A General Optimization Technique for High Quality Community Detection in Complex Networks

Recent years have witnessed the development of a large body of algorithms for community detection in complex networks. Most of them are based upon the optimization of objective functions, among which modularity is the most common, though a number of alternatives have been suggested in the scientific literature. We present here an effective general search strategy for the optimization of various objective functions for community detection purposes. When applied to modularity, on both real-world and synthetic networks, our search strategy substantially outperforms the best existing algorithms in terms of final scores of the objective function; for description length, its performance is on par with the original Infomap algorithm. The execution time of our algorithm is on par with non-greedy alternatives present in literature, and networks of up to 10,000 nodes can be analyzed in time spans ranging from minutes to a few hours on average workstations, making our approach readily applicable to tasks which require the quality of partitioning to be as high as possible, and are not limited by strict time constraints. Finally, based on the most effective of the available optimization techniques, we compare the performance of modularity and code length as objective functions, in terms of the quality of the partitions one can achieve by optimizing them. To this end, we evaluated the ability of each objective function to reconstruct the underlying structure of a large set of synthetic and real-world networks.Comment: MAIN text: 14 pages, 4 figures, 1 table Supplementary information: 19 pages, 8 figures, 5 table

Fast stable direct fitting and smoothness selection for Generalized Additive Models

Existing computationally efficient methods for penalized likelihood GAM fitting employ iterative smoothness selection on working linear models (or working mixed models). Such schemes fail to converge for a non-negligible proportion of models, with failure being particularly frequent in the presence of concurvity. If smoothness selection is performed by optimizing `whole model' criteria these problems disappear, but until now attempts to do this have employed finite difference based optimization schemes which are computationally inefficient, and can suffer from false convergence. This paper develops the first computationally efficient method for direct GAM smoothness selection. It is highly stable, but by careful structuring achieves a computational efficiency that leads, in simulations, to lower mean computation times than the schemes based on working-model smoothness selection. The method also offers a reliable way of fitting generalized additive mixed models

Geo-additive models of Childhood Undernutrition in three Sub-Saharan African Countries

We investigate the geographical and socioeconomic determinants of childhood undernutrition in Malawi, Tanzania and Zambia, three neighboring countries in Southern Africa using the 1992 Demographic and Health Surveys. We estimate models of undernutrition jointly for the three countries to explore regional patterns of undernutrition that transcend boundaries, while allowing for country-specific interactions. We use semiparametric models to flexibly model the effects of selected so-cioeconomic covariates and spatial effects. Our spatial analysis is based on a flexible geo-additive model using the district as the geographic unit of anal-ysis, which allows to separate smooth structured spatial effects from random effect. Inference is fully Bayesian and uses recent Markov chain Monte Carlo techniques. While the socioeconomic determinants generally confirm what is known in the literature, we find distinct residual spatial patterns that are not explained by the socioeconomic determinants. In particular, there appears to be a belt run-ning from Southern Tanzania to Northeastern Zambia which exhibits much worse undernutrition, even after controlling for socioeconomic effects. These effects do transcend borders between the countries, but to a varying degree. These findings have important implications for targeting policy as well as the search for left-out variables that might account for these residual spatial patterns

Cephalopods in the north-eastern Atlantic: Species, biogeography, ecology, exploitation and conservation

