Nowadays, networks are almost ubiquitous. In the past decade, community detection received an increasing interest as a way to uncover the structure of networks by grouping nodes into communities more densely connected internally than externally. Yet most of the effective methods available do not consider the potential levels of organisation, or scales, a network may encompass and are therefore limited. In this paper we present a method compatible with global and local criteria that enables fast multi-scale community detection on large networks. The method is derived in two algorithms, one for each type of criterion, and implemented with 6 known criteria. Uncovering communities at various scales is a computationally expensive task. Therefore this work puts a strong emphasis on the reduction of computational complexity. Some heuristics are introduced for speed-up purposes. Experiments demonstrate the efficiency and accuracy of our method with respect to each algorithm and criterion by testing them against large generated multi-scale networks. This study also offers a comparison between criteria and between the global and local approaches. In particular our results suggest that global criteria seem to be more robust to noise and thus more accurate than local criteria.
Many real systems can be represented as networks whose analysis can be very informative regarding the original system's organisation. In the past decade community detection received a lot of attention and is now a very active field of research. Recently stability was introduced as a new measure for partition quality. This work investigates stability as an optimisation criterion that exploits a Markov process view of networks to enable multi-scale community detection. Several heuristics and variations of an algorithm optimising stability are presented as well as an application to overlapping communities. Experiments show that the method enables accurate multi-scale network analysis.
Computation in biology and in conventional computer architectures seem to share some features, yet many of their important characteristics are very different. To address this, [1] introduced systemic computation, a model of interacting systems with natural characteristics. Following this work, here we introduce the first platform implementing such computation, including programming language, compiler and virtual machine. To investigate their use we then provide an implementation of a genetic algorithm applied to the travelling salesman problem and also explore how SC enables self-adaptation with the minimum of additional code.
Abstract. Previous work suggests that innate immunity and representations of tissue can be useful when combined with artificial immune systems. Here we provide a new implementation of tissue for AIS using systemic computation, a new model of computation and corresponding computer architecture based on a systemics world-view and supplemented by the incorporation of natural characteristics. We show using systemic computation how to create an artificial organism, a program with metabolism that eats data, expels waste, clusters cells based on the nature of its food and emits danger signals suitable for an artificial immune system. The implementation is tested by application to a standard machine learning set and shows excellent abilities to recognise anomalies in its diet.
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