A pamphlet of our lab is available (
English version and
Japanese version). (2023.1.11)
In-person open laboratory will be held on the fifth floor, West 8E building of Ookayama campus from 13:00 to 15:00 on Feb. 20(Mon).
This is mainly for the second and the third grade of Tokyo Tech undergraduate students, but other students can also join.
The place of our laboratory is shown
here.
Date and time of all other open laboratories are available in
this site. (2023.1.13)
A paper (Title:
"Strengthening Robustness under Adversarial Attacks using Brain Visual Codes") written by Ms. Zarina Rakhimberdina (3rd grade of doctoral student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted by
IEEE Access (impact factor : 3.476). (2022.9.5)
A paper (Title:
"Not All Neighbors are Friendly: Learning to Choose Hop Features to Improve Node Classification") written by Mr. Sunil Kumar Maurya (third grade of doctoral course student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted as a short paper of
CIKM 2022. (2022.8.2)
Murata published a book titled "Graph Neural Networks: implementation with PyTorch" (
Ohmsha (publisher) /
Amazon /
additional information). (2022.7.20)
Admission:
If you want to apply for
Tokyo Tech IGP(C),
please read
this site, and send the following documents to
Murata by email: (i) your CV, (ii) your grade transcript(s), (iii) research proposal, (iv) list of publications,
and (v) your thesis for graduating master/undergraduate course (if it is not written in English, please send any English technical papers/documents written by you).
We will think about the possibilities of accepting you based on these documents. We will accept extraordinary excellent applicants only.
Web site of the briefing sessions this year is as follows (written in Japanese).
https://www.li.c.titech.ac.jp/admission/2022.html
"Graduate School Admissions FAQ" is in the following site.
https://www.titech.ac.jp/english/admissions/prospective-students/admissions/faq
Application Guideline of Tokyo Tech Graduate School is available online (for the applicants of master course). Doctor course applicants have to request printed version of the guideline.
https://www.titech.ac.jp/english/graduate_school/admissions/guide.html
As mentioned in the guideline, please check the following "Admission Update" frequently. The guideline may subject to change because of COVID-19.
https://www.titech.ac.jp/english/entrance_information/
Master course applicants who wish to join Murata laboratory
do not have to send emails in advance. (except Tokyo Tech IGP(C) applicants)
Doctor course applicants who wish to join Murata laboratory
have to send the following documents by email in advance: (i) your CV, (ii) your grade transcript(s), (iii) research proposal, (iv) list of publications, and (v) your thesis for graduating master/undergraduate course (if it is not written in English, please send any English technical papers/documents written by you).
Master / doctor course applicants who still have no English proficiency test are strongly requested to take either "TOEIC Listening & Reading Test" or "TOEFL iBT Home Edition" as soon as possible.
https://www.iibc-global.org/english/toeic/test/lr.html
https://www.toefl-ibt.jp/test_takers/at-home.html (written in Japanese)
(2022.4.12)
A paper (Title:"Leaping Through Time with Gradient-based Adaptation for Recommendation") written by Mr. Nuttapong Chairatanakul (3rd grade of doctoral students), Dr. Liu Xin (AIRC, AIST) and Murata is accepted by
AAAI 2022. (2021.12.1)
A paper (Title:
"CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions") written by Mr. Quentin Jodelet (2nd grade of doctoral student) is accepted by
Artificial Intelligence. (2021.11.26)
A paper (Title:
"Natural Image Reconstruction from fMRI using Deep Learning: A Survey") written by Ms. Zarina Rakhimberdina (3rd grade of doctoral student), Mr. Quentin Jodelet (2nd grade of doctoral student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted by
Frontiers in Neuroscience, section Brain Imaging Methods. (2021.11.24)
Murata gave a talk as an invited speaker of
International Conference on Data Science and Official Statistics (ICDSOS 2021). (
slides) (2021.11.13)
Murata gave an online lecture to the students of
Maebashi High School in Gunma Prefecture (Title: "Understanding friendship and infectious disease spread in networks"). (
slides) (2021.11.9)
Graduate School Admissions FAQ is available in the following site.
