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We present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

openaire: EC/H2020/871042/EU//SoBigData-PlusPlus In an earlier study one of us had developed a model of influence spreading for analysing human behaviour and interaction with others in a social network. Here we apply this model and corresponding influence centrality measures to real data of mobile phone call detail records. From this we get structures of human ego-centric networks and use a simple model, based on the number of phone calls, to describe the strengths of social relationships. To analyse 48,000 egos in their ego-centric networks we define normalised out-centrality and in-centrality influence measures, by dividing with out-degree and in-degree, respectively. With these and the betweenness centrality measures, we analyse the influence spreading in the ego-centric networks under different scenarios of link strengths between individuals reflecting the network structure being either interaction or connectivity oriented. The model reveals characteristics of social behaviour that are not obvious from the data analysis of raw empirical data or from theresults of standard centrality measures. A transition is discovered in behaviour from young to older age groups for both genders and in both normalised out-centrality and in-centrality as well as betweenness centrality results.

Modern military organisations have become more cooperation-oriented rather than abide by traditional, leadership-oriented, hierarchical structures. ‘Teams of teams’ have proven effective as organisational structures in actual battlefield situations. Additionally, future battles rely more and more on utilising civilian technology and expert personnel. We present mathematical methods for modelling modern military organisations that are based on teams and networking between team members when performing military tasks or operations. Social network analysis is a novel approach to study relationships between leadership and sub-ordinate roles. We present interactions between units in an organisation with bidirectional relationships, which enables us to illustrate both outward and inward influence of the units. The method is demonstrated with different model parameter values that describe both modern and traditional military organisations.

Our goal is to get better understanding of different kind of dependencies behind the high-level capability areas. The models are suitable for investigating present state capabilities or future developments of capabilities in the context of technology forecasting. Three levels are necessary for a model describing effects of technologies on military capabilities. These levels are capability areas, systems and technologies. The contribution of this paper is to present one possible model for interdependencies between technologies. Modelling interdependencies between technologies is the last building block in constructing a quantitative model for technological forecasting including necessary levels of abstraction. This study supplements our previous research and as a result we present a model for the whole process of capability modelling. As in our earlier studies, capability is defined as the probability of a successful task or operation or proper functioning of a system. In order to obtain numerical data to demonstrate our model, we conducted a questionnaire to a group of defence technology researchers where interdependencies between seven representative technologies were inquired. Because of a small number of participants in questionnaires and general uncertainties concerning subjective evaluations, only rough conclusions can be made from the numerical results.

A dynamic influence spreading model is presented for computing network centrality and betweenness measures. Network topology, and possible directed connections and unequal weights of nodes and links, are essential features of the model. The same influence spreading model is used for community detection in social networks and for analysis of network structures. Weaker connections give rise to more sub-communities whereas stronger ties increase the cohesion of a community. The validity of the method is demonstrated with different social networks. Our model takes into account different paths between nodes in the network structure. The dependency of different paths having common links at the beginning of their paths makes the model more realistic compared to classical structural, simulation and random walk models. The influence of all nodes in a network has not been satisfactorily understood. Existing models may underestimate the spreading power of interconnected peripheral nodes as initiators of dynamic processes in social, biological and technical networks.

Al Qaeda’s network structure before the tragic events of 9/11/2001 is studied using a method of social network analysis. The method is based on a modeling framework to assess the influence of a node in a complex network with respect to spreading information via different paths between source and target nodes. The same framework is used consistently to compute closeness and betweenness centrality measures as well as to detect subcommunities. Centrality measures taking into account all possible paths between source and target nodes, not just the shortest paths, are useful in modeling resilience of covert networks. Along these lines, new versions of node and link betweenness centrality measures are proposed.

We introduce a method for modeling the impact of future technologies and systems on capability areas. In this study, we define capability as the probability of a successful operation. The effects of technology developments are forecasted for three military capability areas (protection, situational awareness and engagement). Expert opinions from a questionnaire were used as capability estimates for the capability areas. Some general results can be obtained from capability areas data, for example, the common-known S-curve development phases can be recognized. The proposed model is based on the idea of system of systems, and the usability of the modeling method is demonstrated with two systems (satellites and UAVs) and three scenarios. We show how to calculate the impact of a system on capability areas by decomposing the capability areas into system level capabilities. The results of the system level model are interpreted with examples of relationships between scenario descriptions and calculated system capability values. By changing system resources, e.g. training, personnel or number of systems, system capability values are changing accordingly. In this way, the model can be regarded as a tool for capability management.

The concept of military capabilities is often used in strategic planning of defense. This study describes an example of scenario-based evaluation of military capability areas using alternative systems. The study concentrates on three capability areas: protection, awareness and engagement. Evaluations of new systems in realistic but future-oriented scenarios may reveal new possibilities to utilize collaboration of different systems or to replace existing systems with new ones. The study indicates how the combination of UAVs and satellites is the most prominent system compared to UAV or satellite systems to enhance protection, engagement and awareness capability, especially in the 10-year span. Technology development may reveal unexpected synergies in the utilization of the combination of these two systems. Further work will focus on the application of the methodology in other areas and on the collection of data to analyze the effect of the technology development to the capability areas.

Earlier work describing NN annihilation in terms of three-meson intermediate states with the quark rearrangement model is now extended to include one- and two-meson states. The latter are incorporated using the 13 P 0 quark-antiquark annihilation mechanism. In addition an estimate is made of the strength of the rearrangement interaction, and also the effect of treating the annihilation into two mesons as a two-step process.