Search Results for author: John Palowitch

Found 9 papers, 5 papers with code

Scalable Privacy-enhanced Benchmark Graph Generative Model for Graph Convolutional Networks

no code implementations10 Jul 2022 Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov

A surge of interest in Graph Convolutional Networks (GCN) has produced thousands of GCN variants, with hundreds introduced every year.

Graph Generation Node Classification

Synthetic Graph Generation to Benchmark Graph Learning

1 code implementation4 Apr 2022 Anton Tsitsulin, Benedek Rozemberczki, John Palowitch, Bryan Perozzi

This shockingly small sample size (~10) allows for only limited scientific insight into the problem.

Graph Generation Graph Learning +2

Zero-shot Transfer Learning on Heterogeneous Graphs via Knowledge Transfer Networks

no code implementations3 Mar 2022 Minji Yoon, John Palowitch, Dustin Zelle, Ziniu Hu, Ruslan Salakhutdinov, Bryan Perozzi

We propose a zero-shot transfer learning module for HGNNs called a Knowledge Transfer Network (KTN) that transfers knowledge from label-abundant node types to zero-labeled node types through rich relational information given in the HG.

Domain Adaptation Graph Learning +1

GraphWorld: Fake Graphs Bring Real Insights for GNNs

1 code implementation28 Feb 2022 John Palowitch, Anton Tsitsulin, Brandon Mayer, Bryan Perozzi

Using GraphWorld, a user has fine-grained control over graph generator parameters, and can benchmark arbitrary GNN models with built-in hyperparameter tuning.

Recurrent Graph Neural Networks for Rumor Detection in Online Forums

1 code implementation8 Aug 2021 Di Huang, Jacob Bartel, John Palowitch

The widespread adoption of online social networks in daily life has created a pressing need for effectively classifying user-generated content.

Finding Stable Groups of Cross-Correlated Features in Two Data Sets With Common Samples

1 code implementation10 Sep 2020 Miheer Dewaskar, John Palowitch, Mark He, Michael I. Love, Andrew B. Nobel

Given two data types, a bimodule is a pair $(A, B)$ of feature sets from the two types such that the aggregate cross-correlation between the features in $A$ and those in $B$ is large.

Graph Clustering with Graph Neural Networks

no code implementations30 Jun 2020 Anton Tsitsulin, John Palowitch, Bryan Perozzi, Emmanuel Müller

Graph Neural Networks (GNNs) have achieved state-of-the-art results on many graph analysis tasks such as node classification and link prediction.

Graph Clustering Link Prediction +1

MONET: Debiasing Graph Embeddings via the Metadata-Orthogonal Training Unit

no code implementations25 Sep 2019 John Palowitch, Bryan Perozzi

In this paper, we show that when metadata is correlated with the formation of node neighborhoods, unsupervised node embedding dimensions learn this metadata.

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