Archive for the ‘Adaptive Networks’ Category

Complex Adaptive Systems Modeling

Wednesday, January 23rd, 2013

Complex Adaptive Systems Modeling, Editor-in-Chief: Muaz A. Niazi, ISSN: 2194-3206 (electronic version)

From the webpage:

Complex Adaptive Systems Modeling is a peer-reviewed open access journal published under the brand SpringerOpen.

Complex Adaptive Systems Modeling (CASM) is a highly multidisciplinary modeling and simulation journal that serves as a unique forum for original, high-quality peer-reviewed papers with a specific interest and scope limited to agent-based and complex network-based modeling paradigms for Complex Adaptive Systems (CAS). The highly multidisciplinary scope of CASM spans any domain of CAS. Possible areas of interest range from the Life Sciences (E.g. Biological Networks and agent-based models), Ecology (E.g. Agent-based/Individual-based models), Social Sciences (Agent-based simulation, Social Network Analysis), Scientometrics (E.g. Citation Networks) to large-scale Complex Adaptive COmmunicatiOn Networks and environmentS (CACOONS) such as Wireless Sensor Networks (WSN), Body Sensor Networks, Peer-to-Peer (P2P) networks, pervasive mobile networks, service oriented architecture, smart grid and the Internet of Things.

In general, submitted papers should have the following key elements:

  • A clear focus on a specific area of CAS E.g. ecology, social sciences, large scale communication networks, biological sciences etc.)
  • Either focus on an agent-based simulation model or else a complex network model based on data from CAS (e.g. Citation networks, Gene regulatory Networks, Social networks, Ecological Networks etc.).

A new open access journal from Springer with a focus on complex adaptive systems.

Adaptive-network simulation library

Wednesday, January 23rd, 2013

Adaptive-network simulation library by Gerd Zschaler.

From the webpage:

The largenet2 library is a collection of C++ classes providing a framework for the simulation of large discrete adaptive networks. It provides data structures for an in-memory representation of directed or undirected networks, in which every node and link can have an integer-valued state.

Efficient access to (random) nodes and links as well as (random) nodes and links with a given state value is provided. A limited number of graph-theoretical measures is implemented, such as the (state-resolved) in- and out-degree distributions and the degree correlations (same-node and nearest-neighbor).

Read the tutorial here. Source code is available here.

A static topic map would not qualify as an adaptive network, but a dynamic, real time topic map system might have the characteristics of complex adaptive systems:

  • The number of elements is sufficiently large that conventional descriptions (e.g. a system of differential equations) are not only impractical, but cease to assist in understanding the system, the elements also have to interact and the interaction must be dynamic. Interactions can be physical or involve the exchange of information.
  • Such interactions are rich, i.e. any element in the system is affected by and affects several other systems.
  • The interactions are non-linear which means that small causes can have large results.
  • Interactions are primarily but not exclusively with immediate neighbours and the nature of the influence is modulated.
  • Any interaction can feed back onto itself directly or after a number of intervening stages, such feedback can vary in quality. This is known as recurrency.
  • Such systems are open and it may be difficult or impossible to define system boundaries
  • Complex systems operate under far from equilibrium conditions, there has to be a constant flow of energy to maintain the organization of the system
  • All complex systems have a history, they evolve and their past is co-responsible for their present behaviour
  • Elements in the system are ignorant of the behaviour of the system as a whole, responding only to what is available to it locally

The more dynamic the connections between networks, the closer we will move towards networks with the potential for adaptation.

That isn’t to say all networks will adapt at all or that those that do, will do it well.

Suspect adaption, like integration, is going to depend upon the amount of semantic information on hand.

You may also want to review: Largenet2: an object-oriented programming library for simulating large adaptive networks by Gerd Zschaler, and Thilo Gross. Bioinformatics (2013) 29 (2): 277-278. doi: 10.1093/bioinformatics/bts663