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Rethinking Node Representation Interpretation through Relation Coherence

Ying-Chun Lin, Jennifer Neville, Cassiano Becker, Purvanshi Metha, Nabiha Asghar, Vipul Agarwal

TL;DR

A novel interpretation method-Node Coherence Rate for Representation Interpretation (NCI) is proposed, which quantifies how well different node relations are captured in node representations and also proposes a novel method (IME) to evaluate the accuracy of different interpretation methods.

Abstract

Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on explainable AI for node representations has primarily emphasized explanations (reasons for model predictions) rather than interpretations (mapping representations to understandable concepts). Furthermore, the limited research that focuses on interpretation lacks validation, and thus the reliability of such methods is unclear. We address this gap by proposing a novel interpretation method-Node Coherence Rate for Representation Interpretation (NCI)-which quantifies how well different node relations are captured in node representations. We also propose a novel method (IME) to evaluate the accuracy of different interpretation methods. Our experimental results demonstrate that NCI reduces the error of the previous best approach by an average of 39%. We then apply NCI to derive insights about the node representations produced by several graph-based methods and assess their quality in unsupervised settings.

Rethinking Node Representation Interpretation through Relation Coherence

TL;DR

A novel interpretation method-Node Coherence Rate for Representation Interpretation (NCI) is proposed, which quantifies how well different node relations are captured in node representations and also proposes a novel method (IME) to evaluate the accuracy of different interpretation methods.

Abstract

Understanding node representations in graph-based models is crucial for uncovering biases ,diagnosing errors, and building trust in model decisions. However, previous work on explainable AI for node representations has primarily emphasized explanations (reasons for model predictions) rather than interpretations (mapping representations to understandable concepts). Furthermore, the limited research that focuses on interpretation lacks validation, and thus the reliability of such methods is unclear. We address this gap by proposing a novel interpretation method-Node Coherence Rate for Representation Interpretation (NCI)-which quantifies how well different node relations are captured in node representations. We also propose a novel method (IME) to evaluate the accuracy of different interpretation methods. Our experimental results demonstrate that NCI reduces the error of the previous best approach by an average of 39%. We then apply NCI to derive insights about the node representations produced by several graph-based methods and assess their quality in unsupervised settings.

Paper Structure

This paper contains 24 sections, 2 theorems, 18 equations, 8 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Interpretation Method Evaluation
  4. Node Coherence Rate for Representation Interpretation
  5. Weak Relation Coherence
  6. Clustering Coherence
  7. Smoothness Coherence
  8. Interpretation Scores
  9. Experiments
  10. Node Relations
  11. RQ1: Accuracy of Interpretation Methods
  12. RQ2-1:Node Representation Interpretation
  13. RQ2-2: Correlation between Task Performance and Coherence Rate
  14. RQ2-3: Model Selection
  15. Related Work
  16. ...and 9 more sections

Key Result

Lemma 3.1

Let $U\Sigma U^{\top}$ be the EVD of $S_r$, where $U$ is a square matrix containing eigenvectors and $\Sigma$ is a diagonal matrix containing eigenvalues. The embeddings can be generated based on $Z_r=U_{|V|\times d}(\Sigma_{d\times d})^{1/2}$ with dimension $d\leq|\mathcal{V}|$, assuming that the e

Figures (8)

  • Figure 1: Illustration of representation interpretation process and Interpretation Method Evaluation (IME) process.
  • Figure 2: Illustration of IME process of evaluating the interpretation accuracy for interpretation method $m$.
  • Figure 3: Interpreting Node Representations with NCI. A high coherence rate of a node relation suggests that the model captures the node relation well in its embedding space.
  • Figure 4: When the embedding is less expressive, our NCI has better interpretation accuracy (MRR) compared to other representation interpretation methods.
  • Figure 5: Model Coherence Score for Model Selection in unsupervised settings. When a model coherence score of a model is higher, the model tends to have better performance on downstream tasks.
  • ...and 3 more figures

Theorems & Definitions (12)

  • Definition 2.1
  • Definition 2.2
  • Definition 2.3
  • Definition 3.1
  • Definition 3.2
  • Lemma 3.1
  • Definition 4.1
  • Definition 4.2
  • Theorem 4.1
  • Definition 4.3
  • ...and 2 more