Agent based systems are increasingly being applied in a wide range of areas including telecommunications, business process modeling, computer games, control of mobile robots, and military simulations. Such systems are typically extremely complex and it is often useful to be able to simulate an agent based system to learn more about its behavior or investigate the implications of alternative architectures. The authors discuss the application of distributed discrete event simulation techniques to the simulation of multiagent systems. We identify the efficient distribution of the agents' environment as a key problem in the simulation of agent based systems and present an approach to the decomposition of the environment that facilitates load balancing.

The level of research that e-voting has attracted is a testimony of its importance as a key element in the implementation of e-government. It is argued that the ease with which voting can be performed will increase participation and enhance accountability. This convenience however, generates a set of specific requirements, not least the ability of the underlying distributed system to model the behaviour of manual systems. More specifically, the elimination of direct physical intervention entails a careful management of the implications of virtual participation. The scope of this work concerns the identification and integration of specific mechanisms for addressing issues of security, privacy and accountability. The aim of this paper is to present a case study on the design and implementation of an e-voting prototype system, and to provide a context for the selection and deployment of relevant mechanisms.

In this article we describe HLA_AGENT, a tool for the distributed simulation of agent-based systems, which integrates the SIM_AGENT agent toolkit and the High Level Architecture (HLA) simulator interoperability framework. HLA_AGENT offers enhanced simulation scalability and allows interoperation with other HLA-compliant simulators, promoting simulation reuse. Using a simple Tileworld example, we show how HLA_AGENT can be used to flexibly distribute a SIM_AGENT simulation so as to exploit available computing resources. We present experimental results that illustrate the performance of HLA_AGENT on a Linux cluster running a distributed version of Tileworld and compare this with the original nondistributed SIM_AGENT version.

Graph embeddings have become a key and widely used technique within the field of graph mining, proving to be successful across a broad range of domains including social, citation, transportation and biological. Unsupervised graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. However, to date, there has been little work exploring exactly which topological structures are being learned in the embeddings, which could be a possible way to bring interpretability to the process. In this paper, we investigate if graph embeddings are approximating something analogous to traditional vertex-level graph features. If such a relationship can be found, it could be used to provide a theoretical insight into how graph embedding approaches function. We perform this investigation by predicting known topological features, using supervised and unsupervised methods, directly from the embedding space. If a mapping between the embeddings and topological features can be found, then we argue that the structural information encapsulated by the features is represented in the embedding space. To explore this, we present extensive experimental evaluation with five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological features. We demonstrate that several topological features are indeed being approximated in the embedding space, allowing key insight into how graph embeddings create good representations.