Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding. Most existing neural network approaches learn latent representations by minimizing reconstruction errors. Rare work considers the data distribution and the topological structure of latent codes simultaneously, which often results in inferior embeddings in real-world graph data. This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) method for attributed graph data to improve the stability and quality of learned representations to tackle the crowding problem. The node-to-node geodesic similarity is preserved between the original and latent space under a pre-defined distribution. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets, which validates our solutions. We promise to release the code after acceptance.
Code (0)
등록된 구현이 없습니다.
Tasks
Graph EmbeddingSimilar Papers 제목 키워드 기반
Aligning the Unseen in Attributed Graphs: Interplay between Graph Geometry and Node Attributes Manifold
The standard approach to representation learning on attributed graphs -- i.e., simultaneously reconstructing node attributes and graph structure -- is geometrically flawed, as it merges two potentially incompatible metri…
Representation LearningGeoGNN: Quantifying and Mitigating Semantic Drift in Text-Attributed Graphs
Graph neural networks (GNNs) on text--attributed graphs (TAGs) typically encode node texts using pretrained language models (PLMs) and propagate these embeddings through linear neighborhood aggregation. However, the repr…
Graph LearningDeep attributed graph clustering with self-separation regularization and parameter-free cluster estimation
Detecting clusters over attributed graphs is a fundamental task in the graph analysis field. The goal is to partition nodes into dense clusters based on both their attributes and structures. Modern graph neural network…
ClusteringDeep ClusteringGraph ClusteringChange Detection in Graph Streams by Learning Graph Embeddings on Constant-Curvature Manifolds
The space of graphs is often characterised by a non-trivial geometry, which complicates learning and inference in practical applications. A common approach is to use embedding techniques to represent graphs as points in …
Change DetectionFunctional ConnectivitySeizure DetectionVariational Graph Autoencoder for Heterogeneous Information Networks with Missing and Inaccurate Attributes
Heterogeneous Information Networks (HINs), which consist of various types of nodes and edges, have recently demonstrated excellent performance in graph mining. However, most existing heterogeneous graph neural networks (…
AttributeDecoderGraph Mining