Community detection in graphs

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.Comment: Review article. 103 pages, 42 figures, 2 tables. Two sections expanded + minor modifications. Three figures + one table + references added. Final version published in Physics Report

Systematics and Species Delimitation in New Guinea Skink Species Complexes (Squamata: Scincidae)

Though among the most controversial topics in systematic and evolutionary biology, species are a fundamental unit in biology, and are utilized by and critical to a wide variety of studies in the life sciences. Despite this importance, little work has focused on developing and examining objective methods for species delimitation until recently. Further, New Guinea and the surrounding regions are among the most diverse and geologically complex regions globally, yet the region remains poorly explored biologically, and little work has examined the evolutionary history of the fauna in the region. To investigate the influence of factors such as sampling intensity, species richness, and phylogenetic structure on discovery methods for species delimitation, I combine simulated and empirical data. In Chapter 1, I use simulated data to examine the accuracy of three discovery methods for species delimitation under a variety of different sampling strategies. I find that genetic clustering algorithms, such as Structurama, can be highly accurate in identifying even recent divergences with limited sampling of individuals and of loci, and that Gaussian clustering can be similarly accurate, though somewhat less sensitive to detecting recent divergences. However, my results show that nonparametric delimitation is highly sensitive to errors in gene genealogy estimation, and generally fails to delimit species accurately when true coalescent gene genealogies are unknown, as in empirical applications. In Chapters 3 and 4, I apply these methods empirically to examine the species boundaries, as well as the phylogeny and other aspects of the evolutionary history of, scincid lizards of the C. bicarinata and C. fusca groups, respectively. My results in Chapter 3 indicate that species delimitation analyses may be prone to underestimating the number of species by identifying only higher levels of clustering in systems with deep phylogenetic structure. I additionally find evidence for several cryptic species in the group, including deep, species-level divergence among the populations of C. storri from Australia, the Aru Islands, and New Guinea, despite their recent connectivity via Sahul Shelf emergence during Pleistocene glaciations. Through also examining niche evolution in the group, I find evidence for niche conservatism among most species in the group, but two species, C. bicarinata and C. sp. Amau from eastern Papua New Guinea, show evidence for environmental niche divergence. Analyses of the C. fusca group in Chapter 4 provide further evidence for a tendency of discovery methods for species delimitation to under-detect species in groups with high diversity or deep phylogenetic structure. Genetic clustering algorithms based on the complete dataset only identify a small number of clusters that correspond largely to deep phylogenetic clades, but when restricted to within these clades, this method identifies clusters that correspond well to finer, putative species-level structure. I also find evidence for extensive cryptic diversity in this group, identifying 28 distinct species among my sampling of 16 currently recognized species, as well as other incongruence with current taxonomy, including synonymous species and mis-assigned populations, supporting previous evidence of the need for extensive taxonomic revision in the C. fusca group. My biogeographic analyses also providence evidence that the C. fusca group likely evolved in Australia or Australia and New Guinea before diversifying in New Guinea, dispersing at least twice across Lydekker’s line into Wallacea, and possibly also recolonizing Australia. Finally, in Chapter 5, I take a more comprehensive approach, and combine genomic and morphological data to test the validity of and examine the demographic history of two putative species of Tribolonotus from the islands of Buka and Bougainville in the northwestern Solomon Archipelogo. I use next-generation sequencing to collect a genomic dataset of several thousand loci, and apply species discovery (genetic clustering algorithms) and species validation (Bayes factor delimitations) to test for speciation between these populations. My results support this speciation event, despite the recent connectivity between these islands. I also collect a suite of morphological characters for this group and provide evidence for morphological divergence and diagnosibility. Demographic analyses applied using approximate Bayesian computation and diffusion analysis further provide evidence for a complex demographic scenario in which migration between these populations continued for some time following their initial divergence, but subsequently decreased in rate or ceased entirely. Combined, these results yield extensive insight into the utility of several methods for species delimitation, the taxonomy and systematics of Carlia and Tribolonotus in New Guinea and the surrounding regions, and the complex processes responsible for driving the generation and maintenance of the phenomenal diversity in the Sahul shelf region

