no code implementations • 16 Mar 2024 • Namyong Park, Xing Wang, Antoine Simoulin, Shuai Yang, Grey Yang, Ryan Rossi, Puja Trivedi, Nesreen Ahmed
To address these limitations, the forward-forward algorithm (FF) was recently proposed as an alternative to BP in the image classification domain, which trains NNs by performing two forward passes over positive and negative data.
no code implementations • 29 Nov 2023 • Puja Trivedi, Ryan Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, Danai Koutra
Most real-world networks are noisy and incomplete samples from an unknown target distribution.
no code implementations • 8 Jul 2023 • April Chen, Ryan A. Rossi, Namyong Park, Puja Trivedi, Yu Wang, Tong Yu, Sungchul Kim, Franck Dernoncourt, Nesreen K. Ahmed
In this article, we examine and categorize fairness techniques for improving the fairness of GNNs.
1 code implementation • 18 Jun 2022 • Namyong Park, Ryan Rossi, Nesreen Ahmed, Christos Faloutsos
In this work, we develop the first meta-learning approach for evaluation-free graph learning model selection, called MetaGL, which utilizes the prior performances of existing methods on various benchmark graph datasets to automatically select an effective model for the new graph, without any model training or evaluations.
1 code implementation • 5 Apr 2022 • Namyong Park, Ryan Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen Ahmed, Christos Faloutsos
Especially, deep graph clustering (DGC) methods have successfully extended deep clustering to graph-structured data by learning node representations and cluster assignments in a joint optimization framework.
1 code implementation • 15 Feb 2022 • Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, Yuxiao Dong
How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)?
no code implementations • 1 Mar 2021 • Namyong Park, MinHyeok Kim, Nguyen Xuan Hoai, R. I., McKay, Dong-Kyun Kim
Modeling real-world phenomena is a focus of many science and engineering efforts, such as ecological modeling and financial forecasting, to name a few.
no code implementations • 11 Nov 2020 • Namyong Park, Andrey Kan, Christos Faloutsos, Xin Luna Dong
Online recommendation is an essential functionality across a variety of services, including e-commerce and video streaming, where items to buy, watch, or read are suggested to users.
no code implementations • 22 Jun 2020 • Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts.
no code implementations • 5 Feb 2020 • Byungsoo Jeon, Namyong Park
This paper addresses a key challenge in MOOC dropout prediction, namely to build meaningful representations from clickstream data.
no code implementations • 5 Feb 2020 • Byungsoo Jeon, Namyong Park, Seojin Bang
Massive Open Online Courses (MOOCs) have become popular platforms for online learning.
no code implementations • 21 May 2019 • Namyong Park, Andrey Kan, Xin Luna Dong, Tong Zhao, Christos Faloutsos
How can we estimate the importance of nodes in a knowledge graph (KG)?