Ringo: Interactive Graph Analytics on Big-Memory Machines
Yonathan Perez, Rok Sosic, Arijit Banerjee, Rohan Puttagunta, Martin, Raison, Pararth Shah, Jure Leskovec

TL;DR
Ringo is an interactive graph analytics system optimized for big-memory machines, enabling efficient analysis, manipulation, and exploration of large graphs with over 200 functions, facilitating rapid data mining workflows.
Contribution
Ringo introduces a high-performance, easy-to-use graph analytics system leveraging large-memory machines, integrating data manipulation and extensive analytics functions for improved graph analysis.
Findings
Single big-memory machine offers excellent performance for large graph analytics.
Ringo simplifies graph construction from relational data tables.
Supports rapid, iterative data exploration workflows.
Abstract
We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can…
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