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2019 Fiscal Year Research-status Report

Machine Learning on Large Graphs

Research Project

Project/Area Number 18K11434
Research InstitutionKyoto University

Principal Investigator

NGUYEN Canh・Hao  京都大学, 化学研究所, 講師 (90626889)

Project Period (FY) 2018-04-01 – 2021-03-31
Keywordslarge graph / graph Laplacian / hypergraph / sparsistency
Outline of Annual Research Achievements

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.

Current Status of Research Progress
Current Status of Research Progress

2: Research has progressed on the whole more than it was originally planned.

Reason

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.

Strategy for Future Research Activity

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.

Causes of Carryover

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.

  • Research Products

    (4 results)

All 2020 2019

All Journal Article (2 results) (of which Int'l Joint Research: 2 results,  Peer Reviewed: 2 results) Presentation (2 results) (of which Int'l Joint Research: 2 results)

  • [Journal Article] Learning on Hypergraphs with Sparsity2020

    • Author(s)
      Nguyen Hao Canh、Mamitsuka Hiroshi
    • Journal Title

      IEEE Transactions on Pattern Analysis and Machine Intelligence

      Volume: 1 Pages: 1-1

    • DOI

      https://doi.org/10.1109/TPAMI.2020.2974746

    • Peer Reviewed / Int'l Joint Research
  • [Journal Article] A survey on adverse drug reaction studies: data, tasks and machine learning methods2019

    • Author(s)
      Nguyen Duc Anh、Nguyen Canh Hao、Mamitsuka Hiroshi
    • Journal Title

      Briefings in Bioinformatics

      Volume: 1 Pages: 1-1

    • DOI

      https://doi.org/10.1093/bib/bbz140

    • Peer Reviewed / Int'l Joint Research
  • [Presentation] Multiplicative Sparse Feature Decomposition for Efficient Multi-View Multi-Task Learning2019

    • Author(s)
      Sun Lu、Nguyen Canh Hao、Mamitsuka Hiroshi
    • Organizer
      Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)
    • Int'l Joint Research
  • [Presentation] Fast and Robust Multi-View Multi-Task Learning via Group Sparsity2019

    • Author(s)
      Sun Lu、Nguyen Canh Hao、Mamitsuka Hiroshi
    • Organizer
      Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)
    • Int'l Joint Research

URL: 

Published: 2021-01-27  

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