Searched for: subject%3A%22graph%255C+classification%22
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VASILEIOU, ANTONIOS (author)
Graph data is widely used in various applications, driving the rapid development of graph-based machine learning methods. However, traditional algorithms tailored for graphs have constraints in capturing intricate node relationships and higher-order patterns. Recent insights from prior research have shed light on comparing different graph neural...
master thesis 2023
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Xu, J. (author), Picek, S. (author)
Graph Neural Networks (GNNs) have achieved impressive results in various graph learning tasks. They have found their way into many applications, such as fraud detection, molecular property prediction, or knowledge graph reasoning. However, GNNs have been recently demonstrated to be vulnerable to backdoor attacks. In this work, we explore a...
conference paper 2022
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Wang, Jing (author)
In the thesis, we aim to compare matrices that can vary in size or shape, and their columns/rows are permutation invariant. We propose a general solution to such problem by using the vector representation to represent a matrix. Several feature representations are invented and investigated in the thesis, based on histogram, statistics,...
master thesis 2017