Cephalopods play a significant role in coastal and oceanic ecosystems, both as consumers of invertebrates and small fish and as the prey of some fish, seabirds and marine mammals and other large predators. Approximately 30 species of cephalopod have been recorded in the north-eastern Atlantic and adjacent waters, including 18 teuthid (squid), seven sepiolid (bobtail), three sepiid (cuttlefish) and 10 octopod (octopus) species. A number of these are exploited commercially and support important target and by-catch fisheries in Western Europe. During the past decade, annual landings of cephalopods from the north-eastern Atlantic (International Council for the Exploration of the Sea [ICES] area) have ranged from 40,000 to 55,000 t, including substantial catches of long-fin (loliginid) squid (7000–11,000 t per annum), short-fin (ommastrephid) squid (3000–10,000 t), cuttlefish (including sepiolids; 16,000–24,000 t) and octopods (12,000–18,000 t). The most important exploited species in the north-eastern Atlantic are Eledone cirrhosa, Illex coindetii, Loligo forbesi, Loligo vulgaris, Octopus vulgaris, Todarodes sagittatus, Todaropsis eblanae and Sepia officinalis. Other species including Alloteuthis subulata, Gonatus fabricii and certain sepiolids, appear to be abundant and may be marketable. Cephalopods tend to rapidly concentrate heavy metals and other toxic substances in their tissues and this plays an important role in the bioaccumulation of these pollutants in marine predators as well as having implications for human consumption. High levels of cadmium and mercury are often recorded in cephalopod tissues. Another important environmental issue concerns the potential impact of widespread human activity on cephalopod spawning areas, particularly bottom-fishing operations but also shipping, and oil exploration and production. In contrast to many finfish species that spawn annually over a number of years, most cephalopods live only 1–2 yr and die after spawning. Therefore, failure to reproduce and recruit adequately in any given year may seriously impact the long-term viability of cephalopod stocks. Climate change is expected to have a significant effect on many species in the north-eastern Atlantic. This review provides a detailed account of the zoogeography, biology and ecology of cephalopods in the north-eastern Atlantic, on a species-by-species basis. Important economic, ecological and conservation issues affecting cephalopods in this area are also discusse

Vestiges of the proto-Caribbean seaway: origin of the San Souci Volcanic Group, Trinidad

Outcrops of volcanic–hypabyssal rocks in Trinidad document the opening of the proto-Caribbean seaway during Jurassic–Cretaceous break-up of the Americas. The San Souci Group on the northern coast of Trinidad comprises the San Souci Volcanic Formation (SSVF) and passive margin sediments of the ~ 130–125 Ma Toco Formation. The Group was trapped at the leading edge of the Pacific-derived Caribbean Plate during the Cretaceous–Palaeogene, colliding with the para-autochthonous margin of Trinidad during the Oligocene–Miocene. In-situ U–Pb ion probe dating of micro-zircons from a mafic volcanic breccia reveal the SSVF crystallised at 135.0 ± 7.3 Ma. The age of the SSVF is within error of the age of the Toco Formation. Assuming a conformable contact, geodynamic models indicate a likely origin for the SSVF on the passive margin close to the northern tip of South America. Immobile element and Nd–Hf radiogenic isotope signatures of the mafic rocks indicate the SSVF was formed by ≪10% partial melting of a heterogeneous spinel peridotite source with no subduction or continental lithospheric mantle component. Felsic breccias within the SSVF are more enriched in incompatible elements, with isotope signatures that are less radiogenic than the mafic rocks of the SSVF. The felsic rocks may be derived from re-melting of mafic crust. Although geochemical comparisons are drawn here with proto-Caribbean igneous outcrops in Venezuela and elsewhere in the Caribbean more work is needed to elucidate the development of the proto-Caribbean seaway and its rifted margins. In particular, ion probe dating of micro-zircons may yield valuable insights into magmatism and metamorphism in the Caribbean, and in altered basaltic terranes more generally

Forecasting Player Behavioral Data and Simulating in-Game Events

Understanding player behavior is fundamental in game data science. Video games evolve as players interact with the game, so being able to foresee player experience would help to ensure a successful game development. In particular, game developers need to evaluate beforehand the impact of in-game events. Simulation optimization of these events is crucial to increase player engagement and maximize monetization. We present an experimental analysis of several methods to forecast game-related variables, with two main aims: to obtain accurate predictions of in-app purchases and playtime in an operational production environment, and to perform simulations of in-game events in order to maximize sales and playtime. Our ultimate purpose is to take a step towards the data-driven development of games. The results suggest that, even though the performance of traditional approaches such as ARIMA is still better, the outcomes of state-of-the-art techniques like deep learning are promising. Deep learning comes up as a well-suited general model that could be used to forecast a variety of time series with different dynamic behaviors