Community Discovery for Complex Networks

Given: a network, especially a large one, directed or not, weighted or not.

Desired: a sensible decomposition of the graph into sub-graphs, wherein the nodes in each sub-graph have more to do with each other than with outsiders, i.e., form communities.

This is also called “module detection“.

Ref from here

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Topological time-series analysis with delay-variant embedding

Preprint: https://arxiv.org/abs/1803.00208

Papers on TDA

Unread (new)

Read & Categorized

Conventional

• H. Edelsbrunner and J. Harer, Computational Topology. An Introduction. (Amer. Math. Soc., 2010).
• H. Edelsbrunner, D. Letscher, and A. Zomorodian, Topological persistence and simplification. Discrete Comput. Geom. 28, 511 (2002)

Material science

• I. Donato, M. Gori, M. Pettini, G. Petri, S. De Nigris, R. Franzosi, and F. Vaccarino,Persistent homology analysis of phase transitions. Phys. Rev. E 93, 052138 (2016).
• Y. Hiraoka, T. Nakamura, A. Hirata, E. G. Escolar, K. Matsue, and Y. Nishiura, Hierarchical structures of amorphous solids characterized by persistent homology. Proc. Natl. Acad. Sci. U.S.A. (2016)
• T. Nakamura, Y. Hiraoka, A. Hirata, E. G. Escolar, and Y. Nishiura, Persistent homology and many-body atomic structure for medium-range order in the glass. Nanotechnology 26, 304001 (2015).
• S. Ardanza-Trevijano, I. Zuriguel, R. Ar ́evalo, and D. Maza, Topological analysis of tapped granular media using persistent homology. Phys. Rev. E 89, 052212 (2014).

Kernel method

• M. Carri`ere, M. Cuturi, and S. Oudot. Sliced Wasserstein Kernel for Persistence Diagrams. Proc. 34th Int. Conf. Machine Learning (ICML), Vol. 70 (2017).
• G. Kusano, K. Fukumizu, and Y. Hiraoka. Persistence weighted Gaussian kernel for topological data analysis. Proc. 33th Int. Conf. Machine Learning (ICML), Vol. 48 (2016).
• J. Reininghaus, S. Huber, U. Bauer, and R. Kwitt,  A Stable Multi-Scale Kernel for Topological Machine Learning. 2015 IEEE Conf. Computer Vision and Pattern Recognition (CVPR) (2015).

Time-series + Dynamic system

• (Preprint) Hoan Tran Quoc, Yoshihiko Hasegawa, Topological time-series analysis with delay-variant embedding.

Clustering

Benchmark

Other

 

Seizure Prediction Challenge Intro.

About challenge

Current challenge:

• Melbourne University AES/MathWorks/NIH Seizure Prediction   (Sep. 2016)

Previous challenges:

• American Epilepsy Society Seizure Prediction Challenge  (Aug. 2014)
• UPenn and Mayo Clinic’s Seizure Detection Challenge (May 2014)

Useful links

Import Yelp dataset to MongoDB

Ref link

• https://codisthan.wordpress.com/2014/09/23/exploring-yelp-data-with-mongodb/
• http://blog.jimjh.com/predicting-user-ratings-on-yelp.html
• http://www.vladsandulescu.com/topic-prediction-lda-user-reviews/
• https://gist.github.com/zhouzhuojie/17a41d67510b32ba27df

My recent work in Recommendation System

Need time to write up.
http://docs.prediction.io/community/showcase/

Description:

Yelp is an online urban guide that helps people find recommended destinations based on the informed opinions and reviews of a community.

Goal: the high level goal of this project is to apply existing built-in PredictionIO engines to a new domain and new dataset.

For this project you will build a restaurant recommendation engine (see Yelp) using built-in Item Recommendation engine and the Yelp dataset.

Dataset:

Yelp dataset via Kaggle – https://www.kaggle.com/c/yelp-recsys-2013/data

Demo link:

http://yelpio.hongo.wide.ad.jp/

http://zorovn.hongo.wide.ad.jp/

Link

Prediction io–final 2014-jp-handout from Ha Phuong

Thesis

 

論文の書き方

東大の千葉滋先生が書いたマニュアル。研究室の先輩が送ってくれた。短くて分かりやすい。

覚えておこう。

http://www.csg.ci.i.u-tokyo.ac.jp/~chiba/writing/sld001.htm