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Murata Lab
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The lab does artificial intelligence research on discovering knowledge from the structure of networks.
AboutAbout the lab
Discovering knowledge from networks.
The Murata Lab researches artificial intelligence, particularly network science, machine learning, and Web mining.
The lab focuses on the structure of networks, which represent the connections between things, and works on discovering knowledge from that structure.
The lab studies deep learning methods for graph-structured data, such as graph neural networks.
From information on the Internet, the lab also discovers related elements, determines the importance of elements, and estimates future connections.
Key terms
- Network science
- A field that represents connections between things as points and lines and investigates the properties hidden in that structure
- Graph neural network
- A deep learning mechanism that can learn from data in the form of points connected by lines, together with the connections themselves
- Community detection
- Finding groups of elements within a network that are strongly connected to one another
NewsLab news
Paper co-authored with researchers in Japan and abroad accepted at the international conference CIKM 2026
A graduate's research-meeting presentation wins the Encouragement Award for Research Meetings from the Japanese Society for Artificial Intelligence
Paper with a former visiting exchange student accepted at the international conference KDD 2025
Paper co-authored with researchers in Japan and abroad accepted in Appl. Netw. Sci.
A graduate's research-meeting presentation wins the Excellence Award for Research Meetings from the Japanese Society for Artificial Intelligence
ResearchResearch themes
The lab researches artificial intelligence, especially network science, machine learning, and Web mining. Through intelligent processing of network structure, it aims to discover knowledge from within connections.
*Illustrative imageDeep Learning
The lab studies deep learning on graph-structured data. Its work includes learning on graphs with missing features, fast approximation of node importance (centrality), community detection, and dictionary-based cross-lingual text classification.
*Illustrative imageLink Mining
Focusing on the network structure of online information, the lab discovers related elements (community detection), determines the importance of elements (ranking, spam detection), and estimates future network structure (link prediction).
*Illustrative imageWeb Usage Mining
The lab researches how to extract users' interests from the data generated when they access the Web, such as access logs, bookmarks, and search terms.
MembersFaculty
The Murata Lab's members consist of faculty, doctoral, master's, and undergraduate students, and assistant staff.As of 2026
- ProfessorTsuyoshi MurataPublications ↗
- Assistant ProfessorYin Jun PhuaPublications ↗
- Assistant ProfessorNorimasa KobayashiPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Spider web Luc Viatour” Luc Viatour / CC BY-SA 3.0 (cropped)
- Deep Learning: “Social Network Analysis Visualization” Martin Grandjean / CC BY-SA 3.0 (cropped)
- Link Mining: “SocialNetworkAnalysis” Martin Grandjean / CC BY-SA 4.0 (cropped)
- Web Usage Mining: “Wikipedia Screenshot User Cyberpower678” Sebastian Wallroth / CC BY 4.0 (cropped)
- Related lab (石田研究室): “4xhe pinnatoxin pentamer” Opabinia regalis / CC BY-SA 4.0 (cropped)
- Related lab (吉野研究室): “Close up view of a microphone set up in a recording studio” Shixart1985 / CC BY 2.0 (cropped)
- Related lab (瀧ノ上正浩研究室): “2T7A4” Yma3591 / CC BY 4.0 (cropped)
- Related lab (岡崎研究室): “Birthdaytagcloud” User:Killiondude / Public domain (cropped)
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