5 papers
When do neural ordinary differential equations generalize on complex networks?
Moritz Laber, Tina Eliassi-Rad, Brennan Klein
Neural ordinary differential equations (neural ODEs) can effectively learn dynamical systems from time series data, but their behavior on graph-structured data remains poorly under…
Identifying and Upweighting Power-Niche Users to Mitigate Popularity Bias in Recommendations
David Liu, Erik Weis, Moritz Laber +2
Recommender systems have been shown to exhibit popularity bias by over-recommending popular items and under-recommending relevant niche items. We seek to understand niche users in…
Effects of higher-order interactions and homophily on information access inequality
Moritz Laber, Samantha Dies, Joseph Ehlert +2
The spread of information through socio-technical systems determines which individuals are the first to gain access to opportunities and insights. Yet, the pathways through which i…
Bypassing Skip-Gram Negative Sampling: Dimension Regularization as a More Efficient Alternative for Graph Embeddings
David Liu, Arjun Seshadri, Tina Eliassi-Rad +1
A wide range of graph embedding objectives decompose into two components: one that enforces similarity, attracting the embeddings of nodes that are perceived as similar, and anothe…
When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations
David Liu, Jackie Baek, Tina Eliassi-Rad
We study the fairness of dimensionality reduction methods for recommendations. We focus on the fundamental method of principal component analysis (PCA), which identifies latent com…