A Fast and Efficient Incremental Approach toward Dynamic Community Detection

Community detection is a discovery tool used by network scientists to analyze the structure of real-world networks. It seeks to identify natural divisions that may exist in the input networks that partition the vertices into coherent modules (or communities). While this problem space is rich with efficient algorithms and software, most of this literature caters to the static use-case where the underlying network does not change. However, many emerging real-world use-cases give rise to a need to incorporate dynamic graphs as inputs. In this paper, we present a fast and efficient incremental approach toward dynamic community detection. The key contribution is a generic technique called $\Delta-screening$, which examines the most recent batch of changes made to an input graph and selects a subset of vertices to reevaluate for potential community (re)assignment. This technique can be incorporated into any of the community detection methods that use modularity as its objective function for clustering. For demonstration purposes, we incorporated the technique into two well-known community detection tools. Our experiments demonstrate that our new incremental approach is able to generate performance speedups without compromising on the output quality (despite its heuristic nature). For instance, on a real-world network with 63M temporal edges (over 12 time steps), our approach was able to complete in 1056 seconds, yielding a 3x speedup over a baseline implementation. In addition to demonstrating the performance benefits, we also show how to use our approach to delineate appropriate intervals of temporal resolutions at which to analyze an input network

What makes medical students better listeners?

Diagnosing heart conditions by auscultation is an important clinical skill commonly learnt by medical students. Clinical proficiency for this skill is in decline [1], and new teaching methods are needed. Successful discrimination of heartbeat sounds is believed to benefit mainly from acoustical training [2]. From recent studies of auditory training [3,4] we hypothesized that semantic representations outside the auditory cortex contribute to diagnostic accuracy in cardiac auscultation. To test this hypothesis, we analysed auditory evoked potentials (AEPs) which were recorded from medical students while they diagnosed quadruplets of heartbeat cycles. The comparison of trials with correct (Hits) versus incorrect diagnosis (Misses) revealed a significant difference in brain activity at 280-310 ms after the onset of the second cycle within the left middle frontal gyrus (MFG) and the right prefrontal cortex. This timing and locus suggest that semantic rather than acoustic representations contribute critically to auscultation skills. Thus, teaching auscultation should emphasize the link between the heartbeat sound and its meaning. Beyond cardiac auscultation, this issue is of interest for all fields where subtle but complex perceptual differences identify items in a well-known semantic context

Exploring low-degree nodes first accelerates network exploration

We consider information diffusion on Web-like networks and how random walks can simulate it. A well-studied problem in this domain is Partial Cover Time, i.e., the calculation of the expected number of steps a random walker needs to visit a given fraction of the nodes of the network. We notice that some of the fastest solutions in fact require that nodes have perfect knowledge of the degree distribution of their neighbors, which in many practical cases is not obtainable, e.g., for privacy reasons. We thus introduce a version of the Cover problem that considers such limitations: Partial Cover Time with Budget. The budget is a limit on the number of neighbors that can be inspected for their degree; we have adapted optimal random walks strategies from the literature to operate under such budget. Our solution is called Min-degree (MD) and, essentially, it biases random walkers towards visiting peripheral areas of the network first. Extensive benchmarking on six real datasets proves that the---perhaps counter-intuitive strategy---MD strategy is in fact highly competitive wrt. state-of-the-art algorithms for cover

Sea Ice Suppression of CO2 Outgassing in the West Antarctic Peninsula: Implications For The Evolving Southern Ocean Carbon Sink

The Southern Ocean plays an important role in the uptake of atmospheric CO2. In seasonally ice-covered regions, estimates of air-sea exchange remain uncertain in part because of a lack of observations outside the summer season. Here we present new estimates of air-sea CO2 flux in the West Antarctic Peninsula (WAP) from an autonomous mooring on the continental shelf. In summer, the WAP is a sink for atmospheric CO2 followed by a slow return to atmospheric equilibrium in autumn and winter. Outgassing is almost entirely suppressed by ice cover from June through October, resulting in a modest net annual CO2 sink. Model projections indicate sea ice formation will occur later in the season in the coming decades potentially weakening the net oceanic CO2 sink. Interannual variability in the WAP is significant, highlighting the importance of sustained observations of air-sea exchange in this rapidly changing region of the Southern Ocean

