J Zhou, L Liu, W Wei, J Fan - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks …
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 …
Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems …
C Zhang, D Song, C Huang, A Swami… - Proceedings of the 25th …, 2019 - dl.acm.org
Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link …
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 …
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 …
Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed …
Networks evolve continuously over time with the addition, deletion, and changing of links and nodes. Although many networks contain this type of temporal information, the majority of …
R Zhu, K Zhao, H Yang, W Lin, C Zhou, B Ai… - arXiv preprint arXiv …, 2019 - arxiv.org
An increasing number of machine learning tasks require dealing with large graph datasets, which capture rich and complex relationship among potentially billions of elements. Graph …