Y Liu, T Safavi, A Dighe, D Koutra - ACM computing surveys (CSUR), 2018 - dl.acm.org
While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph …
Graph representation learning has emerged as a powerful technique for addressing real- world problems. Various downstream graph learning tasks have benefited from its recent …
Q Huang, H He, A Singh, SN Lim… - arXiv preprint arXiv …, 2020 - arxiv.org
Graph Neural Networks (GNNs) are the predominant technique for learning over graphs. However, there is relatively little understanding of why GNNs are successful in practice and …
P Li, Y Wang, H Wang… - Advances in Neural …, 2020 - proceedings.neurips.cc
Learning representations of sets of nodes in a graph is crucial for applications ranging from node-role discovery to link prediction and molecule classification. Graph Neural Networks …
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an …
Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector …
Artificial intelligence and machine learning (AI/ML) algorithms are increasingly developed in health care for diagnosis and treatment of a variety of medical conditions (1). However …
B Rozemberczki, R Sarkar - Proceedings of the 29th ACM international …, 2020 - dl.acm.org
In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce …