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Found 17 papers, 5 papers with code
Examining the consumption of radical content on YouTube
Although it is under-studied relative to other social media platforms, YouTube is arguably the largest and most engaging online media consumption platform in the world.
Stacking Models for Nearly Optimal Link Prediction in Complex Networks
These results indicate that the state-of-the-art for link prediction comes from combining individual algorithms, which achieves nearly optimal predictions.
Predicting the outcomes of policy diffusion from U.S. states to federal law
Using a large, longitudinal data set of state level policies and their traits, we train models to predict (i) whether policies become national policy, and (ii) how many states must pass a given policy before it becomes national.
Computers and Society Physics and Society
Thermodynamics of the Minimum Description Length on Community Detection
Modern statistical modeling is an important complement to the more traditional approach of physics where Complex Systems are studied by means of extremely simple idealized models.
Physics and Society Disordered Systems and Neural Networks Social and Information Networks Data Analysis, Statistics and Probability
Evaluating Overfit and Underfit in Models of Network Community Structure
These results introduce both a theoretically principled approach to evaluate over and underfitting in models of network community structure and a realistic benchmark by which new methods may be evaluated and compared.
Characterizing the structural diversity of complex networks across domains
A number of studies in the field have been focused on finding the common properties among different kinds of networks such as heavy-tail degree distribution, small-worldness and modular structure and they have tried to establish a theory of structural universality in complex networks.
The ground truth about metadata and community detection in networks
We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models.
Structure and inference in annotated networks
For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network, geographic location of nodes in the Internet, or cellular function of nodes in a gene regulatory network.
Detectability thresholds and optimal algorithms for community structure in dynamic networks
We study the fundamental limits on learning latent community structure in dynamic networks.
A unified view of generative models for networks: models, methods, opportunities, and challenges
Here, we describe a unified view of generative models for networks that draws together many of these disparate threads and highlights the fundamental similarities and differences that span these fields.
Learning Latent Block Structure in Weighted Networks
We then evaluate the WSBM's performance on both edge-existence and edge-weight prediction tasks for a set of real-world weighted networks.
Efficiently inferring community structure in bipartite networks
Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected.
Detecting change points in the large-scale structure of evolving networks
Interactions among people or objects are often dynamic in nature and can be represented as a sequence of networks, each providing a snapshot of the interactions over a brief period of time.
Adapting the Stochastic Block Model to Edge-Weighted Networks
We generalize the stochastic block model to the important case in which edges are annotated with weights drawn from an exponential family distribution.
Estimating the historical and future probabilities of large terrorist events
Quantities with right-skewed distributions are ubiquitous in complex social systems, including political conflict, economics and social networks, and these systems sometimes produce extremely large events.
Power-law distributions in empirical data
Power-law distributions occur in many situations of scientific interest and have significant consequences for our understanding of natural and man-made phenomena.
Data Analysis, Statistics and Probability Disordered Systems and Neural Networks Applications Methodology
Finding community structure in very large networks
Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(m d log n) where d is the depth of the dendrogram describing the community structure.
Statistical Mechanics Disordered Systems and Neural Networks
