Translation of string-and-pin-based shortest path construction into data-scalable agent-based computational models

Abstract

From the viewpoint of strong spatial cognition in graph problems, the shortest path can be identified in one physical action using strings and pins that respectively represent graph edges and vertices. By pulling a start and an end pin, we can construct a series of stretched strings as the shortest path. We use agent-based models (ABMs) to translate this action into computational representations. Assuming that a set of strings and pins are hung on a wall with a start pin, agents are disseminated downward to a destination as gravity forces. We implemented three models: a discrete-event, an asynchronous, and an aggregated agent dissemination on top of the MASS (multi-agent spatial simulation) library. To address large-scale network environments, we blended HDFS into MASS so that a graph data set is read over a cluster system in parallel. This paper presents these ABM implementations and performance measurements over a cluster system.