Aria Rezaei

Aria Rezaei
PhD Student
Computer Science
Stony Brook Univiersity

About me

  • I'm a second year PhD student in Computer Science Department of Stony Brook University. I'm advised by professo Jie Gao. I'm currently working on a project focused on modeling how information spreads between agents moving in a grid via different movement policies.

    Previously, I worked with Leman Akoglu, now at CMU.

    Check out my projects here.

Research Interests

    • Social and Information Networks
    • Large-Scale Graph Mining
    • Information/Disease Outbreak Analysis
    • Graph Theory

Education

  • Stony Brook University
    PhD in Computer Science
    2015 - Present

  • Sharif University of Technology
    B.Sc. in Computer Engineering
    2010 - 2015

Honors and Awards

  • Renaissance Technology Fellowship
    2015 - 2017

    Awarded to select PhD students at Computer Science department of Stony Brook University.

  • Silver Medalist in the 18th Iranian National Olympiad
    2009

Aria Rezaei

Aria Rezaei
PhD Student
Computer Science
Stony Brook Univiersity

Peer Reviewed Publications

For an up-to-date list of my publications, please visit my Google Scholar page.

Conference and Journal Papers

  • Ties That Bind: Characterizing Classes by Attributes and Social Ties

    Aria Rezaei, Bryan Perozzi, Leman Akoglu
    WWW Companion 2017, Perth, Australia
    [PDF] [Slides] [Code] [Data]

  • Near linear-time community detection in networks with hardly detectable community structure

    Aria Rezaei, Saeed Mahloujifar, Mahdieh Soleymani
    ASONAM 2015, Paris, France
    [PDF] [Slides] [Code] [Data]

Workshop and Poster Papers

Nothing here yet.

Aria Rezaei

Aria Rezaei
PhD Student
Computer Science
Stony Brook Univiersity

Projects

  • Characterizing Class Differences in Attributed Graphs

    In social and information networks, attributes tend to strengthen the ties inside closely knit communities and weaken the ties between them and the world outside. Here, we propose a method to find such attributes in a setting where several classes of subgraphs are present and we find attributes that separate these classes from eachother.

  • Controlled Label Propagation
    Preventing Ovevr-propagation through Gradual Expansion of Communities

    Label Propagation Algorithm (LPA) is a lightweight, fast and secure method to retrieve community. However, due to over-propagation of labels during the early stages of the algorithm, some communities grow faster and bigger than they should and they flood the whole network. Here, we propose a simple and efficient heuristic to prevent the flooding. Our method can be used on top of most of the previously found heuristics.

Aria Rezaei

Aria Rezaei
PhD Student
Computer Science
Stony Brook Univiersity

Attributed Graphs

  • Congress Dataset
    Attributed Undirected Temporal Node Meta Attribute Meta

    A group of congressmen who introduce a bill to the congress are called that bill's sponsors. Each bill has a group of policy terms. Here, there are 8 periods of congress which are turned into graphs where nodes are congressmen, edges are co-sponsorship relations and attributes are the policy terms a congressmen has introduced bills into.

  • Amazon Co-purchaseship
    Attributed Undirected Node Meta Attribute Meta

    In this dataset, nodes are products and edges are between products that are frequently bought together. Here, attributes are properties about the genre, content or creators of the products. The products are also in either of the 4 categories of Books, Videos, Music or DVD.

  • DBLP Co-authorship
    Attributed Undirected Node Meta Attribute Meta

    In this dataset, nodes are computer scientists on DBLP record and edges show co-authorship relation between to authors. Attributes are the venues of publication in Computer Science, famous conferences, journals and online repositories of scientific articles.

Email Address