6 citations · 7 across the 2 of their papers we have counts for
5 papers
Predicting and Explaining Behavioral Data with Structured Feature Space Decomposition
Peter G Fennell, Zhiya Zuo, Kristina Lerman
Modeling human behavioral data is challenging due to its scale, sparseness (few observations per individual), heterogeneity (differently behaving individuals), and class imbalance…
Degree Correlations Amplify the Growth of Cascades in Networks
Xin-Zeng Wu, Peter G. Fennell, Allon G. Percus +1
Networks facilitate the spread of cascades, allowing a local perturbation to percolate via interactions between nodes and their neighbors. We investigate how network structure affe…
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…
Can you Trust the Trend: Discovering Simpson's Paradoxes in Social Data
Nazanin Alipourfard, Peter G. Fennell, Kristina Lerman
We investigate how Simpson's paradox affects analysis of trends in social data. According to the paradox, the trends observed in data that has been aggregated over an entire popula…
Multistate dynamical processes on networks: Analysis through degree-based approximation frameworks
Peter G. Fennell, James P. Gleeson
Multistate dynamical processes on networks, where nodes can occupy one of a multitude of discrete states, are gaining widespread use because of their ability to recreate realistic,…