Name

Junichiro Mori, Associate Professor

Location

Research Area

Hongo

Large-scale Graph Mining

Graph Mining Approach for Large-Scale Data Analysis
Our research group is studying graph mining methods for large-scale data analysis and their social applications. As the basis of largescale graph mining, we are currently developing several embedding methods on a large-scale graph, which potentially gives a vector
representation of each node or the entire graph to solve machine learning tasks on a graph originated from large-scale data. We arealso applying our methods to developing systems which would solve several social issues. Those applications include mining largescale scholarly data, supply chain network data, human mobility data. Employing the graph mining-based methods and their applications
for large-scale data analysis, we are aiming at supporting human decision making. Our research outcomes have been widely used forevidence-based policy making and strategic planning in research and development. Our research group is also working on educational
contents and tutoring systems for student learning in informatics. We welcome those who try to tackle issues with their creative ideas.

Network Representation Learning
The structure of a network or graph is generally comprised of
set of nodes and edges, in which each node represents an entityand each edge represents a relation between nodes. According
to recent representation learning approaches in deep learning,
we are working on developing several embedding methods on alarge-scale graph, which potentially gives a vector representationof each node or the entire graph to solve machine learning tasks
on a graph originated from large-scale data.

Mining Large-scale Graph DataMining Large-scale Scholarly Data
Big scholarly data includes a large amount of academic papers
and their citation networks. We are developing the novel system
which automatically analyzes big scholarly data, including large
citation networks, to identify current research trends and predict
future emerging technologies in the research fields. The system
is currently used in several applications: technology road
mapping and horizontal scanning for science and technology
policy, strategic planning in research and development.

Figure 1: Network Representation Learning Methods

Mining Large-scale Supply Chain NetworkAiming at designing a supply chain network, supporting small
and medium-sized enterprises (SMEs), and promoting regional
economy in Japan, we are developing the method to analyze alarge-scale supply chain data with a network analysis approach.
We are also developing the system for SMEs to find theirbusiness partners. Our study has contributed to regional
revitalization in the disaster area of the Great East Japan
Earthquake by supporting evidence-based policy making.
Figure 2: Mining Large-scale Scholarly Data

Mining Large-scale Human Mobility Data
We are collaborating with public and private sectors which
operate public transportation systems in a metropolitan area. Weare developing the method to analyze large-scale human mobility
data from the public transportation systems to help the sectors
design and operate their systems effectively. The key idea is togenerate a human mobility graph from the data and apply ourgraph mining methods. We are also conducting socialexperiments to implement our data-driven improvement plan.

Mathematics and Informatics Education
We are designing a curriculum for learning mathematics andinformatics in higher and recurrent education, which arefoundations for recent data science and artificial intelligencetechnologies. We are also developing educational contents andtutoring systems for student learning in the disciplinesFF （石(T南El (東(株)青アF(い石(F (東 C((北(C(新遠石Con(((株(Figure 3: Mining Large-scale Supply Chain Network

Contact: Junichiro Mori (mori@mi.u-tokyo.ac.jp)