Dagstuhl-Seminar 23491
Scalable Graph Mining and Learning
( 03. Dec – 08. Dec, 2023 )
Permalink
Bitte benutzen Sie folgende Kurz-Url zum Verlinken dieser Seite:
https://www.dagstuhl.de/23491
Organisatoren
- Nesreen Ahmed (Intel - San Jose, US)
- Danai Koutra (University of Michigan and Amazon - Ann Arbor)
- Henning Meyerhenke (HU Berlin, DE)
- Ilya Safro (University of Delaware - Newark, US)
Kontakt
- Michael Gerke (für wissenschaftliche Fragen)
- Simone Schilke (für administrative Fragen)
Dagstuhl Seminar Wiki
- Dagstuhl Seminar Wiki (Use personal credentials as created in DOOR to log in)
Gemeinsame Dokumente
- Dagstuhl Materials Page (Use personal credentials as created in DOOR to log in)
Graphs (networks) arise as a natural representation of complex systems across the sciences, e.g., social networks in computational social science, sensor networks in telecommunications, regulatory networks in genetics, functional brain networks in neuroimaging, and meshed surfaces in computer graphics and scientific computing. In many of these applications, graphs are large, complex, and dynamic (e.g., online social networks), which makes them a natural target for graph mining and machine learning (including deep learning) techniques.
Graph mining and learning are sub-disciplines of data mining and machine learning that are concerned with mining, learning and modeling the complex relational structure of data. The remarkable success of deep learning (also called representation or feature learning) in computer vision, natural language processing, and speech recognition has recently increased the interest in the application of deep learning in relational domains. This led to a surge of methods that focus on graph representation learning, where the goal is to learn a mapping that embeds nodes, edges, subgraphs, or entire graphs, as points in a latent low-dimensional vector space. Although the demand for scalable techniques for such methods is increasing in many real-world applications, most graph mining and learning techniques are resource-intensive, since scalability has not been a massive research focus in these areas yet.
Scalable analysis of graphs has been the subject of many recent research efforts, in particular in algorithm engineering (AE) and combinatorial scientific computing (CSC). With a focus on high performance, these communities enabled much larger graphs to be processed in a shorter amount of time. We expect attendees from AE and CSC to share their expertise on processing and analyzing large-scale graph data efficiently, e.g., with parallel and distributed computing techniques as well sophisticated algorithmic methods and data structures.
To summarize, this Dagstuhl Seminar aims at narrowing the gap between (i) researchers from AE and CSC who focus primarily on scalable graph algorithms and on how to implement them on parallel and distributed platforms, as well as (ii) researchers from the areas of graph mining and learning with a strong interest in algorithmic and scalability aspects. The participants from graph mining and learning also represent the domain expertise in real-world applications to a large extent, whereas the graph algorithms experts shall contribute expertise in high-performance algorithms and tools that address real-life graph problems. Our main high-level goal for the seminar is to discuss and achieve the synergies that become possible from bringing the two fields closer together.
Creative Commons BY 4.0
Nesreen K. Ahmed, Danai Koutra, Henning Meyerhenke, and Ilya Safro
- Nesreen Ahmed (Intel - San Jose, US) [dblp]
- David A. Bader (NJIT - Newark, US) [dblp]
- Maciej Besta (ETH Zürich, CH) [dblp]
- Aleksandar Bojchevski (Universität Köln, DE)
- Konstantinos Bougiatiotis (Demokritos - Athens, GR) [dblp]
- Fabian Brandt-Tumescheit (HU Berlin, DE) [dblp]
- Ümit V. Çatalyürek (Georgia Institute of Technology - Atlanta, US) [dblp]
- Ayan Chatterjee (Northeastern University - Boston, US)
- Mikhail Galkin (Intel AI Lab - San Diego, US) [dblp]
- Ernestine Großmann (Universität Heidelberg, DE) [dblp]
- Kathrin Hanauer (Universität Wien, AT) [dblp]
- Mariya Ishteva (KU Leuven - Geel, BE) [dblp]
- George Karypis (University of Minnesota - Minneapolis, US) [dblp]
- Christine Klymko (LLNL - Livermore, US) [dblp]
- Danai Koutra (University of Michigan and Amazon - Ann Arbor) [dblp]
- Kenneth Langedal (University of Bergen, NO) [dblp]
- Johannes Langguth (Simula Research Laboratory - Oslo, NO) [dblp]
- Michelle Li (Harvard University - Boston, US)
- Fredrik Manne (University of Bergen, NO) [dblp]
- Ulrich Carsten Meyer (Goethe University - Frankfurt am Main, DE) [dblp]
- Henning Meyerhenke (HU Berlin, DE) [dblp]
- Christopher Morris (RWTH Aachen, DE) [dblp]
- Davide Mottin (Aarhus University, DK) [dblp]
- Richard Peng (University of Waterloo, CA) [dblp]
- Bryan Perozzi (Google - New York, US) [dblp]
- Cynthia A. Phillips (Sandia National Labs - Albuquerque, US) [dblp]
- Alex Pothen (Purdue University - West Lafayette, US) [dblp]
- Ilya Safro (University of Delaware - Newark, US) [dblp]
- Michael Schaub (RWTH Aachen, DE) [dblp]
- Matthias Schimek (KIT - Karlsruher Institut für Technologie, DE) [dblp]
- Christian Schulz (Universität Heidelberg, DE) [dblp]
- Christian Sohler (Universität Köln, DE) [dblp]
- Frank Takes (Leiden University, NL) [dblp]
- Flavio Vella (University of Trento, IT) [dblp]
- Elena Zheleva (University of Illinois - Chicago, US) [dblp]
Verwandte Seminare
Klassifikation
- Data Structures and Algorithms
- Distributed / Parallel / and Cluster Computing
- Machine Learning
Schlagworte
- Graph mining
- graph machine learning
- (hyper)graph and network algorithms
- high-performance computing for graphs
- algorithm engineering for graphs