Caste as Community? Networks of social affinity in a South Indian village

We examine three theories of caste and community using new data on social networks among residents of a south Indian village. The first theory treats individual caste groups as separated communities driven by the Brahmanical ideology of hierarchy based on purity and pollution. The second theory departs from the first by placing kings and landlords at the centre of rural (primeval) social structure. Here ritual giving by kings provides the glue that holds a community together by transferring inauspiciousness to gift-recipients and ensuring community welfare. The third theory, that may be treated as a corollary of the second, argues that powerful leaders in the religious and political domains act as patrons of people in their constituencies and forge a sense of community. The resulting community may be single or multi-caste. Using a community structure algorithm from social network analysis, we divide the network of the village into thirteen tight-knit clusters. We find that no cluster or community in the social network has exactly the same boundaries as a caste group in the village. Barring three exceptions, all clusters are multi-caste. Our results are most consistent with the third theory: each cluster has a patron/leader who represents the interests of his constituency at village-level fora and bridges caste and community divides.Social networks, culture, caste, social change, community development, rural India

Commissioning Perspectives for the ATLAS Pixel Detector

The ATLAS Pixel Detector, the innermost sub-detector of the ATLAS experiment at the Large Hadron Collider, CERN, is an 80 million channel silicon pixel tracking detector designed for high-precision charged particle tracking and secondary vertex reconstruction. It was installed in the ATLAS experiment and commissioning for the first proton-proton collision data taking in 2008 has begun. Due to the complex layout and limited accessibility, quality assurance measurements were continuously performed during production and assembly to ensure that no problematic components are integrated. The assembly of the detector at CERN and related quality assurance measurement results, including comparison to previous production measurements, will be presented. In order to verify that the integrated detector, its data acquisition readout chain, the ancillary services and cooling system as well as the detector control and data acquisition software perform together as expected approximately 8% of the detector system was progressively assembled as close to the final layout as possible. The so-called System Test laboratory setup was operated for several months under experiment-like environment conditions. The interplay between different detector components was studied with a focus on the performance and tunability of the optical data transmission system. Operation and optical tuning procedures were developed and qualified for the upcoming commission ing. The front-end electronics preamplifier threshold tuning and noise performance were studied and noise occupancy of the detector with low sensor bias voltages was investigated. Data taking with cosmic muons was performed to test the data acquisition and trigger system as well as the offline reconstruction and analysis software. The data quality was verified with an extended version of the pixel online monitoring package which was implemented for the ATLAS Combined Testbeam. The detector raw data of the Combined Testbeam and of the System Test cosmic run was converted for offline data analysis with the Pixel bytestream converter which was continuously extended and adapted according to the offline analysis software needs

Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics

Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18 module definitions, 129 different modularization methods, 13 module comparision methods) and 396 references. All algorithms can be downloaded from this web-site: http://www.linkgroup.hu/modules.ph

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

User hints for optimisation processes

Innovative improvements in the area of Human-Computer Interaction and User Interfaces have en-abled intuitive and effective applications for a variety of problems. On the other hand, there has also been the realization that several real-world optimization problems still cannot be totally auto-mated. Very often, user interaction is necessary for refining the optimization problem, managing the computational resources available, or validating or adjusting a computer-generated solution. This thesis investigates how humans can help optimization methods to solve such difficult prob-lems. It presents an interactive framework where users play a dynamic and important role by pro-viding hints. Hints are actions that help to insert domain knowledge, to escape from local minima, to reduce the space of solutions to be explored, or to avoid ambiguity when there is more than one optimal solution. Examples of user hints are adjustments of constraints and of an objective function, focusing automatic methods on a subproblem of higher importance, and manual changes of an ex-isting solution. User hints are given in an intuitive way through a graphical interface. Visualization tools are also included in order to inform about the state of the optimization process. We apply the User Hints framework to three combinatorial optimization problems: Graph Clus-tering, Graph Drawing and Map Labeling. Prototype systems are presented and evaluated for each problem. The results of the study indicate that optimization processes can benefit from human interaction. The main goal of this thesis is to list cases where human interaction is helpful, and provide an ar-chitecture for supporting interactive optimization. Our contributions include the general User Hints framework and particular implementations of it for each optimization problem. We also present a general process, with guidelines, for applying our framework to other optimization problems