixeira +2
Graph Pattern Mining (GPM) is an important, rapidly evolving, and computation demanding area. GPM computation relies on subgraph enumeration, which consists in extracting subgraphs…
Sequential Stratified Regeneration: MCMC for Large State Spaces with an Application to Subgraph Count Estimation
Carlos H. C. Teixeira, Mayank Kakodkar, Vinícius Dias +2
This work considers the general task of estimating the sum of a bounded function over the edges of a graph, given neighborhood query access and where access to the entire network i…
Unsupervised Joint -node Graph Representations with Compositional Energy-Based Models
Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami +1
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in…
Graph Pattern Mining and Learning through User-defined Relations (Extended Version)
Carlos H. C. Teixeira, Leonardo Cotta, Bruno Ribeiro +1
In this work we propose R-GPM, a parallel computing framework for graph pattern mining (GPM) through a user-defined subgraph relation. More specifically, we enable the computation…
Arabesque: A System for Distributed Graph Mining - Extended version
Carlos H. C. Teixeira, Alexandre J. Fonseca, Marco Serafini +3
Distributed data processing platforms such as MapReduce and Pregel have substantially simplified the design and deployment of certain classes of distributed graph analytics algorit…