| Summary: | Personalized PageRank (PPR) is a widely used graph processing algorithm used to calculate the importance of source nodes in a graph. Generally, PPR is executed by using a high-performance microprocessor of a server, but it needs to be executed on edge devices to guarantee data privacy and network latency. However, since PPR has a variety of computation/memory characteristics that vary depending on the graph datasets, it causes performance/energy inefficiency when it is executed on edge devices with limited hardware resources. In this paper, we propose <i>HedgeRank</i>, a heterogeneity-aware, energy-efficient, partitioning technique of personalized PageRank at the edge. <i>HedgeRank</i> partitions the PPR subprocesses and allocates them to appropriate edge devices by considering their computation capability and energy efficiency. When combining low-power and high-performance edge devices, <i>HedgeRank</i> improves the execution time and energy consumption of PPR execution by up to 26.7% and 15.2% compared to the state-of-the-art PPR technique.
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