A network community detection method with integration of data from multiple layers and node attributes

• URL: http://arxiv.org/abs/2305.13012v1
• Date: Mon, 22 May 2023 13:15:36 GMT
• Title: A network community detection method with integration of data from multiple layers and node attributes
• Authors: Hannu Reittu, Lasse Leskel\"a, Tomi R\"aty
• Abstract summary: We suggest a simple way of representing network data in a data matrix where rows correspond to the nodes, and columns correspond to the data items. For compressing a data matrix, we suggest to extend so called regular decomposition method for non-square matrices. We illustrate our method with synthetic power-law graphs and two real networks: an Internet autonomous systems graph and a world airline graph.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multilayer networks are in the focus of the current complex network study. In such networks multiple types of links may exist as well as many attributes for nodes. To fully use multilayer -- and other types of complex networks in applications, the merging of various data with topological information renders a powerful analysis. First, we suggest a simple way of representing network data in a data matrix where rows correspond to the nodes, and columns correspond to the data items. The number of columns is allowed to be arbitrary, so that the data matrix can be easily expanded by adding columns. The data matrix can be chosen according to targets of the analysis, and may vary a lot from case to case. Next, we partition the rows of the data matrix into communities using a method which allows maximal compression of the data matrix. For compressing a data matrix, we suggest to extend so called regular decomposition method for non-square matrices. We illustrate our method for several types of data matrices, in particular, distance matrices, and matrices obtained by augmenting a distance matrix by a column of node degrees, or by concatenating several distances matrices corresponding to layers of a multilayer network. We illustrate our method with synthetic power-law graphs and two real networks: an Internet autonomous systems graph and a world airline graph. We compare the outputs of different community recovery methods on these graphs, and discuss how incorporating node degrees as a separate column to the data matrix leads our method to identify community structures well-aligned with tiered hierarchical structures commonly encountered in complex scale-free networks.

Related papers

• Multilayer Graph Approach to Deep Subspace Clustering [0.0]
  Deep subspace clustering (DSC) networks based on self-expressive model learn representation matrix, often implemented in terms of fully connected network. Here, we apply selected linear subspace clustering algorithm to learn representation from representations learned by all layers of encoder network including the input data. We validate proposed approach on four well-known datasets with two DSC networks as baseline models.
  arXiv  Detail & Related papers  (2024-01-30T14:09:41Z)
• An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
  Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks. The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions. We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
  arXiv  Detail & Related papers  (2023-09-30T15:57:14Z)
• Attributed Multi-order Graph Convolutional Network for Heterogeneous Graphs [29.618952407794783]
  We propose anAttributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically studies meta-paths containing multi-hop neighbors. AMOGCN gains superior semi-supervised classification performance compared with state-of-the-art competitors.
  arXiv  Detail & Related papers  (2023-04-13T08:31:16Z)
• Exploring ordered patterns in the adjacency matrix for improving machine learning on complex networks [0.0]
  The proposed methodology employs a sorting algorithm to rearrange the elements of the adjacency matrix of a complex graph in a specific order. The resulting sorted adjacency matrix is then used as input for feature extraction and machine learning algorithms to classify the networks.
  arXiv  Detail & Related papers  (2023-01-20T00:01:23Z)
• Learning Feature Aggregation for Deep 3D Morphable Models [57.1266963015401]
  We propose an attention based module to learn mapping matrices for better feature aggregation across hierarchical levels. Our experiments show that through the end-to-end training of the mapping matrices, we achieve state-of-the-art results on a variety of 3D shape datasets.
  arXiv  Detail & Related papers  (2021-05-05T16:41:00Z)
• Deep Two-way Matrix Reordering for Relational Data Analysis [41.60125423028092]
  Matrix reordering is a task to permute rows and columns of a given observed matrix. We propose a new matrix reordering method, Deep Two-way Matrix Reordering (DeepTMR), using a neural network model. We demonstrate the effectiveness of proposed DeepTMR by applying it to both synthetic and practical data sets.
  arXiv  Detail & Related papers  (2021-03-26T01:31:24Z)
• Deep Learning Approach for Matrix Completion Using Manifold Learning [3.04585143845864]
  This paper introduces a new latent variables model for data matrix which is a combination of linear and nonlinear models. We design a novel deep-neural-network-based matrix completion algorithm to address both linear and nonlinear relations among entries of data matrix.
  arXiv  Detail & Related papers  (2020-12-11T01:01:54Z)
• 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)
• Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix [57.11971786407279]
  Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data. This paper proposes a multi-view spectral clustering algorithm that learns a high-order optimal neighborhood Laplacian matrix. Our proposed algorithm generates the optimal Laplacian matrix by searching the neighborhood of the linear combination of both the first-order and high-order base.
  arXiv  Detail & Related papers  (2020-08-31T12:28:40Z)
• Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems [107.3868459697569]
  We introduce an eigendecomposition-free approach to training a deep network. We show that our approach is much more robust than explicit differentiation of the eigendecomposition. Our method has better convergence properties and yields state-of-the-art results.
  arXiv  Detail & Related papers  (2020-04-15T04:29:34Z)
• Multi-view Deep Subspace Clustering Networks [64.29227045376359]
  Multi-view subspace clustering aims to discover the inherent structure of data by fusing multiple views of complementary information. We propose the Multi-view Deep Subspace Clustering Networks (MvDSCN), which learns a multi-view self-representation matrix in an end-to-end manner. The MvDSCN unifies multiple backbones to boost clustering performance and avoid the need for model selection.
  arXiv  Detail & Related papers  (2019-08-06T06:44:43Z)

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.