Akihiro Inokuchi

Proceedings of the Database and Expert Systems Applications, 2024

2023

The Third Common Interface for Graph Neural Networks.

[BibT_eX]

[DOI]

Jun Kato

Proceedings of the International Joint Conference on Neural Networks, 2023

2022

Why Deeper Graph Neural Network Performs Worse? Discussion and Improvement About Deep GNNs.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

2021

Similar Supergraph Search Based on Graph Edit Distance.

[BibT_eX]

[DOI]

Masataka Yamada

Algorithms, 2021

2020

Efficient Supergraph Search Using Graph Coding.

[BibT_eX]

[DOI]

Shun Imai

IEICE Trans. Inf. Syst., 2020

Refining Similarity Matrices to Cluster Attributed Networks Accurately.

[BibT_eX]

[DOI]

CoRR, 2020

Refining Similarity Matrices to Cluster Attributed Networks Accurately<sup>**</sup>.

[BibT_eX]

[DOI]

Proceedings of the Eighth International Symposium on Computing and Networking Workshops, 2020

2018

Accurate and Fast Computation of Approximate Graph Edit Distance based on Graph Relabeling.

[BibT_eX]

[DOI]

Sousuke Takami

Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods, 2018

2017

Graph Classification with Mapping Distance Graph Kernels.

[BibT_eX]

[DOI]

Eimi Shiotsuki

Proceedings of the Pattern Recognition Applications and Methods, 2017

Mapping Distance Graph Kernels using Bipartite Matching.

[BibT_eX]

[DOI]

Eimi Shiotsuki

Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017

Transition-based dependency parser with postponed determinations for Japanese sentences.

[BibT_eX]

[DOI]

Xiaobo Xi

Proceedings of the 2017 International Conference on Asian Language Processing, 2017

2016

Experimental Evaluation of Graph Classification with Hadamard Code Graph Kernels.

[BibT_eX]

[DOI]

Proceedings of the Pattern Recognition Applications and Methods, 2016

Hadamard Code Graph Kernels for Classifying Graphs.

[BibT_eX]

[DOI]

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, 2016

Detecting Smooth Cluster Changes in Evolving Graphs.

[BibT_eX]

[DOI]

Sohei Okui

Kaho Osamura

Proceedings of the 15th IEEE International Conference on Machine Learning and Applications, 2016

2012

FRISSMiner: Mining Frequent Graph Sequence Patterns Induced by Vertices.

[BibT_eX]

[DOI]

IEICE Trans. Inf. Syst., 2012

Efficient Graph Sequence Mining Using Reverse Search.

[BibT_eX]

[DOI]

Hiroaki Ikuta

IEICE Trans. Inf. Syst., 2012

Mining Rules for Rewriting States in a Transition-Based Dependency Parser.

[BibT_eX]

[DOI]

Proceedings of the PRICAI 2012: Trends in Artificial Intelligence, 2012

Mining Rules for Rewriting States in a Transition-based Dependency Parser for English.

[BibT_eX]

[DOI]

Ayumu Yamaoka

Proceedings of the COLING 2012, 2012

2011

GTRACE-RS: Efficient Graph Sequence Mining using Reverse Search

[BibT_eX]

[DOI]

Hiroaki Ikuta

CoRR, 2011

2010

GTRACE: Mining Frequent Subsequences from Graph Sequences.

[BibT_eX]

[DOI]

IEICE Trans. Inf. Syst., 2010

Mining Frequent Graph Sequence Patterns Induced by Vertices.

[BibT_eX]

[DOI]

Proceedings of the SIAM International Conference on Data Mining, 2010

GTRACE2: Improving Performance Using Labeled Union Graphs.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2010

Graph Classification Based on Optimizing Graph Spectra.

[BibT_eX]

[DOI]

Nguyen Duy Vinh

Proceedings of the Discovery Science - 13th International Conference, 2010

2008

Regression with interval output values.

[BibT_eX]

[DOI]

Proceedings of the 19th International Conference on Pattern Recognition (ICPR 2008), 2008

A Fast Method to Mine Frequent Subsequences from Graph Sequence Data.

[BibT_eX]

[DOI]

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

2007

MedTAKMI-CDI: Interactive knowledge discovery for clinical decision intelligence.

[BibT_eX]

[DOI]

IBM Syst. J., 2007

A method for online analytical processing of text data.

[BibT_eX]

[DOI]

Koichi Takeda

Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, 2007

2005

A General Framework for Mining Frequent Subgraphs from Labeled Graphs.

[BibT_eX]

[DOI]

Fundam. Informaticae, 2005

2004

Text analytics for life science using the Unstructured Information Management Architecture.

[BibT_eX]

[DOI]

L. Venkata Subramaniam

IBM Syst. J., 2004

Mining Generalized Substructures from a Set of Labeled Graphs.

[BibT_eX]

[DOI]

Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 2004

2003

Complete Mining of Frequent Patterns from Graphs: Mining Graph Data.

[BibT_eX]

[DOI]

Mach. Learn., 2003

Marginalized Kernels Between Labeled Graphs.

[BibT_eX]

[DOI]

Hisashi Kashima

Koji Tsuda

Proceedings of the Machine Learning, 2003

Mining Significant Pairs of Patterns from Graph Structures with Class Labels.

[BibT_eX]

[DOI]

Hisashi Kashima

Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 2003

2000

An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data.

[BibT_eX]

[DOI]

Proceedings of the Principles of Data Mining and Knowledge Discovery, 2000

1999

Basket Analysis for Graph Structured Data.

[BibT_eX]

[DOI]

Proceedings of the Methodologies for Knowledge Discovery and Data Mining, 1999

Derivation of the Topology Structure from Massive Graph Data.

[BibT_eX]

[DOI]