1 code implementation • 7 Dec 2020 • Carlos H. C. Teixeira, Mayank Kakodkar, Vinícius Dias, Wagner Meira Jr., Bruno Ribeiro
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 is prohibitively expensive.
Social and Information Networks Data Structures and Algorithms
no code implementations • NeurIPS 2020 • Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami, Bruno Ribeiro
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph.
no code implementations • 8 Oct 2020 • Leonardo Cotta, Carlos H. C. Teixeira, Ananthram Swami, Bruno Ribeiro
Existing Graph Neural Network (GNN) methods that learn inductive unsupervised graph representations focus on learning node and edge representations by predicting observed edges in the graph.
1 code implementation • 14 Sep 2018 • Carlos H. C. Teixeira, Leonardo Cotta, Bruno Ribeiro, Wagner Meira Jr
In this work we propose R-GPM, a parallel computing framework for graph pattern mining (GPM) through a user-defined subgraph relation.
no code implementations • 14 Oct 2015 • Carlos H. C. Teixeira, Alexandre J. Fonseca, Marco Serafini, Georgos Siganos, Mohammed J. Zaki, Ashraf Aboulnaga
However, these platforms do not represent a good match for distributed graph mining problems, as for example finding frequent subgraphs in a graph.
Distributed, Parallel, and Cluster Computing