We formulize the network with higher-order dependency as an augmented conventional first-order network, and then feed it into GNNs to derive network embeddings.
Apr 25, 2022 · Graph Neural Network for Higher-Order Dependency Networks ... Graph neural network (GNN) has become a popular tool to analyze the graph data.
May 27, 2022 · To address this, we propose a novel Deep Graph Ensemble (DGE), which captures neighborhood variance by training an ensemble of GNNs on different ...
Graph Neural Network for Higher-Order Dependency Networks WWW (2022). HONEM: Learning Embedding for Higher Order Networks Big data (2020). 2.2 Statistical ...
Duration: 13:18
Posted: Sep 12, 2022
Posted: Sep 12, 2022
The method includes two steps: 1) dynamic sub-graph sampling, and 2) pre-training with dynamic attributed graph generation task. Comparative experiments on ...
Conventionally, a network (also referred to as a graph) G = (V, E) is represented with vertices or nodes V as entities (for example, places, Web pages, etc.) ...
Jan 20, 2024 · Graph Neural Networks (GNNs) have demon- strated remarkable success in modeling com- plex relationships in graph-structured data.
Graph Neural Network for Higher-Order Dependency Networks. https://doi.org/10.1145/3485447.3512161. Journal: Proceedings of the ACM Web Conference 2022, 2022.
Feb 1, 2023 · We propose an ensemble of GNNs that exploits variance in the neighborhood subspaces of nodes in graphs with higher-order dependencies and ...