| 研究課題/領域番号 |
18K11434
|
|---|
| 研究機関 | 京都大学 |
|---|
研究代表者 |
|
|---|
| 研究期間 (年度) |
2018-04-01 – 2021-03-31
|
| キーワード | large graph / graph Laplacian / hypergraph / sparsistency |
|---|
| 研究実績の概要 |
The target of the research is to derive statistically sound models to learn from a large graphs, and its related extensions and applications. In this year, we have discovered a statistically sound model to learn from an extension of hypergraph. That is the set of nodes with more than two-way relationships among them. Previous works are not sound when the sizes of hyperedges go to infinity. This is, realistic in large hypergraphs under reasonable assumption.
|
| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We have found promising result surrounding the main topics of learning on large graphs, with applications. We still continue to look for central results on large graphs.
|
| 今後の研究の推進方策 |
In this year, we plan to continue to work on the target of learning on large graphs with more general semantics of graphs, and their applications in Bioinformatics such as biological networks, molecular graphs and so on.
|
| 次年度使用額が生じた理由 |
We could not spend the budget on business trips this year as planned due to the lack of activities in our side. We will continue more research activities this year that will use the budget.
|
