126 citations · 341 across the 10 of their papers we have counts for
11 papers
Fairness-Aware Graph Neural Networks: A Survey
April Chen, Ryan A. Rossi, Namyong Park +6
Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks.…
MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning
Namyong Park, Ryan Rossi, Nesreen Ahmed +1
Given a graph learning task, such as link prediction, on a new graph, how can we select the best method as well as its hyperparameters (collectively called a model) without having…
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Namyong Park, Ryan Rossi, Eunyee Koh +5
Given entities and their interactions in the web data, which may have occurred at different time, how can we find communities of entities and track their evolution? In this paper,…
EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs
Namyong Park, Fuchen Liu, Purvanshi Mehta +3
How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestam…
Knowledge-Guided Dynamic Systems Modeling: A Case Study on Modeling River Water Quality
Namyong Park, MinHyeok Kim, Nguyen Xuan Hoai +3
Modeling real-world phenomena is a focus of many science and engineering efforts, such as ecological modeling and financial forecasting, to name a few. Building an accurate model f…
J-Recs: Principled and Scalable Recommendation Justification
Namyong Park, Andrey Kan, Christos Faloutsos +1
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 user…
MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals
Namyong Park, Andrey Kan, Xin Luna Dong +2
Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? Node importance estimation is a crucial and challenging task that can benefit a lot of app…
Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos
Byungsoo Jeon, Namyong Park
This paper addresses a key challenge in MOOC dropout prediction, namely to build meaningful representations from clickstream data. While a variety of feature extraction techniques…
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning
Byungsoo Jeon, Namyong Park, Seojin Bang
Massive Open Online Courses (MOOCs) have become popular platforms for online learning. While MOOCs enable students to study at their own pace, this flexibility makes it easy for st…
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
Namyong Park, Andrey Kan, Xin Luna Dong +2
How can we estimate the importance of nodes in a knowledge graph (KG)? A KG is a multi-relational graph that has proven valuable for many tasks including question answering and sem…
Scalable Tucker Factorization for Sparse Tensors - Algorithms and Discoveries
Sejoon Oh, Namyong Park, Lee Sael +1
Given sparse multi-dimensional data (e.g., (user, movie, time; rating) for movie recommendations), how can we discover latent concepts/relations and predict missing values? Tucker…