Search Results for author:
Found 9 papers, 4 papers with code
Graph Condensation via Eigenbasis Matching
The increasing amount of graph data places requirements on the efficiency and scalability of graph neural networks (GNNs), despite their effectiveness in various graph-related applications.
Data-centric Graph Learning: A Survey
Recently, instead of designing more complex neural architectures as model-centric approaches, the attention of AI community has shifted to data-centric ones, which focuses on better processing data to strengthen the ability of neural models.
Specformer: Spectral Graph Neural Networks Meet Transformers
To tackle these issues, we introduce Specformer, which effectively encodes the set of all eigenvalues and performs self-attention in the spectral domain, leading to a learnable set-to-set spectral filter.
A Survey on Spectral Graph Neural Networks
Graph neural networks (GNNs) have attracted considerable attention from the research community.
Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum
Then we theoretically prove that GCL is able to learn the invariance information by contrastive invariance theorem, together with our GAME rule, for the first time, we uncover that the learned representations by GCL essentially encode the low-frequency information, which explains why GCL works.
Beyond Low-frequency Information in Graph Convolutional Networks
For a deeper understanding, we theoretically analyze the roles of low-frequency signals and high-frequency signals on learning node representations, which further explains why FAGCN can perform well on different types of networks.
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e. g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years.
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
We tackle the challenge and propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN).
Structural Deep Clustering Network
The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning.
