Scientific attention is unevenly distributed, creating inequities in recognition and distorting access to opportunities. Using citations as a proxy, we quantify disparities in attention by gender and institutional prestige. We find that women receive systematically fewer citations than men, and that attention is increasingly concentrated among authors from elite institutions -- patterns not fully explained by underrepresentation alone. To explain these dynamics, we introduce a model of citation network growth that incorporates homophily (tendency to cite similar authors), preferential attachment (favoring highly cited authors) and group size (underrepresentation). The model shows that disparities arise not only from group size imbalances but also from cumulative advantage amplifying biased citation preferences. Importantly, increasing representation alone is often insufficient to reduce disparities. Effective strategies should also include reducing homophily, amplifying the visibility of underrepresented groups, and supporting equitable integration of newcomers. Our findings highlight the challenges of mitigating inequities in asymmetric networks like citations, where recognition flows in one direction. By making visible the mechanisms through which attention is distributed, we contribute to efforts toward a more responsible web of science that is fairer, more transparent, and more inclusive, and that better sustains innovation and knowledge production.

Consider a target being tracked by a cognitive radar network. If the target can intercept noisy radar emissions, how can it detect coordination in the network? By ‘coordination’ we mean that the radar emissions satisfy Pareto optimality with respect to multi-objective optimization over the objective functions of each radar and a constraint on total network power output. This paper provides a novel inverse multi-objective optimization approach for statistically detecting Pareto optimal (’coordinating’) behavior, from a finite dataset of noisy radar emissions. Specifically, we develop necessary and sufficient conditions for radar network emissions to be consistent with multi-objective optimization (coordination), and we provide a statistical detector with theoretical guarantees for determining this consistency when radar emissions are observed in noise. We also provide numerical simulations which validate our approach. Note that while we make use of the specific framework of a radar network coordination problem, our results apply more generally to the field of inverse multi-objective optimization.

This article considers the problem of estimating a power-law degree distribution of an undirected network using sampled data. Although power-law degree distributions are ubiquitous in nature, the widely used parametric methods for estimating them (e.g., linear regression on double-logarithmic axes and maximum likelihood estimation with uniformly sampled nodes) suffer from the large variance introduced by the lack of data-points from the tail portion of the power-law degree distribution. As a solution, we present a novel maximum likelihood estimation approach that exploits the friendship paradox to sample more efficiently from the tail of the degree distribution. We analytically show that the proposed method results in a smaller bias, variance and a Cramèr–Rao lower bound compared to the vanilla maximum likelihood estimate obtained with uniformly sampled nodes (which is the most commonly used method in literature). Detailed numerical and empirical results are presented to illustrate the performance of the proposed method under different conditions and how it compares with alternative methods. We also show that the proposed method and its desirable properties (i.e., smaller bias, variance, and Cramèr–Rao lower bound compared to vanilla method based on uniform samples) extend to parametric degree distributions other than the power-law such as exponential degree distributions as well. All the numerical and empirical results are reproducible and the code is publicly available on Github.

This paper deals with randomized polling of a social network. In the case of forecasting the outcome of an election between two candidates A and B, classical intent polling asks randomly sampled individuals: who will you vote for? Expectation polling asks: who do you think will win? In this paper, we propose a novel neighborhood expectation polling (NEP) strategy that asks randomly sampled individuals: what is your estimate of the fraction of votes for A? Therefore, in NEP, sampled individuals will naturally look at their neighbors (defined by the underlying social network graph) when answering this question. Hence, the mean squared error (MSE) of NEP methods rely on selecting the optimal set of samples from the network. To this end, we propose two NEP algorithms for the following cases: (i) the social network graph is not known but, random walks (sequential exploration) can be performed on the graph, and (ii) the social network graph is unknown but, uniformly sampled nodes from the network are available. For both cases, algorithms based on a graph theoretic consequence called friendship paradox are proposed. Theoretical results on the dependence of the MSE of the algorithms on the properties of the network are established. Numerical results on real and synthetic data sets are provided to illustrate the performance of the algorithms.

This paper considers a novel formulation of inverse reinforcement learning with behavioral economics constraints to address inverse sequential hypothesis testing (SHT) in Bayesian agents. The aim is to estimate the detection costs by observing the actions of the sequential hypothesis detector. Our methodology involves Bayesian revealed preferences from microeconomics and rational inattention from behavioral economics. First, we show that Bayesian agents optimally performing SHT are rationally inattentive utility maximizers. Using established results in Bayesian revealed preferences, we outline a feasibility test for a data analyst observing the Bayesian agents to estimate their detection costs. Numerical examples illustrate the performance of the inverse sequential hypothesis testing algorithm.

