Graph (abstract data type)
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Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood… Expand We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging… Expand We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of… Expand Abstract: Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks… Expand Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper… Expand Many practical computing problems concern large graphs. Standard examples include the Web graph and various social networks. The… Expand We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing… Expand In this correspondence, the construction of low-density parity-check (LDPC) codes from circulant permutation matrices is… Expand The Internet consists of rapidly increasing number of hosts interconnected by constantly evolving networks of links and routers… Expand A novel graph theoretic approach for data clustering is presented and its application to the image segmentation problem is… Expand