https://www.titech.ac.jp/english/graduate_school/admissions/faq.html
Murata gave a talk as an invited speaker of
FAN 2021 Online (Title: "Graph Neural Networks"). (
slides) (2021.9.21)
A paper (Title:"Cross-lingual Transfer for Text Classification with Dictionary-based Heterogeneous Graph") written by Mr. Nuttapong Chairatanakul (3rd grade of doctoral student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted by
EMNLP 2021 as Findings of EMNLP. (2021.8.26)
Murata delivered an online lecture for
study of university departments at Toshin preparatory school. (2021.8.7)
A paper (Title:
"PGRA: Projected Graph Relation-Feature Attention Network for Heterogeneous Information Network Embedding") written by Mr. Nuttapong Chairatanakul (3rd grade of doctoral student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted by
Information Sciences (impact factor : 5.910). (2021.4.21)
Murata and other 16 professors have received
Education Award of Tokyo Tech (2019) for the implementation of
Progressive Graduate Minor in Data Science and Artificial Intelligence. (2021.3.2)
Murata's dialogue at
Mirai Dining appeared in
TechTech, Tokyo Tech PR magazine (written in Japanese). It also appeared in
Prospective Students of Tokyo Tech Web site. (2021.1.6)
A paper (Title:
"Predicting Emergency Medical Service Demand with Bipartite Graph Convolutional Networks") written by Mr. Ruidong Jin (1st grade of doctoral student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted by
IEEE Access (impact factor : 3.745). (2021.1.5)
Murata gave a lightening talk (Title:"Network Analysis and Visualization by Google Colaboratory") at
Data Visualization meetup 2020. (
slides) (2020.12.28)
A paper (Title:
"Graph Neural Networks for Fast Node Ranking Approximation") written by Mr. Sunil Kumar Maurya (second grade of doctoral course student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted by
ACM Transactions on Knowledge Discovery from Data (impact factor : 3.290). (2020.12.27)
A paper (Title:
"Graph convolutional networks for graphs containing missing features") written by Mr. Hibiki Taguchi (2nd grade of master student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted by
Future Generation Computer Systems (impact factor : 6.125). (2020.12.5)
Murata gave a talk as an invited speaker of
WCTP 2020 (Title:"Networks, Deep Learning (and COVID-19)").
(slides) (2020.11.21)
Murata gave a talk in
Educational Innovation Symposium 2020 (Title:"An Attempt of On-Demand Classes by T2SCHOLA")
slides and videos (available inside of Tokyo Tech) (2020.11.13)
A paper (Title:
"Active Learning on Graphs via Meta Learning") written by Mr. Kaushalya Madhawa (fourth grade of doctoral student) is accepted by
ACML 2020 (acceptance rate:31%). (2020.8.26)
Murata wrote two Japanese articles (
"COVID-19 and Networks" and
"Web Intelligence" (co-authored with Prof. Yasufumi Takama)) to
JSAI Journal Vol.35, No.5 (Sep. 2020) (2020.9.2)
Mr. Quentin Jodelet (a doctoral student of our laboratory) was in the top 4
challenge winners (among 79 teams) of
CVPR Challenge on Continual Learning on June 14.(2020.6.15)
Our work appear in
attempts for AI research for COVID-19 of
AI Japan R&D Network:
accurate decision of emergency transport hospitals using graph deep learning and
centrality of temporal networks for preventing the spread of infection (in Japanese) (2020.6.1)
Ohmsha decided to print some more copies of the book (
"Learning Network Analysis with Python" (in Japanese)) written by Murata. (2020.2.21)
Murata gave a talk as
an invited speaker of
MARAMI 2019 (title :
Deep Learning Approaches for Networks (slides)). (2019.11.7)
Murata gave a talk as an invited speaker of
a meeting of Plasma Electronics at Tohoku University. (
slides (in Japanese)). (2019.9.25)
Murata published a book "Learning Network Analysis with Python" (
bibliographic info & program codes /
Ohmsha (publisher) /
Amazon). (2019.9.15)
A paper (Title:
"Recurrent Translation-based Network for Top-N Sparse Sequential Recommendation") written by Mr. Nuttapong Chairatanakul (first grade of doctoral course student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted by
IEEE Access (impact factor : 4.098). (2019.8.22)
A paper (Title:"Fast Approximations of Betweenness Centrality using Graph Neural Networks") written by Mr. Sunil Kumar Maurya (first grade of doctoral course student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted as a short paper of
CIKM 2019 (acceptance rate:21%). (2019.8.9)
Mr. Hibiki Taguchi receives
JSAI Annual Conference Award (Oral Presentation) for his presentation (title:
"Community detection in bipartite networks by multi label propagation algorithm") at
The 33rd Annual Conference of the Japanese Society for Artificial Intelligence, 2019. (2019.8.1)
Mr. Makoto Tachibana received
JSAI Incentive Award for his presentation (title:
"Semi-supervised learning on network using structure features and graph convolution" (in Japanese)) at
the 115th SIG meeting on Knowledge Base Systems. (2019.6.27)
Murata gave a talk (title:
