25 citations · 105 across the 16 of their papers we have counts for
16 papers
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…
word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs
Yo Joong Choe, Kyubyong Park, Dongwoo Kim
We present word2word, a publicly available dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. Our dataset…
Analysing user identity via time-sensitive semantic edit distance (t-SED): A case study of Russian trolls on Twitter
Dongwoo Kim, Timothy Graham, Zimin Wan +1
In the digital era, individuals are increasingly profiled and grouped based on the traces they leave behind in online social networks such as Twitter and Facebook. In this paper, w…
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…
Neural Dynamic Programming for Musical Self Similarity
Christian J. Walder, Dongwoo Kim
We present a neural sequence model designed specifically for symbolic music. The model is based on a learned edit distance mechanism which generalises a classic recursion from comp…
PathRec: Visual Analysis of Travel Route Recommendations
Dawei Chen, Dongwoo Kim, Lexing Xie +5
We present an interactive visualisation tool for recommending travel trajectories. This system is based on new machine learning formulations and algorithms for the sequence recomme…
Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora
Jooyeon Kim, Dongwoo Kim, Alice Oh
Much of scientific progress stems from previously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of schola…
Computer Assisted Composition with Recurrent Neural Networks
Christian Walder, Dongwoo Kim
Sequence modeling with neural networks has lead to powerful models of symbolic music data. We address the problem of exploiting these models to reach creative musical goals, by com…
Probabilistic Knowledge Graph Construction: Compositional and Incremental Approaches
Dongwoo Kim, Lexing Xie, Cheng Soon Ong
Knowledge graph construction consists of two tasks: extracting information from external resources (knowledge population) and inferring missing information through a statistical an…
Hierarchical learning of grids of microtopics
Nebojsa Jojic, Alessandro Perina, Dongwoo Kim
The counting grid is a grid of microtopics, sparse word/feature distributions. The generative model associated with the grid does not use these microtopics individually. Rather, it…
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…