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Community informed experimental design
Network information has become a common feature of many modern experiments. From vaccine efficacy studies to marketing for product adoption,...
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Project-Based Learning with a Social Impact: Connecting Data Science Movements, Civic Statistics, and Service-Learning
Ever since there has been an organized collection and use of data for informing decision making, there has been a debate about the extent to which...
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Asymptotic dependence of in- and out-degrees in a preferential attachment model with reciprocity
Reciprocity characterizes the information exchange between users in a network, and some empirical studies have revealed that social networks have a...
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Neural Networks
Neural networks has its roots in the 1950s with the “perceptron.” It is essentially sets of regression equations linked end to end in a manner that...
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Scalable Estimation of Epidemic Thresholds via Node Sampling
Infectious or contagious diseases can be transmitted from one person to another through social contact networks. In today’s interconnected global...
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A Non-overlap** Community Detection Approach Based on \(\alpha \) -Structural Similarity
Community detection in social networks is a widely studied topic in Artificial Intelligence and graph analysis. It can be useful to discover hidden...
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On the Rényi index of random graphs
Networks (graphs) permeate scientific fields such as biology, social science, economics, etc. Empirical studies have shown that real-world networks...
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Paul Holland
Paul Holland started working for the Educational Testing Service in 1975 and retired from ETS in 2006. Between 1993 and 2000, he was professor of...
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Clustering by deep latent position model with graph convolutional network
With the significant increase of interactions between individuals through numeric means, clustering of nodes in graphs has become a fundamental...
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Applications of dual regularized Laplacian matrix for community detection
Spectral clustering is widely used for detecting clusters in networks for community detection, while a small change on the graph Laplacian matrix...
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An attribute-based Node2Vec model for dynamic community detection on co-authorship network
Networks offer a wide range of applications in various domains of life and scientific research. Community detection, which aims at understanding the...
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Analytical Review: Intelligence Scanning
Analytical procedures involve the study of plausible relationships between both financial and non-financial data. Before the mid-2000s, sources of...
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Innovation for improving climate-related data—Lessons learned from setting up a data hub
In this article, we present a framework to assess the challenges in the climate-related data landscape. From our perspective, we describe challenges...
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Dynamic Networks
Most complex systems—and, hence, networks—are dynamic in nature. So, realistically, the corresponding network graphs and processes thereon are...
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Digital Methods and the Evolution of the Epistemology of Social Sciences
After ten years that the debate on big data, computation and digital methods has been a contested epistemological terrain between some who were...
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Using Social Media Surveillance in Order to Enhance the Effectiveness of Crew Members in Search and Rescue Missions
Social media nowadays are linked almost with every aspect of our lives. They can and have been used to explain social relations, human behaviors,...
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Estimating mixed-memberships using the symmetric laplacian inverse matrix
Mixed membership community detection is a challenging problem. In this paper, to detect mixed memberships, we propose a new method Mixed-SLIM which...
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Applied Statistical Learning With Case Studies in Stata
This textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical...
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A dual-frame approach for estimation with respondent-driven samples
Respondent-driven sampling (RDS) is an increasingly common method for surveying rare, hidden, or otherwise hard-to-reach populations. Instead of...
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Discovering Overlap** Communities Based on Cohesive Subgraph Models over Graph Data
Detecting and analyzing dense subgroups or communities from social and information networks has attracted great attention over last decade due to its...