599 citations · 1.2k across the 114 of their papers we have counts for
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Graph Neural Machine: A New Model for Learning with Tabular Data
Giannis Nikolentzos, Siyun Wang, Johannes Lutzeyer +1
In recent years, there has been a growing interest in mapping data from different domains to graph structures. Among others, neural network models such as the multi-layer perceptro…
Permute Me Softly: Learning Soft Permutations for Graph Representations
Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis
Graph neural networks (GNNs) have recently emerged as a dominant paradigm for machine learning with graphs. Research on GNNs has mainly focused on the family of message passing neu…
k-hop Graph Neural Networks
Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis
Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisf…
Graph Kernels: A Survey
Giannis Nikolentzos, Giannis Siglidis, Michalis Vazirgiannis
Graph kernels have attracted a lot of attention during the last decade, and have evolved into a rapidly developing branch of learning on structured data. During the past 20 years,…
Message Passing Graph Kernels
Giannis Nikolentzos, Michalis Vazirgiannis
Graph kernels have recently emerged as a promising approach for tackling the graph similarity and learning tasks at the same time. In this paper, we propose a general framework for…
GraKeL: A Graph Kernel Library in Python
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios +3
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promisin…