%4b%69%6a%75%6e%67%20%53%68%69%6e
-
KAIST Predicts Human Group Behavior with AI! 1st Place at the World’s Top Conference… Major Success after 23 Years
<(From Left) Ph.D candidate Geon Lee, Ph.D candidate Minyoung Choe, M.S candidate Jaewan Chun, Professor Kijung Shin, M.S candidate Seokbum Yoon>
KAIST (President Kwang Hyung Lee) announced on the 9th of December that Professor Kijung Shin’s research team at the Kim Jaechul Graduate School of AI has developed a groundbreaking AI technology that predicts complex social group behavior by analyzing how individual attributes such as age and role influence group relationships.
With this technology, the research team achieved the remarkable feat of winning the Best Paper Award at the world-renowned data mining conference “IEEE ICDM,” hosted by the Institute of Electrical and Electronics Engineers (IEEE). This is the highest honor awarded to only one paper out of 785 submissions worldwide, and marks the first time in 23 years that a Korean university research team has received this award, once again demonstrating KAIST’s technological leadership on the global research stage.
Today, group interactions involving many participants at the same time—such as online communities, research collaborations, and group chats—are rapidly increasing across society. However, there has been a lack of technology that can precisely explain both how such group behavior is structured and how individual characteristics influence it at the same time.
To overcome this limitation, Professor Kijung Shin’s research team developed an AI model called “NoAH (Node Attribute-based Hypergraph Generator),” which realistically reproduces the interplay between individual attributes and group structure.
NoAH is an artificial intelligence that explains and imitates what kinds of group behaviors emerge when people’s characteristics come together. For example, it can analyze and faithfully reproduce how information such as a person’s interests and roles actually combine to form group behavior.
As such, NoAH is an AI that generates “realistic group behavior” by simultaneously reflecting human traits and relationships. It was shown to reproduce various real-world group behaviors—such as product purchase combinations in e-commerce, the spread of online discussions, and co-authorship networks among researchers—far more realistically than existing models.
< The process of generating group interactions using NoAH >
Professor Kijung Shin stated, “This study opens a new AI paradigm that enables a richer understanding of complex interactions by considering not only the structure of groups but also individual attributes together,” and added, “Analyses of online communities, messengers, and social networks will become far more precise.”
This research was conducted by a team consisting of Professor Kijung Shin and KAIST Kim Jaechul Graduate School of AI students: master’s students Jaewan Chun and Seokbum Yoon, and doctoral students Minyoung Choe and Geon Lee, and was presented at IEEE ICDM on November 18.
※ Paper title: “Attributed Hypergraph Generation with Realistic Interplay Between Structure and Attributes” Original paper: https://arxiv.org/abs/2509.21838
< Photo from the award ceremony held on November 14 at the International Spy Museum in Washington, D.C.>
Meanwhile, including this award-winning paper, Professor Shin’s research team presented a total of four papers at IEEE ICDM this year. In addition, in 2023, the team also received the Best Student Paper Runner-up (4th place) at the same conference.
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-202400457882, AI Research Hub Project) (RS-2019-II190075, Artificial Intelligence Graduate School Program (KAIST)) (No. RS-2022-II220871, Development of AI Autonomy and Knowledge Enhancement for AI Agent Collaboration).
2025.12.09 View 1778 -
KAIST Develops AI ‘MARIOH’ to Uncover and Reconstruct Hidden Multi-Entity Relationships
<(From Left) Professor Kijung Shin, Ph.D candidate Kyuhan Lee, and Ph.D candidate Geon Lee>
Just like when multiple people gather simultaneously in a meeting room, higher-order interactions—where many entities interact at once—occur across various fields and reflect the complexity of real-world relationships. However, due to technical limitations, in many fields, only low-order pairwise interactions between entities can be observed and collected, which results in the loss of full context and restricts practical use. KAIST researchers have developed the AI model “MARIOH,” which can accurately reconstruct* higher-order interactions from such low-order information, opening up innovative analytical possibilities in fields like social network analysis, neuroscience, and life sciences.
*Reconstruction: Estimating/reconstructing the original structure that has disappeared or was not observed.
KAIST (President Kwang Hyung Lee) announced on the 5th that Professor Kijung Shin’s research team at the Kim Jaechul Graduate School of AI has developed an AI technology called “MARIOH” (Multiplicity-Aware Hypergraph Reconstruction), which can reconstruct higher-order interaction structures with high accuracy using only low-order interaction data.
Reconstructing higher-order interactions is challenging because a vast number of higher-order interactions can arise from the same low-order structure.
The key idea behind MARIOH, developed by the research team, is to utilize multiplicity information of low-order interactions to drastically reduce the number of candidate higher-order interactions that could stem from a given structure.
In addition, by employing efficient search techniques, MARIOH quickly identifies promising interaction candidates and uses multiplicity-based deep learning to accurately predict the likelihood that each candidate represents an actual higher-order interaction.
<Figure 1. An example of recovering high-dimensional relationships (right) from low-dimensional paper co-authorship relationships (left) with 100% accuracy, using MARIOH technology.>
Through experiments on ten diverse real-world datasets, the research team showed that MARIOH reconstructed higher-order interactions with up to 74% greater accuracy compared to existing methods.
For instance, in a dataset on co-authorship relations (source: DBLP), MARIOH achieved a reconstruction accuracy of over 98%, significantly outperforming existing methods, which reached only about 86%. Furthermore, leveraging the reconstructed higher-order structures led to improved performance in downstream tasks, including prediction and classification.
According to Kijung, “MARIOH moves beyond existing approaches that rely solely on simplified connection information, enabling precise analysis of the complex interconnections found in the real world.” Furthermore, “it has broad potential applications in fields such as social network analysis for group chats or collaborative networks, life sciences for studying protein complexes or gene interactions, and neuroscience for tracking simultaneous activity across multiple brain regions.”
The research was conducted by Kyuhan Lee (Integrated M.S.–Ph.D. program at the Kim Jaechul Graduate School of AI at KAIST; currently a software engineer at GraphAI), Geon Lee (Integrated M.S.–Ph.D. program at KAIST), and Professor Kijung Shin. It was presented at the 41st IEEE International Conference on Data Engineering (IEEE ICDE), held in Hong Kong this past May.
※ Paper title: MARIOH: Multiplicity-Aware Hypergraph Reconstruction ※ DOI: https://doi.ieeecomputersociety.org/10.1109/ICDE65448.2025.00233
<Figure 2. An example of the process of recovering high-dimensional relationships using MARIOH technology>
This research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) through the project “EntireDB2AI: Foundational technologies and software for deep representation learning and prediction using complete relational databases,” as well as by the National Research Foundation of Korea through the project “Graph Foundation Model: Graph-based machine learning applicable across various modalities and domains.”
2025.08.05 View 2497