Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery

• URL: http://arxiv.org/abs/2405.12797v2
• Date: Fri, 07 Mar 2025 15:14:56 GMT
• Title: Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
• Authors: Cencheng Shen, Jonathan Larson, Ha Trinh, Carey E. Priebe,
• Abstract summary: This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery.<n>We show how our proposed method can effectively identify useful hidden communities under block models.
• Score: 16.209340214884776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper introduces a refined graph encoder embedding method, enhancing the original graph encoder embedding through linear transformation, self-training, and hidden community recovery within observed communities. We provide the theoretical rationale for the refinement procedure, demonstrating how and why our proposed method can effectively identify useful hidden communities under stochastic block models. Furthermore, we show how the refinement method leads to improved vertex embedding and better decision boundaries for subsequent vertex classification. The efficacy of our approach is validated through numerical experiments, which exhibit clear advantages in identifying meaningful latent communities and improved vertex classification across a collection of simulated and real-world graph data.

Related papers

• Principal Graph Encoder Embedding and Principal Community Detection [15.747768357012488]
  We introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities. The method computes a sample community score for each community, ranking them to measure community importance and estimate a set of principal communities.
  arXiv  Detail & Related papers  (2025-01-24T21:55:04Z)
• Preserving Node Distinctness in Graph Autoencoders via Similarity Distillation [9.395697548237333]
  Graph autoencoders (GAEs) rely on distance-based criteria, such as mean-square-error (MSE) to reconstruct the input graph. relying solely on a single reconstruction criterion may lead to a loss of distinctiveness in the reconstructed graph. We have developed a simple yet effective strategy to preserve the necessary distinctness in the reconstructed graph.
  arXiv  Detail & Related papers  (2024-06-25T12:54:35Z)
• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• CONVERT:Contrastive Graph Clustering with Reliable Augmentation [110.46658439733106]
  We propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT) In our method, the data augmentations are processed by the proposed reversible perturb-recover network. To further guarantee the reliability of semantics, a novel semantic loss is presented to constrain the network.
  arXiv  Detail & Related papers  (2023-08-17T13:07:09Z)
• Synergistic Graph Fusion via Encoder Embedding [16.20934021488478]
  We introduce a method called graph fusion embedding, designed for multi-graph embedding with shared sets. Under the framework of supervised learning, our method exhibits a remarkable and highly desirable synergistic effect. Our comprehensive simulations and real data experiments provide compelling evidence supporting the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-03-31T13:34:35Z)
• Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection [16.743897700396218]
  We introduce a novel and computationally efficient method for embedding, community detection, and community size determination. Our approach leverages a normalized one-hot graph encoder and a rank-based cluster size measure. Through extensive simulations, we demonstrate the excellent numerical performance of our proposed graph encoder ensemble algorithm.
  arXiv  Detail & Related papers  (2023-01-18T14:49:43Z)
• A Complex Network based Graph Embedding Method for Link Prediction [0.0]
  We present a novel graph embedding approach based on the popularity-similarity and local attraction paradigms. We show, using extensive experimental analysis, that the proposed method outperforms state-of-the-art graph embedding algorithms.
  arXiv  Detail & Related papers  (2022-09-11T14:46:38Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• Learning Graph Regularisation for Guided Super-Resolution [77.7568596501908]
  We introduce a novel formulation for guided super-resolution. Its core is a differentiable optimisation layer that operates on a learned affinity graph. We extensively evaluate our method on several datasets, and consistently outperform recent baselines in terms of quantitative reconstruction errors.
  arXiv  Detail & Related papers  (2022-03-27T13:12:18Z)
• Bayesian Graph Contrastive Learning [55.36652660268726]
  We propose a novel perspective of graph contrastive learning methods showing random augmentations leads to encoders. Our proposed method represents each node by a distribution in the latent space in contrast to existing techniques which embed each node to a deterministic vector. We show a considerable improvement in performance compared to existing state-of-the-art methods on several benchmark datasets.
  arXiv  Detail & Related papers  (2021-12-15T01:45:32Z)
• Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
  We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
  arXiv  Detail & Related papers  (2021-08-15T13:14:28Z)
• Multilayer Graph Clustering with Optimized Node Embedding [70.1053472751897]
  multilayer graph clustering aims at dividing the graph nodes into categories or communities. We propose a clustering-friendly embedding of the layers of a given multilayer graph. Experiments show that our method leads to a significant improvement.
  arXiv  Detail & Related papers  (2021-03-30T17:36:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.