Community detection in multiplex networks using locally adaptive random walks

Multiplex networks, a special type of multilayer networks, are increasingly applied in many domains ranging from social media analytics to biology. A common task in these applications concerns the detection of community structures. Many existing algorithms for community detection in multiplexes attempt to detect communities which are shared by all layers. In this article we propose a community detection algorithm, LART (Locally Adaptive Random Transitions), for the detection of communities that are shared by either some or all the layers in the multiplex. The algorithm is based on a random walk on the multiplex, and the transition probabilities defining the random walk are allowed to depend on the local topological similarity between layers at any given node so as to facilitate the exploration of communities across layers. Based on this random walk, a node dissimilarity measure is derived and nodes are clustered based on this distance in a hierarchical fashion. We present experimental results using networks simulated under various scenarios to showcase the performance of LART in comparison to related community detection algorithms

The voices of youth in envisioning positive futures for nature and people

The unpredictable Anthropocene poses the challenge of imagining a radically different, equitable and sustainable world. Looking 100 years ahead is not easy, and especially as millennials, it appears quite bleak. This paper is the outcome of a visioning exercise carried out in a 2-day workshop, attended by 33 young early career professionals under the auspices of IPBES. The process used Nature Futures Framework in an adapted visioning method from the Seeds of Good Anthropocene project. Four groups envisioned more desirable future worlds; where humanity has organised itself, the economy, politics and technology, to achieve improved nature-human well-being. The four visions had differing conceptualisations of this future. However, there were interesting commonalities in their leverage points for transformative change, including an emphasis on community, fundamentally different economic systems based on sharing and technological solutions to foster sustainability and human-nature connectedness. Debates included questioning the possibility of maintaining local biocultural diversity with increased connectivity globally and the prominence of technology for sustainability outcomes. These visions are the first step towards a wider galvanisation of youth visions for a brighter future, which is often missing in the arena where it can be taken seriously, to trigger more transformative pathways towards meeting global goals

Science and Technology Studies in the energy-water nexus : a naturalistic inquiry of reclaimed water use in thermoelectric power plants

Energy is necessary to transport, treat, pump, convey, cool, and heat water such that it is available at the appropriate time, place, temperature and salinity for an array of human uses. Water is required to produce and extract fuel sources such as oil and gas, and it is used in the cooling systems of power plant operations as they generate electricity. This dissertation examines the interrelationships between these resources, also known as the Energy-Water Nexus, and the associated actors, technologies, environments, and policies that affect them. While there are many interrelated system boundaries to this relationship that are critical to society—such as food, sanitation, and carbon footprint—I focus on large-scale solutions that can make a significant difference in efficient use of energy and water. Specifically, this study is focused on the use of water in thermoelectric power plants and investigates which factors lead decision-makers toward using reclaimed water rather than the traditionally used freshwater. Important quantitative studies have addressed feasibility, costs, logistics, and policy developments related to the use of reclaimed water for cooling, but these studies leave a substantial gap in qualitative understanding of the sociopolitical influences on this transition. To support a growing understanding of using reclaimed water as an alternative, this research design is guided by methods developed in Science and Technology Studies (STS), a field of study that recognizes the complicated and continuously evolving nature of energy and water use. The research began with an Interactive Qualitative Analysis (IQA) of utility company relationships within the ecosociotechnical infrastructure in the state of Texas. This method was followed by and completed with Naturalistic Inquiry, which is well-suited for this research because of the complex and dynamic nature of the topic under study. This approach is especially important to the energy-water nexus as the units of analysis include not only policies, climates, and social pressures, but also the changing relationships between them. Where possible, diagrams have been created to visually aid interpretation and indicate connections between scenarios and solutions. The goal of this research was to: (1) understand the variables that influence the decision-makers in the process of shifting to reclaimed water use, (2) understand how these variables relate to each other, and (3) use that understanding to articulate how to support a dynamic and adaptive framework for continual evaluation of electricity generation and water resource alternatives, and to identify the factors that influence both theory and practice in energy and water planning.Community and Regional Plannin

Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

Mediated commemoration, affect alienation, and why we are not all Charlie: solidarity symbols as vehicles for stancetaking

Public mourning and collective displays of solidarity after terrorist violence are established cultural practices that bring people together at times of tragedy and loss. While it remains common to gather at the site of tragedy, to construct temporary memorials of candles and flowers in memory of the victims and to come together as community, mediated practices of commemoration have become equally important. Sharing solidarity symbols facilitating connective participation is one of the most prevalent and visible ways of joining in public mourning in digital spaces. One of the most popular solidarity symbols to date is #JeSuisCharlie, created after the Charlie Hebdo attack in Paris, 2015. It has since inspired numerous renditions, including #JeSuisMuslim that emerged after the Christchurch mosque attacks in March, 2019. This media-ethnographic study focuses on solidarity symbols circulating on Twitter after four terrorist attacks: Paris in January, 2015, and again in November, Beirut in November, 2015, and Christchurch in March, 2019. The study draws on Appraisal analysis to examine the interpersonal dimension of solidarity symbols, specifically, how stance as interpersonal orientation is constructed in solidarity symbols. When the normative reading of solidarity symbols as vehicles for alignment and solidarity is interrupted, they are experienced as alienating or excluding. Approaching solidarity symbols as vehicles for evaluative practices of stance-taking, the paper explores how solidarity symbols function, first, as bonding icons able to construct affective alignment and a sense of community, and second, how these bonding icons construct the reader as aligned with specific ideology, contributing simultaneously to community-building and alienation, where not sharing the dominant frame of mourning manifests as contestation. The findings reveal, first, how solidarity symbols have the capacity to serve as templates of affect for subsequent tokens; in addition to the iterations replicating the function and form of popular solidarity symbols (like #JeSuisCharlie), there is also a transmission of affect and stance. Second, as individual commemorative acts are always embedded in wider socio-cultural imagination, and therefore cannot escape significations regarding grievability of life, solidarity symbols contribute to affect alienation and not only affective communion. Third, as circulation of solidarity symbols contributes to the visual representation of “us” with an implicit presence of the Other, solidarity symbols can be viewed as struggles for recognition. Solidarity symbols operate within wider regimes of visibility where issues of recognition speak to issues of grievability. It is therefore important to consider the ways in which the meanings embedded in solidarity symbols are constructed and what these meanings are

Controlling for confounding network properties in hypothesis testing and anomaly detection

An important task in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they could be examples of malicious activity or network malfunction. Hypothesis testing using network statistics to summarize the behavior of the network provides a robust framework for the anomaly detection decision process. Unfortunately, choosing network statistics that are dependent on confounding factors like the total number of nodes or edges can lead to incorrect conclusions (e.g., false positives and false negatives). In this dissertation we describe the challenges that face anomaly detection in dynamic network streams regarding confounding factors. We also provide two solutions to avoiding error due to confounding factors: the first is a randomization testing method that controls for confounding factors, and the second is a set of size-consistent network statistics which avoid confounding due to the most common factors, edge count and node count