Graph Analysis Methods Research Articles

Cortical hubs form a module for multisensory integration on top of the hierarchy of cortical networks

Sensory stimuli entering the nervous system follow particular paths of processing, typically separated (segregated) from the paths of other modal information. However, sensory perception, awareness and cognition emerge from the combination of information (integration). The corticocortical networks of cats and macaque monkeys display three prominent characteristics: (i) modular organisation (facilitating the segregation), (ii) abundant alternative processing paths and (iii) the presence of highly connected hubs. Here, we study in detail the organisation and potential function of the cortical hubs by graph analysis and information theoretical methods. We find that the cortical hubs form a spatially delocalised, but topologically central module with the capacity to integrate multisensory information in a collaborative manner. With this, we resolve the underlying anatomical substrate that supports the simultaneous capacity of the cortex to segregate and to integrate multisensory information.

A STRUCTURAL APPROACH TO THE REPRESENTATION OF LIFE HISTORY DATA1

We propose a structural approach to the representation of life history data, based on an interval graph formalism. Various “life history graphs” are defined using this approach, and properties of such graphs are related to life course concepts. The analysis of life history graphs using standard network methods is illustrated, and suggestions are made regarding the use of network analysis for life course research. A duality is demonstrated between individual life history graphs and social networks, and connections between life history graph analysis and conventional life course methods are discussed.

Novel graph model and analysis method for piezoelectric thickness-drive transducers

This study develops a new graph model and analysis scheme for representing and analyzing a piezoelectric thickness-drive transducer. The proposed TWSF graph model, expressed via electrical and acoustical variables instead of electromechanical analogy, simplifies understanding of the physical mechanism of the transducer. The primary advantage of the graph model analogy is that performance characteristics, such as the electrical and mechanical impedance, and the force and velocity responses of the transducer system can be calculated directly using the topology scheme based on the graph model. The dynamic equations do not need to be formulated according to the analogy model for design and analysis. The proposed method also does not require computation schemes to solve the equations for obtaining the characteristics and the responses. Compared to the classical method, the present graph and analysis scheme offers a more efficient technology to obtain precise results with less analysis. Moreover, the derived results are expressed as both analytical and closed-form formulas, making them convenient to apply in analysis and design.

Preliminary estimation of parameters of beta distributions as statistical models of measurement series

Graph-analytic methods based on quantiles and moments are provided for preliminary estimation of parameters of beta-distributions as statistical models of measurement series.

A general algorithm to determine the topological equivalence of optical interconnection networks

Optical interconnection networks have potential uses in photonic switching systems and parallel processing computers. This paper presents a general algorithm, the so-called graph analysis method (GAM), to determine the topological equivalence of optical interconnection networks. Experimental results of an optical implementation of a 64×64-arrays crossover interconnections are also reported.

A Survey of Statistical Methods for Graph Analysis

Sociometric problems involving empirical sociograms and more general networks have been one of the major sources of an increasing interest in statistical graph models. The well-known book by Harary, Norman, and Cartwright (1965) has contributed much to the mathematical modeling of social networks, and we now find numerous methodological articles appearing in the literature-for instance, in the Journal of Mathematical Sociology, Social Networks, and other professional journals. Nonetheless, while statistical testing and estimation problems are discussed in some reports, statistical issues are all too often ignored or treated in a very elementary way. Only recently have statistical models and methods been developed that begin to meet the need for proper handling of sampling variation, measurement errors, and other kinds of uncertainty in network data.

A graph analysis of the relationship between population density and social pathology

This article deals with systematic variables--population density and pathological effects--at the level of a community or organizational system. Existing studies of the relationship between population density and social pathology (e.g., mortality, mental illness) in humans have failed to determine a strictly causal relationship between density and pathology in a linear model. They have not established a direct link between density and pathology in a simple two-variable, linear causal approach. Eliminating other variables by statistical control has obscured the conditions of a complex phenomenon. By incorporating socioeconomic variables into a general system model, this paper suggests that the total configuration of social organization, adaptation, previous group experience, and environment determine the effects of population density. Using a method of graph analysis, a model is presented of the relationship between population density and social pathology which has a high degree of isomorphism with the empirical situation.