GraphLab: A new framework for parallel machine learning - Low et al. 2010 In this paper we propose GraphLab, a new parallel framework for ML which exploits the sparse structure and common computational patterns of ML algorithms. GraphLab enables ML experts to easily design and implement efficient scalable parallel algorithms by composing problem specific computation, … Continue reading GraphLab: A new framework for parallel machine learning
Tag: Graph
Graph processing systems and algorithms
Pregel: A System for Large-Scale Graph Processing
Pregel: A System for Large-Scale Graph Processing - Malewicz et al. (Google) 2010 "Many practical computing problems concern large graphs." Yesterday we looked at some of the models for understanding networks and graphs. Today's paper focuses on processing of graphs, especially the efficient processing of large graphs where large can mean billions of vertices and … Continue reading Pregel: A System for Large-Scale Graph Processing
Exploring Complex Networks
Exploring Complex Networks - Strogatz 2001 Network anatomy is important to characterize because structure always affects function... Written in 2001, this article - recently recommended by Werner Vogels in his 'Back-to-Basics' series - explores the topic of complex networks. It turns out that the behaviour of individual nodes, and the way that we connect them … Continue reading Exploring Complex Networks
TAO: Facebook’s Distributed Data Store for the Social Graph
TAO: Facebook's Distributed Data Store for the Social Graph Bronson et al. (Facebook) 2013 A single Facebook page may aggregate and filter hundreds of items from the social graph. We present each user with content tailored to them, and we filter every item with privacy checks that take into account the current viewer. This extreme … Continue reading TAO: Facebook’s Distributed Data Store for the Social Graph
the morning paper 