Complex Networks on a Rock Joint

A complex network approach on a rough fracture is developed. In this manner, some hidden metric spaces (similarity measurements) between apertures profiles are set up and a general evolutionary network in two directions (in parallel and perpendicular to the shear direction) is constructed. Also, an algorithm (COmplex Networks on Apertures: CONA) is proposed in which evolving of a network is accomplished using preferential detachments and attachments of edges (based on a competition and game manner) while the number of nodes is fixed. Also, evolving of clustering coefficients and number of edges display similar patterns as well as are appeared in shear stress, hydraulic conductivity and dilation changes, which can be engaged to estimate shear strength distribution of asperities.Comment: ROCKENG09: Proceedings of the 3rd CANUS Rock Mechanics Symposium, Toronto, May 2009 (Ed: M.Diederichs and G. Grasselli

Performance Analysis of Genetic Algorithm with PSO for Data Clustering

Data clustering is widely used in several areas like machine learning, data mining, pattern recognition, image processing and bioinformatics. Clustering is the process of partitioning or grouping of a given set of data into disjoint cluster. Basically there are two types of clustering approaches, one is hierarchical and the other is partitioned. K-means clustering is one of the partitioned types and it suffers from the fact that that it may not be easy to clearly identify the initial K elements. To overcome the problems in K-means Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques came into existence. A Genetic Algorithm (GA) is one of hierarchical approach and can be noted as an optimization technique whose algorithm is based on the mechanics of natural selection and genetics. Particle Swarm Optimization (PSO) is also one of the hierarchical search methods whose mechanics are inspired by the swarming. The PSO algorithm is simple and can be developed in a few lines of code whereas GAs suffers from identifying a current solution but good at reaching a global region. Even though GA and PSO have their own set of strengths they have weaknesses too. So a hybrid approach (GA-PSO) which combines the advantages of GA and PSO are proposed to get a better performance. The hybrid method merges the standard velocity and modernizes rules of PSOs with the thoughts of selection, crossover and mutation from GAs. A comparative study is carried out by analyzing the results like fitness value and elapsed time of GA-PSO to the standard GA and PSO

Community Detection in Quantum Complex Networks

Determining community structure is a central topic in the study of complex networks, be it technological, social, biological or chemical, in static or interacting systems. In this paper, we extend the concept of community detection from classical to quantum systems---a crucial missing component of a theory of complex networks based on quantum mechanics. We demonstrate that certain quantum mechanical effects cannot be captured using current classical complex network tools and provide new methods that overcome these problems. Our approaches are based on defining closeness measures between nodes, and then maximizing modularity with hierarchical clustering. Our closeness functions are based on quantum transport probability and state fidelity, two important quantities in quantum information theory. To illustrate the effectiveness of our approach in detecting community structure in quantum systems, we provide several examples, including a naturally occurring light-harvesting complex, LHCII. The prediction of our simplest algorithm, semiclassical in nature, mostly agrees with a proposed partitioning for the LHCII found in quantum chemistry literature, whereas our fully quantum treatment of the problem uncovers a new, consistent, and appropriately quantum community structure.Comment: 16 pages, 4 figures, 1 tabl

A similarity-based community detection method with multiple prototype representation

Communities are of great importance for understanding graph structures in social networks. Some existing community detection algorithms use a single prototype to represent each group. In real applications, this may not adequately model the different types of communities and hence limits the clustering performance on social networks. To address this problem, a Similarity-based Multi-Prototype (SMP) community detection approach is proposed in this paper. In SMP, vertices in each community carry various weights to describe their degree of representativeness. This mechanism enables each community to be represented by more than one node. The centrality of nodes is used to calculate prototype weights, while similarity is utilized to guide us to partitioning the graph. Experimental results on computer generated and real-world networks clearly show that SMP performs well for detecting communities. Moreover, the method could provide richer information for the inner structure of the detected communities with the help of prototype weights compared with the existing community detection models