Search Results for author: Nazanin Alipourfard

Found 5 papers, 5 papers with code

DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data

1 code implementation31 Aug 2021 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.

Friendship Paradox Biases Perceptions in Directed Networks

1 code implementation13 May 2019 Nazanin Alipourfard, Buddhika Nettasinghe, Andres Abeliuk, Vikram Krishnamurthy, Kristina Lerman

For example, in an online network of a social media platform, the number of people who mention a topic in their posts---i. e., its global popularity---can be dramatically different from how people see it in their social feeds---i. e., its perceived popularity---where the feeds aggregate their friends' posts.

Social and Information Networks Physics and Society

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

3 code implementations30 Apr 2019 Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, Aram Galstyan

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 mixing relationships.

Node Classification

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

1 code implementation8 May 2018 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.

online learning

Can you Trust the Trend: Discovering Simpson's Paradoxes in Social Data

2 code implementations13 Jan 2018 Nazanin Alipourfard, Peter G. Fennell, Kristina Lerman

We present a statistical method to automatically identify Simpson's paradox in data by comparing statistical trends in the aggregate data to those in the disaggregated subgroups.

Computers and Society

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