Recent advances in clustering methods for protein interaction networks

The increasing availability of large-scale protein-protein interaction data has made it possible to understand the basic components and organization of cell machinery from the network level. The arising challenge is how to analyze such complex interacting data to reveal the principles of cellular organization, processes and functions. Many studies have shown that clustering protein interaction network is an effective approach for identifying protein complexes or functional modules, which has become a major research topic in systems biology. In this review, recent advances in clustering methods for protein interaction networks will be presented in detail. The predictions of protein functions and interactions based on modules will be covered. Finally, the performance of different clustering methods will be compared and the directions for future research will be discussed

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

Overlapping community search in very large graphs

"In this master thesis we present a novel approach to finding communities in large graphs. Our method finds the overlapped and hierarchical structure of communities efficiently, outperforming previous proposals. We propose a new objective function that allows to evaluate the quality of a community naturally including nodes shared by other communities. This is achieved by implicitly mapping the nodes of the graph in a vectorial space, using as a basis a construction presented by Lóvasz in 1979. We present and analyse several algorithms to decompose a given graph into a set of not necessarily disjoint neighborhoods. This has applications for analysing and summarizing the large-scale structure of complex networks.

Resilience in Transportation Networks

The functionality of transportation networks is greatly challenged by risk factors such as increasing climate-related hazards, rising population exposure, and greater city vulnerability. Inevitably, the transportation network cannot withstand the impact of an overwhelming disaster, which results in rapid declines in the performance of road net-work. As a next step, the authorities need to restore the performance of the road net-work to an acceptable state as soon as possible and rebalance the conflict between the capacity of the road network and travel demand. Resilience is defined as the process of system performance degradation followed by recovery. To improve the transportation network resilience and maintain regular traffic, it is crucial to identify which factors are related to the resilience and investigate how these factors impact resilience. In this thesis, four factors, i.e., road networks, evacuees, disruption types and au-thorities, are identified to analyze resilience mechanisms. Firstly, the change in vehicle speed during a disaster is used as a measure of resilience, and we analyze the quantita-tive relationship between resilience and the structural characteristics and properties of the road network in multiple disruptions in multiple cities. The results show that the connectivity of the road network, the predictability of disruption, and the population density affect the resilience of the road network in different ways. Secondly, as the road connectivity plays a crucial role during the evacuation pe-riod and considering more frequent and extensive bushfires, we explore a practical and challenging problem: are bushfire fatalities related to road network characteristics? Con-nectivity index (CI), a composite metric that takes into account redundancy, connectivi-ty, and population exposure is designed. The statistical analysis of real-world data sug-gests that CI is significantly negatively correlated with historical bushfire fatalities. This parsimonious and simple graph-theoretic measure can provide planners a useful metric to reduce vulnerability and increase resilience among areas that are prone to bushfires. Finally, a modelling framework for optimizing road network pre-disaster invest-ment strategy under different disaster damage levels is proposed. A bi-level multi-objective optimization model is formulated, in which the upper-level aims to maximize the capacity-based functionality and robustness of the road network, and the lower-level is the user equilibrium problem. To efficiently solve the model, the Shapley value is used to select candidate edges and obtain a near-optimal project order. For more reality, the heterogeneity of road segments to hazards and the correlation of road segments in dif-ferent hazard phases are considered. Realistic speed data is used to explore the depend-ency between different disaster states with copula functions. The numerical results illus-trate that the investment strategy is significantly influenced by the road edge character-istics and the level of disaster damage. Critical sections that can significantly improve the overall functionality of the network are identified. Overall, the core contribution of this thesis is to provide insights into the evalua-tion and analysis of resilience in transportation networks, as well as develop modelling frameworks to promote resilience. The results of this work can provide a theoretical ba-sis for road network design, pre-disaster investment and post-disaster emergency rescue

Uncovering the Structures In Ecological Networks: Multiple Techniques For Multiple Purposes

