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pro vyhledávání: '"Khan, Rayyan Ahmad"'
Network embedding has emerged as a promising research field for network analysis. Recently, an approach, named Barlow Twins, has been proposed for self-supervised learning in computer vision by applying the redundancy-reduction principle to the embed
Externí odkaz:
http://arxiv.org/abs/2110.15742
Heterogeneous Information Network (HIN) embedding refers to the low-dimensional projections of the HIN nodes that preserve the HIN structure and semantics. HIN embedding has emerged as a promising research field for network analysis as it enables dow
Externí odkaz:
http://arxiv.org/abs/2108.03953
In this paper, we study how to simultaneously learn two highly correlated tasks of graph analysis, i.e., community detection and node representation learning. We propose an efficient generative model called VECoDeR for jointly learning Variational Em
Externí odkaz:
http://arxiv.org/abs/2101.03885
Autor:
Anwaar, Muhammad Umer, Han, Zhiwei, Arumugaswamy, Shyam, Khan, Rayyan Ahmad, Weber, Thomas, Qiu, Tianming, Shen, Hao, Liu, Yuanting, Kleinsteuber, Martin
In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node connections. Such sequential node connections e.g., metapaths, capture cri
Externí odkaz:
http://arxiv.org/abs/2010.11793
Variational autoencoder (VAE) is a widely used generative model for learning latent representations. Burda et al. in their seminal paper showed that learning capacity of VAE is limited by over-pruning. It is a phenomenon where a significant number of
Externí odkaz:
http://arxiv.org/abs/2004.01468
We propose a new clustering algorithm, Extended Affinity Propagation, based on pairwise similarities. Extended Affinity Propagation is developed by modifying Affinity Propagation such that the desirable features of Affinity Propagation, e.g., exempla
Externí odkaz:
http://arxiv.org/abs/1803.04459