"Deep Learning Approaches for Eruption Prediction of Sakurajima") as an invited speaker of
"Potentiality of Machine Learning in Solid Earth Sciences" session of
JpGU Meeting 2019. (2019.5.26)
Murata gave a lecture on
"Introduction of network analysis with Google Colaboratory" as one of the AI tool introduction of
JSAI (
codes and slides here). (2019.5.23)
Murata gave a talk (title:
"Deep Learning Approaches for Eruption Prediction of Sakurajima") as an invited speaker of
115th SIG-KBS meeting of JSAI. (2018.11.23)
Mr. Hiep Le's work of the prediction of volcanic eruption of Sakurajima appeared in an article of
monthly magazine Fole (in Japanese) issued by Mizuho Research Institute in October 2018. (2018.9.27)
A paper (Title:
"Learning Community Structure with Variational Autoencoder") written by Mr. Choong Jun Jin (second grade of doctoral course student), Dr. Liu Xin (AIRC, AIST) and Murata is accepted as a full paper of
IEEE ICDM 2018 (acceptance rate:8.86%). (2018.8.20)
Murata taught an intensive lecture on
Complex Networks at
Shanghai University of Finance and Economics. (2018.7.12-17)
Mr. Hiep Le's paper (Title:
"Deep Modular Multimodal Fusion on Multiple Sensors for Volcano Activity Recognition") has been accepted by
ECML-PKDD 2018 (acceptance rate:27%). (2018.6.19)
Mr. Hiep Le's research on predicting volcanic eruptions of Sakurajima (presented at
JSAI 2018 on June 6, 2018) appeared in a local TV news of NHK Kagoshima and in an atricle of South Japan Newspaper (2018.6.7)
"Kagoshima NEWS WEB -- AI predicts eruptions of Sakurajima" (NHK Kagoshima, in Japanese)
"AI predicts eruptions of Sakurajima --- 51.9% accuracy achieved with four-stage warning system by research team of Tokyo Tech & Kyoto Univ." (South Japan Newspaper Company, in Japanese)
An article about Mr. Hiep Le's work on Sakurajima volcanic activity recognition appeared in Nikkei Newspaper (page 9) on Jan. 15, 2018. (2018.1.15)
"Tokyo Tech & Kyoto Univ. use AI for volcano activity recognition (Nikkei Newspaper (written in Japanese))" "Deep Learning Approach for Volcano Activity Recognition (News of the Dept. of CS (written in Japanese))" "Deep Learning Approach for Volcano Activity Recognition (link to an article of KBS meeting in Nov. 2017(written in Japanese))"
call for postdocs: Our laboratory takes part in RWBC-OIL(AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory).
RWBC-OIL is a joint laboratory of Tokyo Tech and AIST for the analysis of real-world big data started from Feb. 2017 (news from Tokyo Tech (in Japanese), news from AIST (in Japanese)).
RWBC-OIL hires researchers of machine learning / deep learning / graph mining / high performance computing. Please read call for postdocs (in Japanese) for more information. If you are interested in, please contact Murata. (2017.4.6)
Mr. Yohei Oonuki receives
JSAI Incentive Award for his presentation (title:
"Prediction of relations among RDF entities by DNN" (in Japanese)) at
the 41st SIG meeting on Semantic Web and Ontology. (2017.5.8)
Mr. Shun Nukui received
JSAI Annual Conference Student Incentive Award for his presentation (title:
"Constrained community detection based on semi-supervised non-negative matrix factorization on TensorFlow" (in Japanese)) at
2016 JSAI Annual Conference. (2017.3.1)
Murata was invited to
MARAMI 2016 as an invited speaker, but the talk was canceled. (2016.10.14)
Murata gave a talk ("Mining and learning on heterogeneous networks") as an invited speaker at
WIMS 2016 (
slides). (2016.6.15)
Dr. Xin Liu and Mr. Weichu Liu's paper (title: "Community Detection in Multi-Partite Multi-Relational Networks Based on Information Compression") was accepted by
New Generation Computing. (2016.3.7)
Mr. Phiradet Bangcharoensap's paper (title:
"Transductive Classification on Heterogeneous Information Networks with Edge Betweenness-based Normalization") was accepted by
WSDM 2016 conference (acceptance rate: 18.2%). (2015.10.14)
Mr. Phiradet Bangcharoensap's paper (title:
"Two step graph-based semi-supervised learning for online auction fraud detection") was accepted by
ECML-PKDD 2015 conference (acceptance rate: 28.6%). (2015.6.3)
Mr. Keisuke Nakata presented his work at
CompleNet 2015(the 6th Workshop on Complex Networks). His photo is
here. (2015.3.25)
Mr. Keisuke Nakata (title:
"Speeding-up of Constrained Community Detection and Development of its Interactive Environment") received
JSAI Annual Conference Award. 19 papers were selected out of 584 papers. (2014.7.22)
Dr. Xin Liu and Mr. Weichu Liu's paper (A Framework for Community Detection in Heterogeneous Multi-Relational Networks) was accepted by
Advances in Complex Systems.(2014.7.9)
Dr. Xin Liu's paper (Detecting network communities beyond assortativity-related attributes) was accepted by
Physical Review E.(2014.6.19)
Mr. Ikematsu's programs for detecting communities from n-partite networks are
available online.
Reference:"Improvement of a Tripartite Modularity and Its Optimization Method"(written in Japanese)
(Transactions of the Japanese Society for Artificial Intelligence, Vol.29, No.2, pp.245-258, 2014)(2014.2.20)
