23 citations · 75 across the 18 of their papers we have counts for
18 papers
Criteria-Aware Graph Filtering: Extremely Fast Yet Accurate Multi-Criteria Recommendation
Jin-Duk Park, Jaemin Yoo, Won-Yong Shin
Multi-criteria (MC) recommender systems, which utilize MC rating information for recommendation, are increasingly widespread in various e-commerce domains. However, the MC recommen…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…
Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities
Leman Akoglu, Jaemin Yoo
Self-supervised learning (SSL) is a growing torrent that has recently transformed machine learning and its many real world applications, by learning on massive amounts of unlabeled…
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection
Jaemin Yoo, Yue Zhao, Lingxiao Zhao +1
Self-supervised learning (SSL) has proven effective in solving various problems by generating internal supervisory signals. Unsupervised anomaly detection, which faces the high cos…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
Classification of Edge-dependent Labels of Nodes in Hypergraphs
Minyoung Choe, Sunwoo Kim, Jaemin Yoo +1
A hypergraph is a data structure composed of nodes and hyperedges, where each hyperedge is an any-sized subset of nodes. Due to the flexibility in hyperedge size, hypergraphs repre…
Towards Deep Attention in Graph Neural Networks: Problems and Remedies
Soo Yong Lee, Fanchen Bu, Jaemin Yoo +1
Graph neural networks (GNNs) learn the representation of graph-structured data, and their expressiveness can be further enhanced by inferring node relations for propagation. Attent…
Self-Tuning Self-Supervised Image Anomaly Detection
Jaemin Yoo, Lingxiao Zhao, Leman Akoglu
Self-supervised learning (SSL) has emerged as a promising paradigm that presents supervisory signals to real-world problems, bypassing the extensive cost of manual labeling. Conseq…
How Transitive Are Real-World Group Interactions? -- Measurement and Reproduction
Sunwoo Kim, Fanchen Bu, Minyoung Choe +2
Many real-world interactions (e.g., researcher collaborations and email communication) occur among multiple entities. These group interactions are naturally modeled as hypergraphs.…
NetEffect: Discovery and Exploitation of Generalized Network Effects
Meng-Chieh Lee, Shubhranshu Shekhar, Jaemin Yoo +1
Given a large graph with few node labels, how can we (a) identify whether there is generalized network-effects (GNE) or not, (b) estimate GNE to explain the interrelations among no…
Reciprocity in Directed Hypergraphs: Measures, Findings, and Generators
Sunwoo Kim, Minyoung Choe, Jaemin Yoo +1
Group interactions are prevalent in a variety of areas. Many of them, including email exchanges, chemical reactions, and bitcoin transactions, are directional, and thus they are na…
Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining
Jaemin Yoo, Meng-Chieh Lee, Shubhranshu Shekhar +1
How can we solve semi-supervised node classification in various graphs possibly with noisy features and structures? Graph neural networks (GNNs) have succeeded in many graph mining…
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of Success
Jaemin Yoo, Tiancheng Zhao, Leman Akoglu
Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals to real-world problems, avoiding the extensive cost of manual labeling. SSL is p…
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…
Transition Matrix Representation of Trees with Transposed Convolutions
Jaemin Yoo, Lee Sael
How can we effectively find the best structures in tree models? Tree models have been favored over complex black box models in domains where interpretability is crucial for making…
Model-Agnostic Augmentation for Accurate Graph Classification
Jaemin Yoo, Sooyeon Shim, U Kang
Given a graph dataset, how can we augment it for accurate graph classification? Graph augmentation is an essential strategy to improve the performance of graph-based tasks, and has…
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…
Efficient Learning of Bounded-Treewidth Bayesian Networks from Complete and Incomplete Data Sets
Mauro Scanagatta, Giorgio Corani, Marco Zaffalon +2
Learning a Bayesian networks with bounded treewidth is important for reducing the complexity of the inferences. We present a novel anytime algorithm (k-MAX) method for this task, w…