Graph Neural Networks (GNNs) have shown immense potential in improving the performance of large-scale models by effectively incorporating structured relational information.However, current approaches face two key challenges: (1) achieving robust semantic alignment between graph representations and large models, and (2) ensuring interpretability in the generated outputs.To address these challenges, we propose ExGLM (Explainable Graph Language Model), a novel training framework designed to seamlessly integrate graph and language modalities while enhancing transparency.Our framework introduces two core components: (1) a graph-language synergistic alignment module, which aligns graph structures with language model to ensure semantic consistency across modalities; and (2) a Judgeand-Improve paradigm, which allows the language model to iteratively evaluate, refine, and prioritize responses with higher interpretability, thereby improving both performance and transparency.Extensive experiments conducted on three benchmark datasets-ogbn-arxiv, Cora, and PubMed-demonstrate that ExGLM not only surpasses existing methods in efficiency but also generates outputs that are significantly more interpretable, effectively addressing the primary limitations of current approaches.

Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting methods often overlook two important characteristics of cumulative data, namely monotonicity and irregularity, which limit their practical applicability. To address this limitation, we propose a principled approach called Monotonic neural Ordinary Differential Equation (MODE) within the framework of neural ordinary differential equations. By leveraging MODE, we are able to effectively capture and represent the monotonicity and irregularity in practical cumulative data. Through extensive experiments conducted in a bonus allocation scenario, we demonstrate that MODE outperforms state-of-the-art methods, showcasing its ability to handle both monotonicity and irregularity in cumulative data and delivering superior forecasting performance.

Detecting and diagnosing anomalies in observational data plays a crucial role in various real-world applications, such as e-commerce applet maintenance. Unsupervised machine learning techniques are typically employed for anomaly detection and diagnosis due to their convenience and independence from labeled data. Density estimation (DE), as one of the most widely used unsupervised machine learning techniques for anomaly detection, can be categorized into kernel density estimation (KDE)-based methods and normalizing flow (NF)-based methods. While KDE-based methods offer fast computation speed, they often ignore the complex manifold structure present in observational data. On the other hand, NF-based methods address the manifold issue but suffer from longer computation times. In this study, we propose a novel DE-based anomaly detection & diagnosis method using Stein Variational Gradient Descent (SVGD), aiming to leverage the strengths of KDE and NF approaches. Firstly, we rigorously derive the DE capability of SVGD through mathematical analysis. Subsequently, we demonstrate the ability of the SVGD method to perform anomaly diagnosis based on input feature attribution. Finally, to validate the effectiveness of our approach, we conduct experiments using synthetic, benchmark, and industrial datasets. The results demonstrate the superior performance and practical applicability of our proposed method.

Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues. Methods in the Entire Space Multi-task Model (ESMM) family leverage the sequential pattern of user actions, \ie $impression\rightarrow click \rightarrow conversion$ to address data sparsity issue. However, they still fail to ensure the unbiasedness of CVR estimates. In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB) for CVR estimation, where the CVR estimate is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where ESMM might overlook the causality from click to conversion. To this end, we devise a principled approach named Entire Space Counterfactual Multi-task Modelling (ESCM$^2$), which employs a counterfactual risk miminizer as a regularizer in ESMM to address both IEB and PIP issues simultaneously. Extensive experiments on offline datasets and online environments demonstrate that our proposed ESCM$^2$ can largely mitigate the inherent IEB and PIP issues and achieve better performance than baseline models.