25 citations · 105 across the 16 of their papers we have counts for
8 papers · 1 filter
Local Vertex Colouring Graph Neural Networks
Shouheng Li, Dongwoo Kim, Qing Wang
In recent years, there has been a significant amount of research focused on expanding the expressivity of Graph Neural Networks (GNNs) beyond the Weisfeiler-Lehman (1-WL) framework…
Graph Generation with -trees
Yunhui Jang, Dongwoo Kim, Sungsoo Ahn
Generating graphs from a target distribution is a significant challenge across many domains, including drug discovery and social network analysis. In this work, we introduce a nove…
Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering
Shouheng Li, Dongwoo Kim, Qing Wang
While a growing body of literature has been studying new Graph Neural Networks (GNNs) that work on both homophilic and heterophilic graphs, little has been done on adapting classic…
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs
Sean Li, Dongwoo Kim, Qing Wang
Graph neural networks (GNNs) have been extensively studied for prediction tasks on graphs. As pointed out by recent studies, most GNNs assume local homophily, i.e., strong similari…
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Dongwoo Kim, Christian Walder
Prediction suffix trees (PST) provide an effective tool for sequence modelling and prediction. Current prediction techniques for PSTs rely on exact matching between the suffix of t…
Hierarchical Dirichlet Scaling Process
Dongwoo Kim, Alice Oh
We present the \textit{hierarchical Dirichlet scaling process} (HDSP), a Bayesian nonparametric mixed membership model. The HDSP generalizes the hierarchical Dirichlet process (HDP…
Dirichlet Process with Mixed Random Measures: A Nonparametric Topic Model for Labeled Data
Dongwoo Kim, Suin Kim, Alice Oh
We describe a nonparametric topic model for labeled data. The model uses a mixture of random measures (MRM) as a base distribution of the Dirichlet process (DP) of the HDP framewor…
Variable Selection for Latent Dirichlet Allocation
Dongwoo Kim, Yeonseung Chung, Alice Oh
In latent Dirichlet allocation (LDA), topics are multinomial distributions over the entire vocabulary. However, the vocabulary usually contains many words that are not relevant in…