activity
20172021
most citedMixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

270 citations · 283 across the 5 of their papers we have counts for

collaborators

8 papers

cs.LG2021

DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data

Nazanin Alipourfard, Keith Burghardt, Kristina Lerman

Quantitative analysis of large-scale data is often complicated by the presence of diverse subgroups, which reduce the accuracy of inferences they make on held-out data. To address…

physics.soc-ph2021

Emergence of Structural Inequalities in Scientific Citation Networks

Buddhika Nettasinghe, Nazanin Alipourfard, Vikram Krishnamurthy +1

Structural inequalities persist in society, conferring systematic advantages to some people at the expense of others, for example, by giving them substantially more influence and o…

cs.SI2021

A Directed, Bi-Populated Preferential Attachment Model with Applications to Analyzing the Glass Ceiling Effect

Buddhika Nettasinghe, Nazanin Alipourfard, Vikram Krishnamurthy +1

Preferential attachment, homophily and, their consequences such as the glass ceiling effect have been well-studied in the context of undirected networks. However, the lack of an in…

cs.LG2019270 cited

MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing

Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor +5

Existing popular methods for semi-supervised learning with Graph Neural Networks (such as the Graph Convolutional Network) provably cannot learn a general class of neighborhood mix…

cs.SI2019

Friendship Paradox Biases Perceptions in Directed Networks

Nazanin Alipourfard, Buddhika Nettasinghe, Andres Abeliuk +2

How popular a topic or an opinion appears to be in a network can be very different from its actual popularity. For example, in an online network of a social media platform, the num…

cs.CY2018

Using Simpson's Paradox to Discover Interesting Patterns in Behavioral Data

Nazanin Alipourfard, Peter G. Fennell, Kristina Lerman

We describe a data-driven discovery method that leverages Simpson's paradox to uncover interesting patterns in behavioral data. Our method systematically disaggregates data to iden…