Disentangled and Self-Explainable Node Representation Learning

• URL: http://arxiv.org/abs/2410.21043v1
• Date: Mon, 28 Oct 2024 13:58:52 GMT
• Title: Disentangled and Self-Explainable Node Representation Learning
• Authors: Simone Piaggesi, André Panisson, Megha Khosla,
• Abstract summary: We introduce DiSeNE, a framework that generates self-explainable embeddings in an unsupervised manner. Our method employs disentangled representation learning to produce dimension-wise interpretable embeddings. We formalize novel desiderata for disentangled and interpretable embeddings, which drive our new objective functions.
• Score: 1.4002424249260854
• License:
• Abstract: Node representations, or embeddings, are low-dimensional vectors that capture node properties, typically learned through unsupervised structural similarity objectives or supervised tasks. While recent efforts have focused on explaining graph model decisions, the interpretability of unsupervised node embeddings remains underexplored. To bridge this gap, we introduce DiSeNE (Disentangled and Self-Explainable Node Embedding), a framework that generates self-explainable embeddings in an unsupervised manner. Our method employs disentangled representation learning to produce dimension-wise interpretable embeddings, where each dimension is aligned with distinct topological structure of the graph. We formalize novel desiderata for disentangled and interpretable embeddings, which drive our new objective functions, optimizing simultaneously for both interpretability and disentanglement. Additionally, we propose several new metrics to evaluate representation quality and human interpretability. Extensive experiments across multiple benchmark datasets demonstrate the effectiveness of our approach.

Related papers

• DINE: Dimensional Interpretability of Node Embeddings [3.3040172566302206]
  Graph representation learning methods, such as node embeddings, are powerful approaches to map nodes into a latent vector space. We develop new metrics that measure the global interpretability of embedding vectors. We then introduce DINE, a novel approach that can retrofit existing node embeddings by making them more interpretable without sacrificing their task performance.
  arXiv  Detail & Related papers  (2023-10-02T12:47:42Z)
• Augment to Interpret: Unsupervised and Inherently Interpretable Graph Embeddings [0.0]
  In this paper, we study graph representation learning and we show that data augmentation that preserves semantics can be learned and used to produce interpretations. Our framework, which we named INGENIOUS, creates inherently interpretable embeddings and eliminates the need for costly additional post-hoc analysis.
  arXiv  Detail & Related papers  (2023-09-28T16:21:40Z)
• Self-Supervised Node Representation Learning via Node-to-Neighbourhood Alignment [10.879056662671802]
  Self-supervised node representation learning aims to learn node representations from unlabelled graphs that rival the supervised counterparts. In this work, we present simple-yet-effective self-supervised node representation learning via aligning the hidden representations of nodes and their neighbourhood. We learn node representations that achieve promising node classification performance on a set of graph-structured datasets from small- to large-scale.
  arXiv  Detail & Related papers  (2023-02-09T13:21:18Z)
• Sparse Relational Reasoning with Object-Centric Representations [78.83747601814669]
  We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric representations. We find that increasing sparsity, especially on features, improves the performance of some models and leads to simpler relations.
  arXiv  Detail & Related papers  (2022-07-15T14:57:33Z)
• 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)
• Towards Explanation for Unsupervised Graph-Level Representation Learning [108.31036962735911]
  Existing explanation methods focus on the supervised settings, eg, node classification and graph classification, while the explanation for unsupervised graph-level representation learning is still unexplored. In this paper, we advance the Information Bottleneck principle (IB) to tackle the proposed explanation problem for unsupervised graph representations, which leads to a novel principle, textitUnsupervised Subgraph Information Bottleneck (USIB) We also theoretically analyze the connection between graph representations and explanatory subgraphs on the label space, which reveals that the robustness of representations benefit the fidelity of explanatory subgraphs.
  arXiv  Detail & Related papers  (2022-05-20T02:50:15Z)
• Explaining, Evaluating and Enhancing Neural Networks' Learned Representations [2.1485350418225244]
  We show how explainability can be an aid, rather than an obstacle, towards better and more efficient representations. We employ such attributions to define two novel scores for evaluating the informativeness and the disentanglement of latent embeddings. We show that adopting our proposed scores as constraints during the training of a representation learning task improves the downstream performance of the model.
  arXiv  Detail & Related papers  (2022-02-18T19:00:01Z)
• 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)
• From Canonical Correlation Analysis to Self-supervised Graph Neural Networks [99.44881722969046]
  We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data. We optimize an innovative feature-level objective inspired by classical Canonical Correlation Analysis. Our method performs competitively on seven public graph datasets.
  arXiv  Detail & Related papers  (2021-06-23T15:55:47Z)
• Closed-Form Factorization of Latent Semantics in GANs [65.42778970898534]
  A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. We propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.
  arXiv  Detail & Related papers  (2020-07-13T18:05:36Z)
• Deep Graph Contrastive Representation Learning [23.37786673825192]
  We propose a novel framework for unsupervised graph representation learning by leveraging a contrastive objective at the node level. Specifically, we generate two graph views by corruption and learn node representations by maximizing the agreement of node representations in these two views. We perform empirical experiments on both transductive and inductive learning tasks using a variety of real-world datasets.
  arXiv  Detail & Related papers  (2020-06-07T11:50:45Z)

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.