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Found 8 papers, 2 papers with code
Understanding Heterophily for Graph Neural Networks
Firstly, we show that by applying a GC operation, the separability gains are determined by two factors, i. e., the Euclidean distance of the neighborhood distributions and $\sqrt{\mathbb{E}\left[\operatorname{deg}\right]}$, where $\mathbb{E}\left[\operatorname{deg}\right]$ is the averaged node degree.
Research Team Identification Based on Representation Learning of Academic Heterogeneous Information Network
Academic networks in the real world can usually be described by heterogeneous information networks composed of multi-type nodes and relationships.
Common Knowledge Learning for Generating Transferable Adversarial Examples
Specifically, to reduce the model-specific features and obtain better output distributions, we construct a multi-teacher framework, where the knowledge is distilled from different teacher architectures into one student network.
Heterophily-Aware Graph Attention Network
In this paper, we firstly propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i. e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, which enable nodes to acquire appropriate information from distinct neighbors.
Binary Graph Convolutional Network with Capacity Exploration
The current success of Graph Neural Networks (GNNs) usually relies on loading the entire attributed graph for processing, which may not be satisfied with limited memory resources, especially when the attributed graph is large.
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning
To solve the above challenges, aiming at the data information of scientific research teams closely related to science and technology, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network.
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding
Extensive experiments demonstrate that our RE-GNN can effectively and efficiently handle the heterogeneous graphs and can be applied to various homogeneous GNNs.
Bi-GCN: Binary Graph Convolutional Network
Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning.
