iGraph: an incremental data processing system for dynamic graph

With the popularity of social network, the demand for real-time processing of graph data is increasing. However, most of the existing graph systems adopt a batch processing mode, therefore the overhead of maintaining and processing of dynamic graph is significantly high. In this paper, we design iGraph, an incremental graph processing system for dynamic graph with its continuous updates. The contributions of iGraph include: 1) a hash-based graph partition strategy to enable fine-grained graph updates; 2) a vertexbased graph computing model to support incremental data processing; 3) detection and rebalance methods of hotspot to address the workload imbalance problem during incremental processing. Through the general-purpose API, iGraph can be used to implement various graph processing algorithms such as PageRank. We have implemented iGraph on Apache Spark, and experimental results show that for real life datasets, iGraph outperforms the original GraphX in respect of graph update and graph computation.