Temporal Graph Neural Networks (TGNN) have the ability to capture both the
graph topology and dynamic dependencies of interactions within a graph over
time. There has been a growing need to explain the predictions of TGNN models
due to the difficulty in identifying how past events influence their
predictions. Since the explanation model for a static graph cannot be readily
applied to temporal graphs due to its inability to capture temporal
dependencies, recent studies proposed explanation models for temporal graphs.
However, existing explanation models for temporal graphs rely on post-hoc
explanations, requiring separate models for prediction and explanation, which
is limited in two aspects: efficiency and accuracy of explanation. In this
work, we propose a novel built-in explanation framework for temporal graphs,
called Self-Explainable Temporal Graph Networks based on Graph Information
Bottleneck (TGIB). TGIB provides explanations for event occurrences by
introducing stochasticity in each temporal event based on the Information
Bottleneck theory. Experimental results demonstrate the superiority of TGIB in
terms of both the link prediction performance and explainability compared to
state-of-the-art methods. This is the first work that simultaneously performs
prediction and explanation for temporal graphs in an end-to-end manner.

基于图信息瓶颈的自解释时态图网络（TGIB）是一种新的内置解释框架，能够同时对事件发生和解释进行预测，通过引入信息瓶颈理论中的随机性来对事件发生提供解释。实验结果表明，与现有方法相比，TGIB 在链接预测性能和可解释性方面具有优势。

基于图信息瓶颈的自解释时变图网络

Self-Explainable Temporal Graph Networks based on Graph Information  Bottleneck

In some situations, the interpretability of the machine learning models plays
a role as important as the model accuracy. Interpretability comes from the need
to trust the prediction model, verify some of its properties, or even enforce
them to improve fairness. Many model-agnostic explanatory methods exists to
provide explanations for black-box models. In the regression task, the
practitioner can use white-boxes or gray-boxes models to achieve more
interpretable results, which is the case of symbolic regression. When using an
explanatory method, and since interpretability lacks a rigorous definition,
there is a need to evaluate and compare the quality and different explainers.
This paper proposes a benchmark scheme to evaluate explanatory methods to
explain regression models, mainly symbolic regression models. Experiments were
performed using 100 physics equations with different interpretable and
non-interpretable regression methods and popular explanation methods,
evaluating the performance of the explainers performance with several
explanation measures. In addition, we further analyzed four benchmarks from the
GP community. The results have shown that Symbolic Regression models can be an
interesting alternative to white-box and black-box models that is capable of
returning accurate models with appropriate explanations. Regarding the
explainers, we observed that Partial Effects and SHAP were the most robust
explanation models, with Integrated Gradients being unstable only with
tree-based models. This benchmark is publicly available for further
experiments.

建议评估解释性方法以解释回归模型，特别是符号回归模型。在实验中，我们使用各种可解释和不可解释的回归方法以及流行的解释方法评估了解释器的性能，并发现符号回归模型是一种能够提供准确模型和合适解释的有趣选择。最稳定的解释模型是 Partial Effects 和 SHAP，而 Integrated Gradients 在基于树的模型中不稳定。此基准可供进一步实验使用。

符号回归中的可解释性：使用费曼数据集的解释方法基准

Interpretability in Symbolic Regression: a benchmark of Explanatory  Methods using the Feynman data set

Existing explanation models generate only text for recommendations but still
struggle to produce diverse contents. In this paper, to further enrich
explanations, we propose a new task named personalized showcases, in which we
provide both textual and visual information to explain our recommendations.
Specifically, we first select a personalized image set that is the most
relevant to a user's interest toward a recommended item. Then, natural language
explanations are generated accordingly given our selected images. For this new
task, we collect a large-scale dataset from Google Local (i.e.,~maps) and
construct a high-quality subset for generating multi-modal explanations. We
propose a personalized multi-modal framework which can generate diverse and
visually-aligned explanations via contrastive learning. Experiments show that
our framework benefits from different modalities as inputs, and is able to
produce more diverse and expressive explanations compared to previous methods
on a variety of evaluation metrics.

本研究提出了一种名为个性化展示的新任务，以更丰富的信息形式生成推荐解释，通过对来自 Google Local 的大规模数据集的构建和收集，运用对比学习的方法构建了一个个性化多模态框架，利用多种输入模态生成多样化和可视化的解释，该框架在多种评估指标上比之前的方法有更多元化和表现力。