Temporal Graph Neural Networks (TGNNs) are increasingly applied in dynamic scenarios, however, their limited explainability hinders their adoption in high-stakes domains. Existing methods tend to conflate causality with temporal proximity, leading to ambiguous explanations that mix impactful and irrelevant events. Moreover, they lack counterfactual reasoning to assess whether altering specific temporal events would change TGNN predictions. To overcome these challenges, we propose CTM-Explainer, which identifies critical temporal dependencies through iterative “what-if” perturbation analysis. To the best of our knowledge, this is the first post-hoc counterfactual explanation framework for TGNN. It enables precise attribution of how specific timestamped events influence TGNN predictions. By embedding causal analysis into a reinforcement learning framework, CTM-Explainer constructs Counterfactual Temporal Motifs (CTMs) that are causally grounded in model outcome shifts via interventional probability estimation. This design eliminates temporally correlated but non-essential events, while preserving those with verified causal influence. Extensive experiments on real-world and synthetic datasets confirm that CTM-Explainer generates more faithful and concise explanations than existing methods, at significantly lower computational cost.

Contrastive Language-Image Pre-training (CLIP) has emerged as an effective framework for multi-modal representation learning, achieving notable success in diverse tasks such as medical image analysis. CLIP's growing prominence in medical image applications is restricted by its significant computational demands, creating implementation challenges in resource-constrained clinical environments. While knowledge distillation offers an effective approach for model compression with preserved accuracy, existing methods suffer from two fundamental limitations. Firstly, existing methods focus on learning better information from single models while ignoring the fact that student models can generalize well under the guidance of multiple teachers. Secondly, they overlook the complementary information in the CLIP model where the text encoder and image encoder can be leveraged as heterogeneous information to teach one single modality. To tackle these challenges, we propose an Adaptive Heterogeneous Multi-teacher Knowledge Distillation (AdaHet-MKD) framework for effective knowledge transfer across heterogeneous text-image models and among multiple teacher models. The key innovations include: (i) adaptively determining the contribution of each teacher model to specific instances, thereby generating integrated soft logits, and (ii) enabling the student model to operate independently of the teacher model's architecture, which enhances flexibility in teacher-student pairings. Experimental evaluations on publicly available medical datasets demonstrate that our approach has achieved the state-of-the-art performance compared to baselines.

The drug recommendation task aims to provide effective and safe prescription decision support for clinical treatment based on patients' past Electronic Health Records (EHR). However, the prevalent phenomenon of missing views in multi-source heterogeneous EHR data may cause performance degradation. This is due to the lack of sufficient information and increased learning difficulties, which limit the practical effectiveness of drug recommendation models in medical applications. In this paper, we emphasize the problems of incompleteness in practical drug recommendation and propose the Incomplete Multi-View Drug Recommendation model via Multi-Level Representation Learning and Curriculum Learning named IMDR. In particular, IMDR employs a Multi-Level Representation Learning architecture equipped with a Medical Code-Level Drug Knowledge Infusion Module and a Visit-Level Cross-View Information Module for patient representation learning to overcome the information loss caused by incomplete data. And then, a Gaussian-guided curriculum learning strategy is proposed to assist the learning process of IMDR with a novel difficulty measure to achieve effective progressive learning under missing medical views. Systematic evaluation on two large-scale real-world medical datasets, MIMIC-III and MIMIC-IV, demonstrates that IMDR reduces the Drug-Drug Interaction (DDI) rate by 2.97% compared to existing state-of-the-art drug recommendation baselines, while achieving significant improvements of 3.29% and 1.97% in Jaccard similarity scores and F1 score, respectively. Furthermore, compared to advanced incomplete multi-view learning (IML) models, IMDR's advantages in Jaccard similarity scores and F1 score further expand to 4.03% and 2.41%.

The drug recommendation task aims to predict safe and effective drug prescriptions based on the patients' historical electronic health records (EHRs). However, existing drug recommendation models generally have two limitations. First, they neglect the inherent characteristics of multiple views existing in patients' clinical data (e.g., diagnoses and procedures), leading to fragmented and inconsistent patient representations. Second, they do not fully exploit drug label information. Most models do not explicitly establish a mapping relationship between drug labels and patients' historical visits. To address these two problems, we proposed a label-aware multi-view drug recommendation model named LAMRec. In particular, LAMRec uses a cross-attention module to fuse information from the diagnosis and procedure views, and increases the mutual information of patient multi-view representations through multi-view contrastive loss; the label-wise attention mechanism fully explores drug label information by constructing an adaptive mapping of drug-visit to generate personalized representations that are aware of the drug-related visit information. Experiments on three real world medical datasets demonstrated the superiority of LAMRec, with a relative reduction of 5.25% in DDI compared to the optimal baseline, a relative improvement of 4.20% in Jaccard similarity scores, and a relative improvement of 3.10% in F1 scores. We released the code online at: https://github.com/Tyunsen/LAMRec.

