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This proposal presents a graph computing framework intending to support bothonline and offline computing on large dynamic graphs efficiently. The frameworkproposes 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 adaptto data access patterns of different algorithms. A new computing model calledprotocol dataflow is proposed to implement and integrate various programmingmodels for both online and offline computing on large dynamic graphs. A centraltopic of the proposal is also the analysis of large real dynamic graphs usingour proposed framework. Our goal is to calculate the temporal patterns andproperties which emerge when the large graphs keep evolving. Thus we canevaluate the capability of the proposed framework. Key words: Large dynamicgraph, programming model, distributed computing.

A Framework for Computing on Large Dynamic Graphs