13 papers with code • 1 benchmarks • 1 datasets

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Most implemented papers

A Simple Algorithm for Scalable Monte Carlo Inference

stivalaa/EstimNetDirected 2 Jan 2019

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

Community detection in graphs

Arda-Bati/Reddit-Hyperlinks-Conflict-Prediction 3 Jun 2009

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

YunjinPark/modu_project 25 Aug 2016

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

martingerlach/hSBM_Topicmodel 4 Aug 2017

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

fdtomasi/regain 12 Feb 2018

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

bioannidis/adaptive_recurrent_graph_neural_network 5 Nov 2018

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

nvedant07/effort_reward_fairness 4 Mar 2019

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

octeufer/Adversarial-Balancing-based-representation-learning-for-Causal-Effect-Inference 30 Apr 2019

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

drkostas/HGN Algorithms 2019

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

TristaCao/into_inclusivecoref ACL 2020

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