Search Results for author:
Found 9 papers, 4 papers with code
Sensor Placement for Learning in Flow Networks
Large infrastructure networks (e. g. for transportation and power distribution) require constant monitoring for failures, congestion, and other adversarial events.
Better Fair than Sorry: Adversarial Missing Data Imputation for Fair GNNs
We address this challenge by proposing Better Fair than Sorry (BFtS), a fair missing data imputation model for protected attributes used by fair GNNs.
Robust Ante-hoc Graph Explainer using Bilevel Optimization
Explaining the decisions made by machine learning models for high-stakes applications is critical for increasing transparency and guiding improvements to these decisions.
Link Prediction without Graph Neural Networks
Link prediction, which consists of predicting edges based on graph features, is a fundamental task in many graph applications.
A Broader Picture of Random-walk Based Graph Embedding
Graph embedding based on random-walks supports effective solutions for many graph-related downstream tasks.
Event Detection on Dynamic Graphs
In this paper, we propose DyGED, a simple yet novel deep learning model for event detection on dynamic graphs.
POLE: Polarized Embedding for Signed Networks
From the 2016 U. S. presidential election to the 2021 Capitol riots to the spread of misinformation related to COVID-19, many have blamed social media for today's deeply divided society.
Feature-based Individual Fairness in k-Clustering
Ensuring fairness in machine learning algorithms is a challenging and essential task.
Outlier Detection from Network Data with Subnetwork Interpretation
Detecting a small number of outliers from a set of data observations is always challenging.