Food recommendation systems play a pivotal role in shaping dietary salubrity and fostering sustainable lifestyles by recommending recipes and foodstuffs that align with user preferences. Metadata information of a recipe, encompassing multi-modal descriptions, constituent ingredients, and health-related attributes, can furnish a more holistic perspective on the recipe's profile, thereby augmenting recommendation performance. However, existing state-of-the-art methods often overlook the inherent interdependencies between modalities, ingredients, and health factors, leaving the health information pertaining to recipe characteristics underexploited. Notably, our preliminary investigation on two datasets unveiled that the semantic divergence between health-related knowledge and collaborative filtering signals is more pronounced in comparison to other metadata information, thereby potentially impeding the efficacy of food recommendation systems. To address these limitations, we propose HealthRec, a novel multi-modal food recommendation framework with health-aware knowledge distillation. HealthRec employs a global graph representation learning module to capture high-order dependencies across diverse food-related relations, enriching the representations. Subsequently, a co-attention network is leveraged to capture local, recipe-level knowledge transfer between modality-related and ingredient-related embeddings. Additionally, we exploit external supervision signals derived from WHO recommendations, utilizing knowledge distillation during the training phase to transfer local health-aware knowledge into global collaborative embeddings. Extensive experimentation on real-world datasets demonstrates HealthRec's superiority compared to current state-of-the-art recommendation baselines, highlighting its effectiveness in modeling health-aware food recommendations.

In recent years, the integration of federated learning and deep learning technologies has become increasingly prevalent in privacy-preserved scenarios, such as smart health applications and automatic financial support. However, the inherent robustness issue in deep learning poses potential risks to federated learning systems when subjected to various attack methods. These attacks can inflict damage during the training and testing phases, perturbing models and inputs. To enhance the robustness of existing federated learning systems, we propose a novel framework called RobFL. This framework incorporates a unique feature learning module - feature center separation learning - that is specifically designed to increase the margins between different classes in the feature space, thereby augmenting the difficulty of attacks employing imperceptible perturbations on inputs. Furthermore, we design a malicious center detection method to detect malicious clients and mitigate their adverse impact. Extensive experiments substantiate the robustness of our proposed framework, RobFL, demonstrating its resilience against both evasion attacks and poisoning attacks.

Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. Specifically, we first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. The impressive performance demonstrates that UCTR can significantly reduce the dependence on manual annotation. Moreover, we also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique.

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g., knowledge base completion, question answering, and information extraction. While great efforts have been devoted to this task, most of these studies follow the assumption that large-scale labeled data is available. However, when the labeled data is insufficient for specific domains due to labor-intensive annotation work, the performance of existing algorithms will suffer an intolerable decline. In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations. Specifically, we firstly propose a novel weak supervision strategy to generate non-trivial synthetic entity-mention pairs based on mention rewriting. Since the quality of the synthetic data has a critical impact on effective model training, we further design a meta-learning mechanism to assign different weights to each synthetic entity-mention pair automatically. Through this way, we can profoundly exploit rich and precious semantic information to derive a well-trained entity linking model under the few-shot setting. The experiments on real-world datasets show that the proposed method can extensively improve the state-of-the-art few-shot entity linking model and achieve impressive performance when only a small amount of labeled data is available. Moreover, we also demonstrate the outstanding ability of the model's transferability. Our code and models will be open-sourced.

Mortality prediction is one of the essential tasks in medical data mining and is significant for inferring clinical outcomes. With a large number of medical notes collected from hospitals, there is an urgent need for developing effective models for predicting mortality based on them. In contrast to structured electronic health records, medical notes are unstructured texts written by experienced caregivers and contain more complicated information about patients, posing more challenges for modeling. Most previous studies rely on tedious hand-crafted features or generating indirect features based on some statistical models such as topic modeling, which might incur information loss for later model training. Recently, some deep models have been proposed to unify the stages of feature construction and model training. However, domain concept knowledge has been neglected, which is important to gain a better understanding of medical notes. To address the above issues, we propose novel Knowledge-aware Deep Dual Networks (K-DDN) for the text-based mortality prediction task. Specifically, a simple deep dual network is first proposed to fuse the representations of medical knowledge and raw text for prediction. Afterward, we incorporate a co-attention mechanism into the basic model, guiding the knowledge and text representation learning with the help of each other. Experimental results on two publicly real-world datasets show the proposed deep dual networks outperform state-of-the-art methods and the co-attention mechanism can further improve the performance.