12 citations · 54 across the 12 of their papers we have counts for
12 papers
AugWard: Augmentation-Aware Representation Learning for Accurate Graph Classification
Minjun Kim, Jaehyeon Choi, SeungJoo Lee +2
How can we accurately classify graphs? Graph classification is a pivotal task in data mining with applications in social network analysis, web analysis, drug discovery, molecular p…
Learning Disentangled Representations in Signed Directed Graphs without Social Assumptions
Geonwoo Ko, Jinhong Jung
Signed graphs are complex systems that represent trust relationships or preferences in various domains. Learning node representations in such graphs is crucial for many mining task…
NeuKron: Constant-Size Lossy Compression of Sparse Reorderable Matrices and Tensors
Taehyung Kwon, Jihoon Ko, Jinhong Jung +1
Many real-world data are naturally represented as a sparse reorderable matrix, whose rows and columns can be arbitrarily ordered (e.g., the adjacency matrix of a bipartite graph).…
Time-aware Random Walk Diffusion to Improve Dynamic Graph Learning
Jong-whi Lee, Jinhong Jung
How can we augment a dynamic graph for improving the performance of dynamic graph neural networks? Graph augmentation has been widely utilized to boost the learning performance of…
Accurate Node Feature Estimation with Structured Variational Graph Autoencoder
Jaemin Yoo, Hyunsik Jeon, Jinhong Jung +1
Given a graph with partial observations of node features, how can we estimate the missing features accurately? Feature estimation is a crucial problem for analyzing real-world grap…
Signed Graph Diffusion Network
Jinhong Jung, Jaemin Yoo, U Kang
Given a signed social graph, how can we learn appropriate node representations to infer the signs of missing edges? Signed social graphs have received considerable attention to mod…
T-GAP: Learning to Walk across Time for Temporal Knowledge Graph Completion
Jaehun Jung, Jinhong Jung, U Kang
Temporal knowledge graphs (TKGs) inherently reflect the transient nature of real-world knowledge, as opposed to static knowledge graphs. Naturally, automatic TKG completion has dra…
Fast and Accurate Pseudoinverse with Sparse Matrix Reordering and Incremental Approach
Jinhong Jung, Lee Sael
How can we compute the pseudoinverse of a sparse feature matrix efficiently and accurately for solving optimization problems? A pseudoinverse is a generalization of a matrix invers…
Zoom-SVD: Fast and Memory Efficient Method for Extracting Key Patterns in an Arbitrary Time Range
Jun-Gi Jang, Dongjin Choi, Jinhong Jung +1
Given multiple time series data, how can we efficiently find latent patterns in an arbitrary time range? Singular value decomposition (SVD) is a crucial tool to discover hidden fac…
Supervised and Extended Restart in Random Walks for Ranking and Link Prediction in Networks
Woojeong Jin, Jinhong Jung, U Kang
Given a real-world graph, how can we measure relevance scores for ranking and link prediction? Random walk with restart (RWR) provides an excellent measure for this and has been ap…
A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems
Haekyu Park, Jinhong Jung, U Kang
Between matrix factorization or Random Walk with Restart (RWR), which method works better for recommender systems? Which method handles explicit or implicit feedback data better? D…
TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs
Minji Yoon, Jinhong Jung, U Kang
Given a large graph, how can we determine similarity between nodes in a fast and accurate way? Random walk with restart (RWR) is a popular measure for this purpose and has been exp…