• Publications
  • Influence
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
TLDR
We propose a new model, MixHop, that can learn these relationships, including difference operators, by repeatedly mixing feature representations of neighbors at various distances. Expand
Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks
TLDR
We propose a temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. Expand
Information transfer in social media
TLDR
We propose a measure of causal relationships between nodes based on the information--theoretic notion of transfer entropy, or information transfer, which allows us to differentiate between weak and strong influence over large groups. Expand
Invariant Representations without Adversarial Training
TLDR
We show that adversarial training is unnecessary and sometimes counter-productive; we cast invariant representation learning as a single information-theoretic objective that can be directly optimized. Expand
Efficient Estimation of Mutual Information for Strongly Dependent Variables
TLDR
We demonstrate that a popular class of nonparametric mutual information (MI) estimators based on k-nearest-neighbor graphs requires number of samples that scales exponentially with the true MI. Expand
Discovering Structure in High-Dimensional Data Through Correlation Explanation
TLDR
We introduce a method to learn a hierarchy of successively more abstract representations of complex data based on optimizing an information-theoretic objective that automatically discovers meaningful structure for data from diverse sources including personality tests, DNA, and human language. Expand
Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge
TLDR
We introduce Correlation Explanation (CorEx), an alternative approach to topic modeling that does not assume an underlying generative model, and instead learns maximally informative topics through an information-theoretic framework. Expand
Information-theoretic measures of influence based on content dynamics
TLDR
We introduce content transfer, an information-theoretic measure with a predictive interpretation that directly quantifies the strength of the effect of one user's content on another's in a model-free way. Expand
Modeling Temporal Activity Patterns in Dynamic Social Networks
TLDR
We propose a coupled hidden Markov model (HMM), where each user's activity evolves according to a Markov chain with a hidden state that is influenced by the collective activity of the friends of the user. Expand
What Stops Social Epidemics?
TLDR
Theoretical progress in understanding the dynamics of spreading processes on graphs suggests the existence of an epidemic threshold below which no epidemics form and above which epidemics spread to a significant fraction of the graph. Expand
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