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Found 13 papers, 7 papers with code
Variational Inference of Parameters in Opinion Dynamics Models
We validate our method on a bounded confidence model with agent roles (leaders and followers).
Extracting the Multiscale Causal Backbone of Brain Dynamics
The bulk of the research effort on brain connectivity revolves around statistical associations among brain regions, which do not directly relate to the causal mechanisms governing brain dynamics.
Learning Multiscale Non-stationary Causal Structures
Our model allows sampling an MN-DAG according to user-specified priors on the time-dependence and multiscale properties of the causal graph.
On learning agent-based models from data
In this paper, we propose a protocol to learn the latent micro-variables of an ABM from data.
The Evolving Causal Structure of Equity Risk Factors
They link the evolution of the causal structure of equity risk factors with market volatility and a worsening macroeconomic environment, and show that, in times of financial crisis, exposure to different factors boils down to exposure to the market risk factor.
Learning Opinion Dynamics From Social Traces
In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces.
Predicting the Role of Political Trolls in Social Media
In particular, we show how to classify trolls according to their political role ---left, news feed, right--- by using features extracted from social media, i. e., Twitter, in two scenarios: (i) in a traditional supervised learning scenario, where labels for trolls are available, and (ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls.
Link Prediction via Higher-Order Motif Features
A common approach is to use features based on the common neighbors of an unconnected pair of nodes to predict whether the pair will form a link in the future.
Political Discourse on Social Media: Echo Chambers, Gatekeepers, and the Price of Bipartisanship
By comparing the two, we find that Twitter users are, to a large degree, exposed to political opinions that agree with their own.
Social and Information Networks
VHT: Vertical Hoeffding Tree
IoT Big Data requires new machine learning methods able to scale to large size of data arriving at high speed.
Partial Key Grouping: Load-Balanced Partitioning of Distributed Streams
We study the problem of load balancing in distributed stream processing engines, which is exacerbated in the presence of skew.
Distributed, Parallel, and Cluster Computing
Quantifying Controversy in Social Media
Unlike previous work, rather than study controversy in a single hand-picked topic and use domain specific knowledge, we take a general approach to study topics in any domain.
Social and Information Networks
Says who? Automatic Text-Based Content Analysis of Television News
We perform an automatic analysis of television news programs, based on the closed captions that accompany them.
