MS-E1603 - Random Graphs and Network Statistics D, Lecture, 13.9.2021-21.10.2021

Översikt

• Project
  Description
  

  The project work will focus on a subject chosen by the student, among a list of themes proposed by the teachers. 

  This is a reading exercise where a group of 1-2 students reads a selected research article related to the course theme. Based on the article, the group writes an essay of 3-4 pages and prepares a 15-20 min presentation where the following four key points will be discussed:

  1. Objective: What is the goal of the research?
  2. Motivation: Why is the research worth carrying out?
  3. Results: What are the main results obtained?
  4. Evaluation: What is your own estimate of the usefulness of the results?

  
  

  Important dates

  • Fri 8 Oct 2021 12:00. You should email to the teachers a copy of the article that you propose to read.
  • Mon 25 Oct 2021 15:00-17:00. Project workshop online (Meeting ID: 665 4105 4702, https://aalto.zoom.us/j/66541054702) where presentations are given. You should email your written essay as pdf attachment to the lecturer.

  If you cannot attend the project workshop and give a presentation, you can compensate this by writing a longer essay of 6-8 pages.
  

  • Välj aktivitet Project topics Presenter(s) Author(s) Title Afrin ...

    Project topics

    
    

    Presenter(s)	Author(s)	Title
    Afrin & Kai	EF Touli, O Lindberg 2021	Relative Clustering Coefficient
    Sakari	M Gösgens, R van der Hofstad, N Litvak 2021	The Hyperspherical Geometry of Community Detection: Modularity as a Distance
    Heikki	Z Hu, Y Fang, L Lin 2021	Training Graph Neural Networks by Graphon Estimation
    Vikke	R van der Hofstad, P van der Hoorn, N Maitra 2021	Local limits of spatial inhomogeneous random graphs
    Emma & Linda	JSH van Leeuwaarden, C Stegehuis 2021	Robust subgraph counting with distribution-free random graph analysis
    Silja	PA Gomes, A Pereira, R Sanchis 2021	Upper bounds for critical probabilities in Bernoulli Percolation models
    Noora	S Olivella, T Pratt, K Imai 2021	Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts

    
    
    

  • Välj aktivitet Interesting new articlesEM-based smooth graphon es...

    Interesting new articles

    EM-based smooth graphon estimation using MCMC and spline-based approaches

    B Sischka, G Kauermann - Social Networks, 2022

    
    

    
    

    Change Point Detection in Weighted and Directed Random Dot Product Graphs

    F Larroca, P Bermolen, M Fiori, G Mateos

    
    

    
    

    
    

    Robust subgraph counting with distribution-free random graph analysis
    

    JSH van Leeuwaarden, C Stegehuis - arXiv preprint arXiv:2107.10089, 2021

    
    

    
    

    Local limits of spatial inhomogeneous random graphs

    R van der Hofstad, P van der Hoorn, N Maitra - arXiv preprint arXiv:2107.08733, 2021

    
    

    Spectral Recovery of Binary Censored Block Models

    S Dhara, J Gaudio, E Mossel, C Sandon - arXiv preprint arXiv:2107.06338, 2021

    
    

    
    

    Supercritical percolation on graphs of polynomial growth

    D Contreras, S Martineau, V Tassion - arXiv preprint arXiv:2107.06326, 2021

    
    

    
    

    Upper bounds for critical probabilities in Bernoulli Percolation models

    P Almeida Gomes, A Pereira, R Sanchis - arXiv e-prints, 2021

    
    

    
    

    The Hyperspherical Geometry of Community Detection: Modularity as a Distance

    M Gösgens, R van der Hofstad, N Litvak - arXiv preprint arXiv:2107.02645, 2021

    
    

    Optimal Non-Convex Exact Recovery in Stochastic Block Model via Projected Power Method

    P Wang, H Liu, Z Zhou, AMC So - arXiv preprint 

    
    

    Hypothesis Testing for Equality of Latent Positions in Random Graphs

    X Du, M Tang - arXiv preprint arXiv:2105.10838, 2021

    
    

    A Stochastic Block Model Based Approach to Detect Outliers in Networks
    

    F Angiulli, F Fassetti, C Serrao - International Conference on Database and Expert …, 2021

    
    

    SPECTRAL ESTIMATION OF LARGE STOCHASTIC BLOCKMODELS WITH DISCRETE NODAL COVARIATES

    A MELE, L HAO, J CAPE, CE PRIEBE

    
    

    Training Graph Neural Networks by Graphon Estimation

    Z Hu, Y Fang, L Lin - arXiv preprint arXiv:2109.01918, 2021

    
    

    Approximate Conditional Sampling for Pattern Detection in Weighted Networks

    JA Scott, A Gandy - arXiv preprint arXiv:2109.09104, 2021

    
    

    Weighted stochastic block model

    TLJ Ng, TB Murphy - Statistical Methods & Applications, 2021