# Sociology

13 papers with code • 1 benchmarks • 1 datasets

## Most implemented papers

# A Simple Algorithm for Scalable Monte Carlo Inference

The methods of statistical physics are widely used for modelling complex networks.

# Community detection in graphs

The modern science of networks has brought significant advances to our understanding of complex systems.

# Semantics derived automatically from language corpora contain human-like biases

Here we show for the first time that human-like semantic biases result from the application of standard machine learning to ordinary language---the same sort of language humans are exposed to every day.

# A network approach to topic models

By adapting existing community-detection methods -- using a stochastic block model (SBM) with non-parametric priors -- we obtain a more versatile and principled framework for topic modeling (e. g., it automatically detects the number of topics and hierarchically clusters both the words and documents).

# Latent Variable Time-varying Network Inference

The estimation of the contribution of the latent factors is embedded in the model which produces both sparse and low-rank components for each time point.

# A Recurrent Graph Neural Network for Multi-Relational Data

The era of data deluge has sparked the interest in graph-based learning methods in a number of disciplines such as sociology, biology, neuroscience, or engineering.

# On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning

Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population.

# Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data

The challenges for this problem are two-fold: on the one hand, we have to derive a causal estimator to estimate the causal quantity from observational data, where there exists confounding bias; on the other hand, we have to deal with the identification of CATE when the distribution of covariates in treatment and control groups are imbalanced.

# A Distributed Hybrid Community Detection Methodology for Social Networks

Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the millions or even billions of vertices.

# Toward Gender-Inclusive Coreference Resolution

Correctly resolving textual mentions of people fundamentally entails making inferences about those people.