Abstract
As real-world graphs are often evolving over time, interest in analyzing the temporal behavior of graphs has grown. Herein, we propose Auxo, a novel temporal graph management system to support temporal graph analysis. It supports both efficient global and local queries with low space overhead. Auxo organizes temporal graph data in spatio-temporal chunks. A chunk spans a particular time interval and covers a set of vertices in a graph. We propose chunk layout and chunk splitting designs to achieve the desired efficiency and the above-mentioned goals. First, by carefully choosing the time split policy, Auxo achieves linear complexity in both space usage and query time. Second, graph splitting further improves the worst-case query time, and reduces the performance variance introduced by splitting operations. Third, Auxo optimizes the data layout inside chunks, thereby significantly imporving the performance of traverse-based graph queries. Experimental evaluation showed that Auxo achieved 2.9× to 12.1× improvement for global queries, and 1.7× to 2.7× improvement for local queries, as compared with state-of-the-art open-source solutions.
Highlights
As real-world graphs are often evolving over time, interest in the temporal behavior of graphs has increased
Temporal graph analysis usually accesses a series of snapshots of a graph over time, and either performs iterative computation over the full snapshots or visits individual vertices
We focus on designing a temporal graph management system that efficiently handles the storage and retrieval of evolving graph structures
Summary
Graphs are an important data model for representing complex relationships in big data applications. Representative real-world graph applications include the Web, social networks, road networks, and semantic networks. As real-world graphs are often evolving over time, interest in the temporal behavior of graphs has increased. Temporal graph analysis usually accesses a series of snapshots of a graph over time, and either performs iterative computation over the full snapshots or visits individual vertices. Temporal graph analysis requires re-designing both components. Recent work has proposed Chronos, an in-memory temporal graph engine[4] that exploits locality-aware batch scheduling to speed up the computation of each vertex across multiple graph snapshots. We focus on designing a temporal graph management system that efficiently handles the storage and retrieval of evolving graph structures
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