The proliferation of connected cars globally has the potential to produce torrents of Big Data that will enable improvements in driver safety, new location based services, improvements in vehicle quality, and optimized vehicle designs. One aspect of connected car data involves driving behavior data and its use for Usage Based Insurance (UBI). UBI has become one of the most widely used applications of driving behavior data. Currently, the transmission and processing of high frequency driving behavior data from the connected car to the cloud is limited by wireless data costs and in-vehicle hardware complexity. To alleviate these issues, we detail the development of a machine learning framework utilizing a kinematic classification methodology applied to down sampled time series vehicle data sets for accurate imputation of driving behavior events in UBI applications. The down-sampled data, consisting of 5 second frames with data fields of timestamp, vehicle speed, and vehicle acceleration is classified into unique kinematic clusters to standardize any driving behavior data distributions. Subsequently, machine learning is used to impute harsh driving events for each 5 second frame in select kinematic clusters. This novel machine learning methodology reduced data set sizes by 75%, utilized a limited set of five attributes, and achieved an average precision and recall of 84.5% and 63.5% for two distinct connected car data sets with 1/5 Hz down-sampled data.

Image processing and machine learning have become valuable tools for predictive maintenance applications for a wide variety of industrial and commercial components. We present a novel light transmission image processing methodology utilizing statistical distance algorithms (Wasserstein distance (WD), Kolmogorov-Smirnov statistic (K-S)) for physical attribute correlation combined with Bayesian linear regression to estimate wear level and lifetime prediction for air filters. Robustness of this machine learning algorithm was evaluated using spatial block bootstrapping to generate synthetic training data to estimate the 95% prediction interval for air filter lifetime. Validation of this lifetime prediction was performed using imaging measurements on a test air filter, which showed good agreement with the machine learning model. The proposed machine learning based image analytics framework effectively enables robust predictions of component wear for predictive maintenance.

This paper deals with the problem of information fusion for state/ parameter estimation in social networks. The information consists of the sentiment of the opinions expressed by people. The average sentiment of the opinions expressed by people constitutes a noisy observation of an unknown state. A controller seeks to estimate the state by controlling the dynamics of sampling that minimizes an objective function comprising of state estimation error and the cost of acquiring sentiments. The stochastic control problem is formulated as a partially observed Markov decision process (POMDP), and sufficient conditions under which a myopic policy forms an upperbound to the optimal policy of the POMDP are provided. The myopic policy minimizes the immediate costs while ignoring the expected costs incurred over time, and is computationally inexpensive for large state spaces. Finally, the performance of the proposed myopic policy is evaluated for POMDP formulation whose parameters are computed from real-data set obtained from Twitter.

YouTube, with millions of content creators, has become the preferred destination for viewing videos online. Through the Partner program, YouTube allows content creators to monetize their popular videos. Of significant importance for content creators is which meta-level features (title, tag, thumbnail, and description) are most sensitive for promoting video popularity. The popularity of videos also depends on the social dynamics, i.e., the interaction of the content creators (or channels) with YouTube users. Using real-world data consisting of about 6 million videos spread over 25 thousand channels, we empirically examine the sensitivity of YouTube meta-level features and social dynamics. The key meta-level features that impact the view counts of a video include: first day view count, number of subscribers, contrast of the video thumbnail, Google hits, number of keywords, video category, title length, and number of upper-case letters in the title, respectively, and illustrate that these meta-level features can be used to estimate the popularity of a video. In addition, optimizing the meta-level features after a video is posted increases the popularity of videos. In the context of social dynamics, we discover that there is a causal relationship between views to a channel and the associated number of subscribers. Additionally, insights into the effects of scheduling and video playthrough in a channel are also provided. Our findings provide a useful understanding of user engagement in YouTube.

This paper describes the development of a hierarchical situation assessment system using Bayesian networks and also a situation assessment based decision making system for battlespace environment. The situation assessment system consists of two levels of reconfigurable Bayesian networks that are adapted with changes that occur in the battlespace on two different timescales. The decision making system uses this adaptive situation assessment system to make decisions that in turn affect the battlespace dynamics. An algorithm is provided to model these interactions and dynamics of the battlespace. Furthermore, a Markovian model for the battlespace dynamics is provided