Ecosystem structure and function are the product of biological and ecological elements and their connections and interactions. Understanding structure and process in ecosystems is critical to ecological studies. Ecological networks, based on simple concepts in which biological and ecological elements are depicted as nodes with relationships between them described as links, have been recognized as a valuable means of clarifying the relationship between structures and process in ecosystems. Ecological network analysis has benefited from the advancement of techniques in social science, computer science, and mathematics, but attention must be paid to whether the designs of these techniques follow ecological principles and produce results that are ecologically meaningful and interpretable. The objective of this dissertation is to examine the suitability of these methods for various applications addressing different ecological concerns. Specifically, the studies that comprise this dissertation test methods that reveal the structure of various ecological networks by decomposing networks of interest into groups of nodes or aggregating nodes into groups. The key findings in each specific application are summarized below. In the first paper, REgionalization with Clustering And Partitioning (GraphRECAP) (Guo 2009) and Girvan and Newman\u27s method (Girvan and Newman 2002) were compared in the study of finding compartments in the habitat network of ring-tailed lemurs (Lemur catta). The compartments are groups of nodes in which lemur movements are more prevalent among the groups than across the groups. GraphRECAP found compartments with a larger minimum number of habitat patches in compartments. These compartments are considered to be more robust to local extinctions because they had stronger within-compartment dispersal, greater traversability, and more alternative routes for escape from disturbance. The potential defect of the Girvan and Newman\u27s method, an unbalanced partitioning of graphs under certain circumstances, was believed to account for its lower performance. In the second study, Modularity based Hierarchical Region Discovery (MHRD) and Edge ratio-based Hierarchical Region Discovery (EHRD) were used to detect movement patterns in trajectories of 34 cattle (Bos taurus), 30 mule deer (Odocoileus hemionus), and 38 elk (Cervus elaphus) tracked by an Automated Telemetry at Starkey National Forest, in northeastern Oregon, USA. Both methods treated animal trajectories as a spatial and ecological graph, regionalized the graph such that animals have more movement within the regions than across the regions, and then investigated the movement patterns on the basis of regions. EHRD identified regions that more effectively captured the characteristics of different species movement than MHRD. Clusters of trajectories identified by EHRD had higher cohesion within clusters and better separation between clusters on the basis of attributes of trajectories extracted from the regions. The regions detected by EHRD also served as more effective predictors for classifying trajectories of different species, achieving a higher classification accuracy with more simplicity. EHRD had better performance, because it did not rely on the null model that MHRD compared to, but invalid in this application. In the third study, a proposed Extended Additive Jaccard Similarity index (EAJS) overcame the weakness of the Additive Jaccard Similarity index (AJS) (Yodzis and Winemiller 1999) in the aggregation of species for the mammalian food web in the Serengeti ecosystem. As compared to AJS, the use of the EAJS captured the similarity between species that have equivalent trophic roles. Clusters grouped using EAJS showed higher trophic similarities between species within clusters and stronger separation between species across clusters as compared to AJS. The EAJS clusters also exhibited patterns related to habitat structure of plants and network topology associated with animal weights. The consideration of species feeding relations at a broader scale (i.e., not limited in adjacent trophic levels) accounted for the advantages of EAJS over AJS. The concluding chapter summarizes how the methods examined in the previous chapters perform in different ecological applications and examines the designs of these algorithms and whether the designs make ecological sense. It then provides valuable suggestions on the selections of methods to answer different ecological questions in practice and on the development and improvement of more ecological-oriented techniques

Telling sexual auto-ethnography: (fictional) stories of the (homo)sexual in social science

The dissertation is an autoethnographic exploration of some of the meanings available, from within a contemporary British urban context, in naming and locating male same-sex genital relations (Moran, 1996). In particular, the dissertation analyses some of the dynamics at stake in locating male samesex genital relations under the sign ‘gay’. An argument is made for the pervasiveness of this nomenclature in contemporary liberal western contexts in describing male same-sex desire/attraction/activity and, concomitantly, what might be lost in consigning male same-sex sexuality thus. Autoethnography is adopted as a methodological approach in (re)tracing some elements of my biography in order to disrupt the potentially assimilationist impulse attaching to ‘gay’ as a way of normativising male same-sex relations. I adopt this approach given the uneases by which I recognise my own same-sex sexual proclivities as fitting (or not) within the homonormative (Duggan, 2004) excesses of ‘gay’. The autoethnographic approach allows me to reflect on previous experience as a means of que(e)r(y)ing the seeming ease with which ‘gay’ might be seen as accounting for all those who labour under its sign. In particular, I explore (my) Irishness, (my) queered relation to gender, (my) in/disciplined engagements with psychology, (my) Class location and (my) early childhood sexuality in an attempt to explore how these might locate me more queerly in a contemporary socios that has a tendency to render (me as a) males with same-sex inclinations as identifiable and knowable. Alongside this autoethnographic work I explore how writing creative fictions might complement/supplement the impulse to queer ‘gay’. This aspect of the work is borne out of an interest in how Humanities-inspired academic discourses might be brought to bear in bending those Social Science discourses through which I became academic and through which I have come to understand (my) (homo)sexuality. Ultimately, the dissertation is an attempt to find a writing voice that speaks to and for the multiply queered (dis)locations that I have become subject to in ‘becoming’ (academic). It is an attempt to (re)write (my) (homo)sexuality into social science discourse without recourse to those discursive frames that tolerate and/or pathologise. This is my journey into doctoring myself