Distributed-Memory Vertex-Centric Network Embedding for Large-Scale Graphs

Autor:	Riazi, Sara, Norris, Boyana
Rok vydání:	2020
Předmět:	Computer Science - Distributed Parallel and Cluster Computing
Druh dokumentu:	Working Paper
DOI:	10.1109/DataCom.2019.00029
Popis:	Network embedding is an important step in many different computations based on graph data. However, existing approaches are limited to small or middle size graphs with fewer than a million edges. In practice, web or social network graphs are orders of magnitude larger, thus making most current methods impractical for very large graphs. To address this problem, we introduce a new distributed-memory parallel network embedding method based on Apache Spark and GraphX. We demonstrate the scalability of our method as well as its ability to generate meaningful embeddings for vertex classification and link prediction on both real-world and synthetic graphs. Comment: 2019 IEEE 5th International Conference on Big Data Intelligence and Computing (DATACOM)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.04236 Zobrazit plný text záznamu View this record from Arxiv