Sporotrichoid Mycobacterium marinum infection in an elderly woman

We describe the case of an elderly woman who acquired a Mycobacterium marinum infection following skin exposure to the bacteria through a small wound on her right ring finger, obtained while preparing fish. The resultant sporotrichoid nodules of the right hand and the distal forearm, refractory to the initial therapy with doxycycline and rifampicin, were successfully treated with oral regimen of clarithromycin. \ua9 2015 by the article author(s)

Does Oxidative Stress Play a Role in the Pathogenesis of Urticarias

Radical oxygen species (ROS) modulate various cellular processes and are involved in physiologic and pathologic conditions, including inflammation. There is growing evidence that supports the existence of an abnormal redox status in some chronic inflammatory skin diseases, including contact dermatitis, atopic dermatitis and psoriasis. This review introduces some general aspects on the role of oxidative stress in cutaneous inflammation, with special emphasis on urticarias, summarizing recent novel findings derived from the study of physical urticarias and chronic idiopathic urticaria

Resource use of crucian carp along a lake productivity gradient is related to body size, predation risk, and resource competition

Generalist fish species can feed on a wide resource spectrum and across trophic levels depending on resource availability and trophic interactions. Crucian carp (Carassius carassius) represents a good candidate species to investigate variation in the trophic ecology of generalist fish as it can be found in highly variable fish communities and its resource use is well documented. In this study, we explored the trophic ecology of crucian carp at the individual and population levels using stable isotope and gut content analysis. We tested if trophic resource use varied according to lake productivity, predation risk, intra- and interspecific competition, or individual fish size. We found that crucian carp resource preference was highly variable among and within lakes. In predator-free lakes, small crucian carp occurred in high densities, showed increased interindividual specialisation, and relied mainly on pelagic zooplankton. In presence of predators, large crucian carp occurred in low densities and included greater proportions of benthic macroinvertebrates in their diet. This shift in resource use was further favoured in productive, shallow lakes where littoral prey were probably abundant. Resource partitioning was an important factor determining crucian carp niche use, as fish had higher trophic position in absence of other cyprinids. Crucian carp showed highly dynamic resource use and food preferences in response to variable environmental conditions. Overlooking complex diet preferences of generalist fish may lead to an oversimplification of freshwater community dynamics.Peer reviewe

Resource use of crucian carp along a lake productivity gradient is related to body size, predation risk, and resource competition

Generalist fish species can feed on a wide resource spectrum and across trophic levels depending on resource availability and trophic interactions. Crucian carp (Carassius carassius) represents a good candidate species to investigate variation in the trophic ecology of generalist fish as it can be found in highly variable fish communities and its resource use is well documented. In this study, we explored the trophic ecology of crucian carp at the individual and population levels using stable isotope and gut content analysis. We tested if trophic resource use varied according to lake productivity, predation risk, intra- and interspecific competition, or individual fish size. We found that crucian carp resource preference was highly variable among and within lakes. In predator-free lakes, small crucian carp occurred in high densities, showed increased interindividual specialisation, and relied mainly on pelagic zooplankton. In presence of predators, large crucian carp occurred in low densities and included greater proportions of benthic macroinvertebrates in their diet. This shift in resource use was further favoured in productive, shallow lakes where littoral prey was probably abundant. Resource partitioning was an important factor determining crucian carp niche use, as fish had higher trophic position in absence of other cyprinids. Crucian carp showed highly dynamic resource use and food preferences in response to variable environmental conditions. Overlooking complex diet preferences of generalist fish may lead to an oversimplification of freshwater community dynamics.publishedVersio

Disrupting resilient criminal networks through data analysis: The case of sicilian mafia

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to LawEnforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs' internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two realworld datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions' frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks