no code implementations • 24 Mar 2023 • Sandeep Polisetty, Juelin Liu, Kobi Falus, Yi Ren Fung, Seung-Hwan Lim, Hui Guan, Marco Serafini
Large-scale graphs with billions of edges are ubiquitous in many industries, science, and engineering fields such as recommendation systems, social graph analysis, knowledge base, material science, and biology.
no code implementations • 5 May 2021 • Marco Serafini, Hui Guan
In this paper, we review and compare the two approaches.
no code implementations • 14 Sep 2020 • Abhinav Jangda, Sandeep Polisetty, Arjun Guha, Marco Serafini
Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, sample the graph to produce mini-batches that are suitable for training a DNN.
2 code implementations • 26 Oct 2015 • Muhammad Anis Uddin Nasir, Gianmarco De Francisci Morales, David Garcia-Soriano, Nicolas Kourtellis, Marco Serafini
We study the problem of load balancing in distributed stream processing engines, which is exacerbated in the presence of skew.
Distributed, Parallel, and Cluster Computing
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