The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges

• URL: http://arxiv.org/abs/2304.08242v3
• Date: Tue, 13 Feb 2024 14:14:14 GMT
• Title: The Deep Latent Position Topic Model for Clustering and Representation of Networks with Textual Edges
• Authors: R\'emi Boutin, Pierre Latouche, Charles Bouveyron
• Abstract summary: Deep-LPTM is a model-based clustering strategy based on a variational graph auto-encoder approach. The emails of the Enron company are analysed and visualisations of the results are presented.
• Score: 2.6334900941196087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerical interactions leading to users sharing textual content published by others are naturally represented by a network where the individuals are associated with the nodes and the exchanged texts with the edges. To understand those heterogeneous and complex data structures, clustering nodes into homogeneous groups as well as rendering a comprehensible visualisation of the data is mandatory. To address both issues, we introduce Deep-LPTM, a model-based clustering strategy relying on a variational graph auto-encoder approach as well as a probabilistic model to characterise the topics of discussion. Deep-LPTM allows to build a joint representation of the nodes and of the edges in two embeddings spaces. The parameters are inferred using a variational inference algorithm. We also introduce IC2L, a model selection criterion specifically designed to choose models with relevant clustering and visualisation properties. An extensive benchmark study on synthetic data is provided. In particular, we find that Deep-LPTM better recovers the partitions of the nodes than the state-of-the art ETSBM and STBM. Eventually, the emails of the Enron company are analysed and visualisations of the results are presented, with meaningful highlights of the graph structure.

Related papers

• Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks [50.42343781348247]
  We develop a graph Poisson factor analysis (GPFA) which provides analytic conditional posteriors to improve the inference accuracy. We also extend GPFA to a multi-stochastic-layer version named graph Poisson gamma belief network (GPGBN) to capture the hierarchical document relationships at multiple semantic levels. Our models can extract high-quality hierarchical latent document representations and achieve promising performance on various graph analytic tasks.
  arXiv  Detail & Related papers  (2024-10-13T02:22:14Z)
• Interactive dense pixel visualizations for time series and model attribution explanations [8.24039921933289]
  DAVOTS is an interactive visual analytics approach to explore raw time series data, activations of neural networks, and attributions in a dense-pixel visualization. We apply clustering approaches to the visualized data domains to highlight groups and present ordering strategies for individual and combined data exploration.
  arXiv  Detail & Related papers  (2024-08-27T14:02:21Z)
• Exploiting Contextual Target Attributes for Target Sentiment Classification [53.30511968323911]
  Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. We present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes.
  arXiv  Detail & Related papers  (2023-12-21T11:45:28Z)
• A GAN Approach for Node Embedding in Heterogeneous Graphs Using Subgraph Sampling [35.94125831564648]
  Our research addresses class imbalance issues in heterogeneous graphs using graph neural networks (GNNs) We propose a novel method combining the strengths of Generative Adversarial Networks (GANs) with GNNs, creating synthetic nodes and edges that effectively balance the dataset.
  arXiv  Detail & Related papers  (2023-12-11T16:52:20Z)
• Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning [78.49090351193269]
  We propose a novel graph-based framework to leverage the inter-relationships among different types of nuclei for WSI analysis. Specifically, we formulate the WSI as a heterogeneous graph with "nucleus-type" attribute to each node and a semantic attribute similarity to each edge. Our framework outperforms the state-of-the-art methods with considerable margins on various tasks.
  arXiv  Detail & Related papers  (2023-07-09T14:43:40Z)
• Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering [4.264842058017711]
  Community detection is a critical challenge in analysing real graphs. This article proposes three new, general, hierarchical frameworks to deal with this task. We compare over a hundred module combinations on the Block Model graphs and real-life datasets.
  arXiv  Detail & Related papers  (2023-03-21T22:12:53Z)
• 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)
• Interpolation-based Correlation Reduction Network for Semi-Supervised Graph Learning [49.94816548023729]
  We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN) In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Deep Embedded Clustering with Distribution Consistency Preservation for Attributed Networks [15.895606627146291]
  In this study, we propose an end-to-end deep embedded clustering model for attributed networks. It utilizes graph autoencoder and node attribute autoencoder to respectively learn node representations and cluster assignments. The proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-28T02:35:34Z)
• A Variational Edge Partition Model for Supervised Graph Representation Learning [51.30365677476971]
  This paper introduces a graph generative process to model how the observed edges are generated by aggregating the node interactions over a set of overlapping node communities. We partition each edge into the summation of multiple community-specific weighted edges and use them to define community-specific GNNs. A variational inference framework is proposed to jointly learn a GNN based inference network that partitions the edges into different communities, these community-specific GNNs, and a GNN based predictor that combines community-specific GNNs for the end classification task.
  arXiv  Detail & Related papers  (2022-02-07T14:37:50Z)
• Deep Fusion Clustering Network [38.540761683389135]
  We propose a Deep Fusion Clustering Network (DFCN) for deep clustering. In our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder. Experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods.
  arXiv  Detail & Related papers  (2020-12-15T09:37:59Z)

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.