A Framework for Computing on Large Dynamic Graphs

TL;DR

This paper introduces a comprehensive framework for efficient online and offline computation on large dynamic graphs, featuring a novel data model, a replica-coherence protocol, and a protocol dataflow model to handle evolving graph data.

Contribution

It presents a new data model, a replica-coherence protocol, and a protocol dataflow computing model tailored for large dynamic graphs, enabling effective analysis of their evolving properties.

Findings

Supports rich evolving vertex and edge data types

Improves data locality through replica-coherence protocol

Enables analysis of temporal patterns in dynamic graphs

Abstract

This proposal presents a graph computing framework intending to support both online and offline computing on large dynamic graphs efficiently. The framework proposes a new data model to support rich evolving vertex and edge data types. It employs a replica-coherence protocol to improve data locality thus can adapt to data access patterns of different algorithms. A new computing model called protocol dataflow is proposed to implement and integrate various programming models for both online and offline computing on large dynamic graphs. A central topic of the proposal is also the analysis of large real dynamic graphs using our proposed framework. Our goal is to calculate the temporal patterns and properties which emerge when the large graphs keep evolving. Thus we can evaluate the capability of the proposed framework. Key words: Large dynamic graph, programming model, distributed computing.