Nadya Bliss

Mass media and the contagion of fear: The case of Ebola in America

Background In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends. Methodology We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data. Conclusions We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.

A Spectral Framework for Anomalous Subgraph Detection

A wide variety of application domains is concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse application areas, a common problem of interest is the detection of a subset of entities whose connectivity is anomalous with respect to the rest of the data. While the detection of such anomalous subgraphs has received a substantial amount of attention, no application-agnostic framework exists for analysis of signal detectability in graph-based data. In this paper, we describe a framework that enables such analysis using the principal eigenspace of a graphs residuals matrix, commonly called the modularity matrix in community detection. Leveraging this analytical tool, we show that the framework has a natural power metric in the spectral norm of the anomalous subgraphs adjacency matrix (signal power) and of the background graphs residuals matrix (noise power). We propose several algorithms based on spectral properties of the residuals matrix, with more computationally expensive techniques providing greater detection power. Detection and identification performance are presented for a number of signal and noise models, including clusters and bipartite foregrounds embedded into simple random backgrounds, as well as graphs with community structure and realistic degree distributions. The trends observed verify intuition gleaned from other signal processing areas, such as greater detection power when the signal is embedded within a less active portion of the background. We demonstrate the utility of the proposed techniques in detecting small, highly anomalous subgraphs in real graphs derived from Internet traffic and product co-purchases.

Efficient anomaly detection in dynamic, attributed graphs: Emerging phenomena and big data

When working with large-scale network data, the interconnected entities often have additional descriptive information. This additional metadata may provide insight that can be exploited for detection of anomalous events. In this paper, we use a generalized linear model for random attributed graphs to model connection probabilities using vertex metadata. For a class of such models, we show that an approximation to the exact model yields an exploitable structure in the edge probabilities, allowing for efficient scaling of a spectral framework for anomaly detection through analysis of graph residuals, and a fast and simple procedure for estimating the model parameters. In simulation, we demonstrate that taking into account both attributes and dynamics in this analysis has a much more significant impact on the detection of an emerging anomaly than accounting for either dynamics or attributes alone. We also present an analysis of a large, dynamic citation graph, demonstrating that taking additional document metadata into account emphasizes parts of the graph that would not be considered significant otherwise.

On the Connectivity of Multi-layered Networks: Models, Measures and Optimal Control

Networks appear naturally in many high-impact real-world applications. In an increasingly connected and coupled world, the networks arising from many application domains are often collected from different channels, forming the so-called multi-layered networks, such as cyber-physical systems, organization-level collaboration platforms, critical infrastructure networks and many more. Compared with single-layered networks, multi-layered networks are more vulnerable as even a small disturbance on one supporting layer/network might cause a ripple effect to all the dependent layers, leading to a catastrophic/cascading failure of the entire system. The state-of-the-art has been largely focusing on modeling and manipulating the cascading effect of two-layered interdependent network systems for some specific type of network connectivity measure. This paper generalizes the challenge to multiple dimensions. First, we propose a new data model for multi-layered networks MULAN, which admits an arbitrary number of layers with a much more flexible dependency structure among different layers, beyond the current pair-wise dependency. Second, we unify a wide range of classic network connectivity measures SUBLINE. Third, we show that for any connectivity measure in the SUBLINE family, it enjoys the diminishing returns property which in turn lends itself to a family of provable near-optimal control algorithms with linear complexity. Finally, we conduct extensive empirical evaluations on real network data, to validate the effectiveness of the proposed algorithms.

Performance of the CORDEX‐Africa regional climate simulations in representing the hydrological cycle of the Niger River basin

The water resources of the Niger River basin (NRB) in West Africa are crucial to support the socioeconomic development of nine countries. In this study, we compared and evaluated performances of simulations at 0.44° resolution of several regional climate models (RCMs) of the Coordinated Regional climate Downscaling Experiment (CORDEX) in reproducing the statistical properties of the hydrological cycle of the NRB in the current climate. To capture the large range of climatic zones in the region, analyses were conducted by spatially averaging the water balance components in four nested subbasins. Most RCMs overestimate (order of +10% to +400%, depending on model and subbasin) the mean annual difference between precipitation (P) and evaporation (E), whose observed value was assumed equal to the long-term discharge based on the mass conservation principle. This is due to a tendency to simulate larger mean annual P and a weak hydrological cycle in the E channel. Some exceptions appear in the humid most-upstream subbasin, where a few RCMs underestimate P. Overall, the representation of the water balance is mostly sensitive to the parameterized land surface and atmospheric processes of the nested RCMs, with less influence of the driving general circulation model. This finding is supported by further analyses on seasonal cycle and spatial variability of the water balance components and on model performances in reproducing observed climatology. Results of this work should be considered when RCMs are used directly or in impact studies to develop policies and plan investments aimed at ensuring water sustainability in the NRB.

Towards Optimal Connectivity on Multi-Layered Networks

Networks are prevalent in many high impact domains. Moreover, cross-domain interactions are frequently observed in many applications, which naturally form the dependencies between different networks. Such kind of highly coupled network systems are referred to as multi-layered networks, and have been used to characterize various complex systems, including critical infrastructure networks, cyber-physical systems, collaboration platforms, biological systems, and many more. Different from single-layered networks where the functionality of their nodes is mainly affected by within-layer connections, multi-layered networks are more vulnerable to disturbance as the impact can be amplified through cross-layer dependencies, leading to the cascade failure to the entire system. To manipulate the connectivity in multi-layered networks, some recent methods have been proposed based on two-layered networks with specific types of connectivity measures. In this paper, we address the above challenges in multiple dimensions. First, we propose a family of connectivity measures (SubLine) that unifies a wide range of classic network connectivity measures. Third, we reveal that the connectivity measures in the SubLine family enjoy diminishing returns property, which guarantees a near-optimal solution with linear complexity for the connectivity optimization problem. Finally, we evaluate our proposed algorithm on real data sets to demonstrate its effectiveness and efficiency.

Anomalous subgraph detection in publication networks: Leveraging truth

Analysis of social networks has the potential to provide insight into a wide range of applications. As datasets grow, a key challenge is the lack of existing truth models. Unlike traditional signal processing, where models of truth and background data exist and are often well defined, these models are commonly lacking for social networks. This paper presents a transdisciplinary approach of mitigating this challenge by leveraging research on scientific innovation together with a novel Signal Processing for Graphs (SPG) algorithmic framework. The results suggest new ways for the study of innovation patterns in publication networks.

A Collaborative Web-Based Environmental Data Visualization and Analysis Framework

We present an environmental data visualization framework that features synchronous and asynchronous multi-user interaction with all the benefits of modern web-based applications, such as easy accessibility and cross-platform compatibility. In contrast to outdated web-based network protocols, the proposed framework uses HTML5 WebSockets to enable full-duplex communication between server and clients. To demonstrate the framework, we chose the ecological problem of water scarcity in Africa. In this case study, water scarcity is calculated and visualized using various models and parameters, which can easily be shared among users and devices. Hence, we show the potential and the utilization of web technologies for collaborative environmental data exploration on distributed desktop and mobile devices.