Telecommunications
Social network analysis, Material discovery, Urban planning

Efficient and accurate graph data analytics
Multimedia Engineering, Graduate School of Information Science and Technology
Assistant Professor Yuya Sasaki
https://researchmap.jp/sasakiyuya?lang=en

Telecommunications

Abstract
Graph data is a data structure that represents the states of objects and relationships between objects. We utilize graph data invarious applications, such as recommendations on Web shopping
and new material discovery on catalysts and crystals (Figure 1).
For such diverse and large-scale graphs, I study (1) efficient management and fast search techniques on graph databases, (2) data
mining techniques to find new knowledge, and (3) neural networks
for accurate prediction (Figure 2).

develop a platform to support easy analysis and management ofgraph data, and promote the development of technology that canefficiently and accurately analyze large-scale and diverse relationships in the past, present, and future. I contribute to the realizationof a more comfortable and happier world.

▍ Background & Results
Relationships between objects can be modeled by graphs, andgraphs are widely utilized in our daily life applications. For example, knowledge graphs (structured knowledge of relationships between objects) are utilized in Web searches (e.g., Google searchshows tables on the right side when searching for a certain person)and recommendation systems (e.g., related products on Amazon
Web shopping). Molecular data represent atoms and the connections between atoms, and we can use them to search for moleculeswith the same structure. Other applications, such as social network
services and road networks, are closely related to our daily lives,and the graphs are becoming larger and more diverse.
I study technology for diverse and large-scale graphs from four
points of view: management, search, discovery, and prediction.
More concretely, I develop (1) database technology that enables
efficient management and fast search on graph data, (2) data mining technology to discover new knowledge, and (3) deep learningtechnology for accurate predictions (Figure 2).
To my research results, first, we have achieved a speedup of
more than 1000 times faster than queries in graph databases byprecomputing the structural features of graph data. Second, wedeveloped techniques that can discover exceptional facts andassess discriminatory biases in graph data. Third, we have developed deep learning techniques that can be applied to large-scalegraph data with small memory usage and fast learning while keeping high accuracy. Furthermore, in addition to basic development,
we are also applying these graph management and analysis techniques to applied fields such as material discovery and medical AI
by collaborating with researchers in other fields.

Figure 1. Examples of graph data▍ Significance of the research and Future perspectiveWe are in the Big-data era, and thus technology for efficiently
handling large and diverse data is becoming more and more important. Our lives are closely intertwined with various relationships,
such as human relationships, jobs, objects owned by people, access to the Web, and agreements between nations. These relationships are converted into graph data. Thus, for understanding anddeveloping the world, it is necessary to predict the state of objects
from these relationships and relationships from the state of objects,
manage graph data and discover new knowledge from them. I will

Figure 2. Examples of graph analysis

P a t e n t
Maekawa, Seiji; Noda, Koki; Sasaki, Yuya et al. Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNs.
Conference on Neural Information Processing Systems (NeurIPS), Nov. 2022. doi: 10.48550/arXiv.2206.09144
Sasaki Yuya. Cost-constrained Minimal Steiner Tree Enumeration. Proceedings of International Conference on Information and Knowledge Management
Tr e a t i s e(CIKM), pp. 4439-4443, Oct. 2022. doi: 10.1145/3511808.3557570
Sasaki Yuya, George Fletcher, and Onizuka Makoto. Language-aware Indexing for Conjunctive Path Queries. Proceedings of International Conference on
Data Engineering (ICDE), pp. 661-673, May 2022. doi: 10.1109/ICDE53745.2022.00054
URLK e y w o r d graph neural network, big data, data mining, database