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Data and Web Science Seminar (FSS 2019)

The Data and Web Science seminar covers recent topics in data and web science. This term, the seminar focuses on Graph Mining and Learning from Graphs.

Organization

  • This seminar is organized by Kiril Gashteovski and Prof. Dr. Rainer Gemulla.
  • Available for Master students (2 SWS, 4 ECTS). If you are a Bachelor student and want to take this seminar (2 SWS, 5 ECTS), please contact Prof. Gemulla.
  • Prerequisites: solid background in data mining / machine learning

Goals

In this seminar, you will

  • Read, understand, and explore scientific literature
  • Summarize a current research topic in a concise report (10 single-column pages + references)
  • Give two presentations about your topic (3 flash presentation, 15 final presentation)
  • Moderate a scientific discussion about the topic of one of your fellow students
  • Provide feedback to a report and to a presentation of a fellow student

Schedule

  • Register as described below
  • Attend the kickoff meeting on Feb 20 (tentative)
  • Work individually throughout the semester according to this schedule
  • Meet your advisor for guidance and feedback

Registration

Register via Portal2 until February 11, 2019.

Explore the list of topics below and select at least 3 papers of your preference. You may also propose alternative topics relevant to the seminar.

If you are accepted into the seminar, provide your preferred topics February 17, 2019 via email to Kiril Gashteovski. The actual topic assignment takes place soon afterwards; we will notify you via email. Our goal is to assign to you to one of your preferred topics.

Topics

Note:  You might find some of the listed papers unaivailable. In case you can not access the paper you want to see, contact Kiril Gashteovski directly.

Graph Mining

  1. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph
    Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, Panos Kalnis, PVLDB 2014
  2. Local Higher-Order Graph Clustering
    Hao Yin, Austin R. Benson, Jure Leskovec, David F. Gleich, KDD 2017

Graph Representation

  1. node2vec: Scalable Feature Learning for Networks
    Aditya Grover, Jure Leskovec, KDD 2016
  2. DeepWalk: Online Learning of Social Representations
    Bryan Perozzi, Rami Al-Rfou, Steven Skiena, KDD 2014
  3. GraRep: Learning Graph Representations with Global Structural Information
    Shaosheng Cao, Wei Lu, Qiongkai Xu, CIKM 2015
  4. Deep Convolutional Networks on Graph-Structured Data
    Mikael Henaff, Joan Bruna, Yann LeCun, CoRR 2015
  5. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
    Michaël Defferrard, Xavier Bresson, Pierre Vandergheynst, NIPS 2016

Frameworks

  1. Arabesque: a system for distributed graph mining
    Carlos HC Teixeira, Alexandre J. Fonseca, Marco Serafini, Georgos Siganos, Mohammed J. Zaki, Ashraf Aboulnaga, SOSP 2015
  2. Extracting and Analyzing Hidden Graphs from Relational Databases
    Konstantinos Xirogiannopoulos, Amol Deshpande, SIGMOD 2017

Applications

  1. Incorporating Knowledge Graph Embedding Topic Modeling
    Liang Yao, Yin Zhang, Baogang Wei, Zhe Jin, Rui Zhang, Yangyang Zhang, Qinfei Chen, AAAI 2017
  2. Explicit Semantic Ranking for Academic Search via Knowledge Graph Embedding
    Chenyan Xiong, Russell Power, Jamie Callan, WWW 2017
  3. Unsupervised Person Slot Filling based on Mining
    Graphing Dian Yu, Heng Ji, ACL 2016
  4. Pixie: A System for Recommending 3+ Billion Items to 200+ Million Real-Time
    Users Chantat Eksombatchai, Pranav Jindal, Jerry Zitao Liu, Yuchen Liu, Rahul Sharma, Charles Sugnet, Mark Ulrich, Jure Leskovec, WWW 2018

Supplementary materials and references