Signed Graph Neural Networks (SGNNs) have been shown to be effective in analyzing complex patterns in real-world situations where positive and negative links coexist. However, SGNN models suffer from poor explainability, which limit their adoptions in critical scenarios that require understanding the rationale behind predictions. To the best of our knowledge, there is currently no research work on the explainability of the SGNN models. Our goal is to address the explainability of decision-making for the downstream task of link sign prediction specific to signed graph neural networks. Since post-hoc explanations are not derived directly from the models, they may be biased and misrepresent the true explanations. Therefore, in this paper we introduce a Self-Explainable Signed Graph transformer (SE-SGformer) framework, which can not only outputs explainable information while ensuring high prediction accuracy. Specifically, We propose a new Transformer architecture for signed graphs and theoretically demonstrate that using positional encoding based on signed random walks has greater expressive power than current SGNN methods and other positional encoding graph Transformer-based approaches. We constructs a novel explainable decision process by discovering the $K$-nearest (farthest) positive (negative) neighbors of a node to replace the neural network-based decoder for predicting edge signs. These $K$ positive (negative) neighbors represent crucial information about the formation of positive (negative) edges between nodes and thus can serve as important explanatory information in the decision-making process. We conducted experiments on several real-world datasets to validate the effectiveness of SE-SGformer, which outperforms the state-of-the-art methods by improving 2.2\% prediction accuracy and 73.1\% explainablity accuracy in the best-case scenario.
xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links
Institute of Informatics, LMU Munich（慕尼黑大学信息学研究所）；Corporate Technology, Siemens AG（西门子股份公司中央研究院）；Department of Informatics, Technical University of Munich（慕尼黑工业大学信息学系）
B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
University of Edinburgh（爱丁堡大学）；Jagiellonian University（雅盖隆大学）；Jagiellonian University, Faculty of Mathematics and Computer Science（雅盖隆大学数学与计算机科学学院）；Jagiellonian University, Faculty of Chemistry（雅盖隆大学化学学院）；Ardigen SA（Ardigen公司）
The Road to Learning Explainable Inverse Kinematic Models: Graph Neural Networks as Inductive Bias for Symbolic Regression
Faculty of Computer Science, Otto-von-Guericke-University Magdeburg（马格德堡奥托·冯·格里克大学计算机科学学院）；Fraunhofer Institute for Transportation and Infrastructure Systems IVI（弗劳恩霍夫运输与基础设施系统研究所IVI）
Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings
LIX, École Polytechnique（巴黎综合理工学院LIX实验室）；Institute Polytechnique de Paris（巴黎理工学院）；Université Paris Saclay（巴黎萨克雷大学）；Université Paris Cité（巴黎西岱大学）；ENS Paris Saclay（巴黎萨克雷高等师范学校）；CNRS（法国国家科学研究中心）；INSERM（法国国家健康与医学研究院）；Centre Borelli（博雷利中心）；University of Peloponnese（伯罗奔尼撒大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
University of Cambridge（剑桥大学）；Mila - Quebec AI Institute（魁北克人工智能研究院）；McGill University（麦吉尔大学）；University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；University of Stuttgart（斯图加特大学）；Amazon（亚马逊公司）；AITHYRA
Predicting Delayed Train Trajectories on the Dutch Railway Network: Explainable AI Evaluation of Topological, Operational and Weather Features with Tree Based Ensemble Methods
Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls
Yale Institute for Network Science, Yale University, USA（耶鲁网络科学研究所，耶鲁大学，美国）；Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, France（巴黎萨克雷大学，巴黎城市大学，巴黎萨克雷高等学院，法国国家科学研究中心，SSA，INSERM，Borelli中心，法国）；École Polytechnique, Paris, France（巴黎高等理工学院，巴黎，法国）；Department of Informatics and Telecommunications, University of Peloponnese, Greece（信息与电信系，希腊皮埃利翁大学）
RaX-Crash: A Resource Efficient and Explainable Small Model Pipeline with an Application to City Scale Injury Severity Prediction
1 Department of Computer Science；Engineering, Santa Clara University, Santa Clara, USA Email；2 Manning College of Information \& Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA Email；3 Department of Electrical；Computer Engineering, Carnegie Mellon University, Pittsburgh, USA Email；4 University of Pennsylvania, Philadelphia, USA Email
Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands（杰罗尼穆斯数据科学学院，埃因霍温理工大学，埃因霍温，荷兰）；Haskoning, Amersfoort, The Netherlands（哈索宁，阿默斯福尔特，荷兰）；School of Industrial Engineering, Eindhoven University of Technology（工业工程学院，埃因霍温理工大学）
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph
Tsinghua University（清华大学）；University of Southern California（南加利福尼亚大学）
可解释性和有效性是构建推荐系统的两个关键方面。以前的努力主要集中在合并边信息，以实现更好的推荐性能。然而，这些方法具有一些弱点：（1）基于神经网络的嵌入方法的预测难以解释和调试;（2）基于符号、图形的方法（例如，基于Meta路径的模型）需要人工努力和领域知识来定义模式和规则，并且忽略项目关联类型（例如，可替换的和互补的）。在本文中，我们提出了一种新的联合学习框架集成\textit{induction of explainable rules from knowledge graph}与\textit{construction of a rule-guided neural recommendation model}。该框架鼓励两个模块相互补充，以产生有效的和可解释的推荐：1）归纳规则，从以项目为中心的知识图谱中挖掘，总结常见的多跳关系模式，用于推断不同的项目关联，并为模型预测提供人类可读的解释; 2）推荐模块可以通过归纳规则进行扩充，从而在处理冷启动问题时具有更好的泛化能力。大量的实验\footnote{Code and data can be found at: \url{https://github.com/THUIR/RuleRec}}表明，我们提出的方法已经取得了显着的改善，在现实世界的数据集上的基线项目推荐。我们的模型在通过将项目名称链接到相关实体而生成的“嘈杂”项目知识…
Explainable Forecasting of Scientific Breakthroughs from Concept Network Dynamics
Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland（日内瓦经济管理学院，日内瓦大学，瑞士日内瓦）；Faculty of Medicine, University of Geneva, Geneva, Switzerland（日内瓦大学医学学院，瑞士日内瓦）；TU Eindhoven, The Netherlands（埃因霍温理工大学，荷兰）；Open Quantum Institute, CERN, Geneva, Switzerland（开放量子研究所，欧洲核子研究中心，瑞士日内瓦）；armasuisse Science + Technology, Switzerland（armasuisse 科学与技术，瑞士）
Does Explainable Artificial Intelligence Improve Human Decision-Making?
EXPEC Computer Center（EXPEC计算机中心）；Saudi Aramco（沙特阿美公司）；U.S. Army Combat Capabilities Development Command Army Research Laboratory South（美国陆军作战能力发展司令部南部陆军研究实验室）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of Texas at Dallas（德克萨斯大学达拉斯分校）
Domain-Adversarial Neural Network and Explainable AI for Reducing Tissue-of-Origin Signal in Pan-cancer Mortality Classification
Instituto Nacional de Medicina Genómica (INMEGEN)（国家基因组医学研究所）；Universidad Nacional Autónoma de México (UNAM)（墨西哥国立自治大学）；Centro de Ciencias de la Complejidad (C3)（复杂科学中心）
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable Neural Network for Dynamic Physical Processes
Oden Institute for Computational Engineering and Sciences（奥登计算工程与科学研究所）；Department of Computer Science（计算机科学系）；School of Information（信息学院）；University of Texas, Austin（得克萨斯大学奥斯汀分校）
Explainable Autoencoder-Based Anomaly Detection in IEC 61850 GOOSE Networks
Universidad Rey Juan Carlos（雷奥恩卡洛斯大学）；Red Eléctrica (REDEIA Group)（电力公司（REDEIA小组））；Centro de Investigación for Data, Complex Networks and Cybersecurity Sciences（数据、复杂网络和网络安全科学研究中心）
