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Found 5 papers, 2 papers with code
Sequential Stratified Regeneration: MCMC for Large State Spaces with an Application to Subgraph Count Estimation
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
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models
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.
Unsupervised Joint $k$-node Graph Representations with Compositional Energy-Based Models
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.
Graph Pattern Mining and Learning through User-defined Relations (Extended Version)
In this work we propose R-GPM, a parallel computing framework for graph pattern mining (GPM) through a user-defined subgraph relation.
Arabesque: A System for Distributed Graph Mining - Extended version
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
