no code implementations • 29 Oct 2021 • Rayyan Ahmad Khan, Martin Kleinsteuber
Motivated by this, we propose Barlow Graph Auto-Encoder, a simple yet effective architecture for learning network embedding.
no code implementations • 9 Aug 2021 • Rayyan Ahmad Khan, Martin Kleinsteuber
HIN embedding has emerged as a promising research field for network analysis as it enables downstream tasks such as clustering and node classification.
1 code implementation • 11 Jan 2021 • Rayyan Ahmad Khan, Muhammad Umer Anwaar, Omran Kaddah, Martin Kleinsteuber
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i. e., community detection and node representation learning.
no code implementations • 1 Jan 2021 • Rayyan Ahmad Khan, Muhammad Umer Anwaar, Omran Kaddah, Martin Kleinsteuber
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i. e., community detection and node representation learning.
1 code implementation • 22 Oct 2020 • Muhammad Umer Anwaar, Zhiwei Han, Shyam Arumugaswamy, Rayyan Ahmad Khan, Thomas Weber, Tianming Qiu, Hao Shen, Yuanting Liu, Martin Kleinsteuber
In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures.
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1 code implementation • 3 Apr 2020 • Rayyan Ahmad Khan, Muhammad Umer Anwaar, Martin Kleinsteuber
Variational autoencoder (VAE) is a widely used generative model for learning latent representations.
no code implementations • 12 Mar 2018 • Rayyan Ahmad Khan, Rana Ali Amjad, Martin Kleinsteuber
We propose a new clustering algorithm, Extended Affinity Propagation, based on pairwise similarities.