no code implementations • 17 Jan 2024 • Junfu Wang, Yuanfang Guo, Liang Yang, Yunhong Wang
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.
no code implementations • 2 Nov 2023 • Junfu Wang, Yawen Li, Zhe Xue, Ang Li
Academic networks in the real world can usually be described by heterogeneous information networks composed of multi-type nodes and relationships.
no code implementations • 1 Jul 2023 • Ruijie Yang, Yuanfang Guo, Junfu Wang, Jiantao Zhou, Yunhong Wang
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.
no code implementations • 7 Feb 2023 • Junfu Wang, Yuanfang Guo, Liang Yang, Yunhong Wang
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.
1 code implementation • 24 Oct 2022 • Junfu Wang, Yuanfang Guo, Liang Yang, Yunhong Wang
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.
no code implementations • 7 Oct 2022 • Junfu Wang, Yawen Li, Meiyu Liang, Ang Li
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.
no code implementations • 23 Sep 2022 • Junfu Wang, Yuanfang Guo, Liang Yang, Yunhong Wang
Extensive experiments demonstrate that our RE-GNN can effectively and efficiently handle the heterogeneous graphs and can be applied to various homogeneous GNNs.
1 code implementation • CVPR 2021 • Junfu Wang, Yunhong Wang, Zhen Yang, Liang Yang, Yuanfang Guo
Graph Neural Networks (GNNs) have achieved tremendous success in graph representation learning.