The graph embedding paradigm projects nodes of a graph into a vector space, which can facilitate various downstream graph analysis tasks such as node classification and …
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 …
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types. Heterogeneous graph embedding is to embed …
Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from the high computational cost and excessive …
L Yang, Z Xiao, W Jiang, Y Wei, Y Hu… - Advances in Information …, 2020 - Springer
Graph embedding has attracted many research interests. Existing works mainly focus on static homogeneous/heterogeneous networks or dynamic homogeneous networks …
Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph …
Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight …
L Yu, L Sun, B Du, C Liu, W Lv… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Representation learning on heterogeneous graphs aims to obtain meaningful node representations to facilitate various downstream tasks, such as node classification and link …
P Yu, C Fu, Y Yu, C Huang, Z Zhao… - Proceedings of the 28th …, 2022 - dl.acm.org
Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link …