Search Results for author: John Palowitch

Found 11 papers, 9 papers with code

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.

Recommendation Systems

Graph Clustering with Graph Neural Networks

no code implementations NeurIPS 2023 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.

Attribute Clustering +3

Finding groups of cross-correlated features in bi-view data

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

A common problem in the exploratory analysis of such data is to identify groups of features of different data types that are strongly associated.

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.

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.

Benchmarking

Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks

1 code implementation3 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 +2

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

Graph Generative Model for Benchmarking Graph Neural Networks

1 code implementation10 Jul 2022 Minji Yoon, Yue Wu, John Palowitch, Bryan Perozzi, Ruslan Salakhutdinov

As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems.

Benchmarking Graph Generation +1

Examining the Effects of Degree Distribution and Homophily in Graph Learning Models

1 code implementation17 Jul 2023 Mustafa Yasir, John Palowitch, Anton Tsitsulin, Long Tran-Thanh, Bryan Perozzi

In this work we examine how two additional synthetic graph generators can improve GraphWorld's evaluation; LFR, a well-established model in the graph clustering literature and CABAM, a recent adaptation of the Barabasi-Albert model tailored for GNN benchmarking.

Benchmarking Graph Clustering +3

Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions

1 code implementation26 Feb 2024 Tzuf Paz-Argaman, Sayali Kulkarni, John Palowitch, Jason Baldridge, Reut Tsarfaty

Current navigation studies concentrate on egocentric local descriptions (e. g., `it will be on your right') that require reasoning over the agent's local perception.

Information Retrieval

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