Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding

• URL: http://arxiv.org/abs/2312.16834v1
• Date: Thu, 28 Dec 2023 05:39:33 GMT
• Title: Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding
• Authors: Kamel Abdous, Nairouz Mrabah, Mohamed Bouguessa
• Abstract summary: HMGE is a novel embedding method based on hierarchical aggregation for high-dimensional multiplex graphs. We leverage mutual information between local patches and global summaries to train the model without supervision. Detailed experiments on synthetic and real-world data illustrate the suitability of our approach to downstream supervised tasks.
• Score: 7.271256448682229
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the problem of multiplex graph embedding, that is, graphs in which nodes interact through multiple types of relations (dimensions). In recent years, several methods have been developed to address this problem. However, the need for more effective and specialized approaches grows with the production of graph data with diverse characteristics. In particular, real-world multiplex graphs may exhibit a high number of dimensions, making it difficult to construct a single consensus representation. Furthermore, important information can be hidden in complex latent structures scattered in multiple dimensions. To address these issues, we propose HMGE, a novel embedding method based on hierarchical aggregation for high-dimensional multiplex graphs. Hierarchical aggregation consists of learning a hierarchical combination of the graph dimensions and refining the embeddings at each hierarchy level. Non-linear combinations are computed from previous ones, thus uncovering complex information and latent structures hidden in the multiplex graph dimensions. Moreover, we leverage mutual information maximization between local patches and global summaries to train the model without supervision. This allows to capture of globally relevant information present in diverse locations of the graph. Detailed experiments on synthetic and real-world data illustrate the suitability of our approach to downstream supervised tasks, including link prediction and node classification.

Related papers

• DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts [70.21017141742763]
  Graph neural networks (GNNs) are gaining popularity for processing graph-structured data. Existing methods generally use a fixed number of GNN layers to generate representations for all graphs. We propose the depth adaptive mixture of expert (DA-MoE) method, which incorporates two main improvements to GNN.
  arXiv  Detail & Related papers  (2024-11-05T11:46:27Z)
• Representation learning in multiplex graphs: Where and how to fuse information? [5.0235828656754915]
  Multiplex graphs possess richer information, provide better modeling capabilities and integrate more detailed data from potentially different sources. In this paper, we tackle the problem of learning representations for nodes in multiplex networks in an unsupervised or self-supervised manner. We propose improvements in how to construct GNN architectures that deal with multiplex graphs.
  arXiv  Detail & Related papers  (2024-02-27T21:47:06Z)
• GrannGAN: Graph annotation generative adversarial networks [72.66289932625742]
  We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases. In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features.
  arXiv  Detail & Related papers  (2022-12-01T11:49:07Z)
• High-order Multi-view Clustering for Generic Data [15.764819403555512]
  Graph-based multi-view clustering has achieved better performance than most non-graph approaches. We introduce an approach called high-order multi-view clustering (HMvC) to explore the topology structure information of generic data.
  arXiv  Detail & Related papers  (2022-09-22T07:49:38Z)
• SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
  This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations. We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path. Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path. Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
  arXiv  Detail & Related papers  (2021-12-12T14:18:18Z)
• Semi-Supervised Deep Learning for Multiplex Networks [20.671777884219555]
  Multiplex networks are complex graph structures in which a set of entities are connected to each other via multiple types of relations. We present a novel semi-supervised approach for structure-aware representation learning on multiplex networks.
  arXiv  Detail & Related papers  (2021-10-05T13:37:43Z)
• 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)
• Multiple Graph Learning for Scalable Multi-view Clustering [26.846642220480863]
  We propose an efficient multiple graph learning model via a small number of anchor points and tensor Schatten p-norm minimization. Specifically, we construct a hidden and tractable large graph by anchor graph for each view. We develop an efficient algorithm, which scales linearly with the data size, to solve our proposed model.
  arXiv  Detail & Related papers  (2021-06-29T13:10:56Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Multilayer Clustered Graph Learning [66.94201299553336]
  We use contrastive loss as a data fidelity term, in order to properly aggregate the observed layers into a representative graph. Experiments show that our method leads to a clustered clusters w.r.t. We learn a clustering algorithm for solving clustering problems.
  arXiv  Detail & Related papers  (2020-10-29T09:58:02Z)
• Learning Multi-layer Graphs and a Common Representation for Clustering [13.90938823562779]
  We focus on graph learning from multi-view data of shared entities for spectral clustering. We propose an efficient solver based on alternating minimization to solve the problem. Numerical experiments on synthetic and real datasets demonstrate that the proposed algorithm outperforms state-of-the-art multi-view clustering techniques.
  arXiv  Detail & Related papers  (2020-10-23T11:12:43Z)

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.