What enables large language models (LLMs) to effectively model user preferences in sequential recommendation?Our investigation reveals that existing preference-alignment approaches largely rely on binary pairwise comparisons, overlooking two critical factors: preference intensity-the structured strength of affinity or aversion -and temporal context-the extent to which recent interactions better reflect a user's current intent.Through controlled experiments, we show that leveraging comprehensive feedback with structured preference signals substantially improves recommendation performance, indicating that binary modeling discards essential information.Motivated by these findings, we propose RecPO, a unified preference optimization framework that maps both explicit and implicit feedback into a common preference signal and constructs adaptive reward margins that jointly account for preference intensity and interaction recency.Experiments across five datasets show that RecPO consistently outperforms state-of-the-art baselines while exhibiting behavioral patterns aligned with human decision-making, including favoring immediate satisfaction, maintaining preference coherence, and avoiding dispreferred items.Our results highlight that preference intensity and temporal context are fundamental ingredients for effective LLM-based recommendation.Code:

Self-supervised learning (SSL) has gained increasing attention in the graph learning community, owing to its capability of enabling powerful models pre-trained on large unlabeled graphs for general purposes, facilitating quick adaptation to specific domains. Though promising, existing graph SSL frameworks often struggle to capture both high-level abstract features and fine-grained features simultaneously, leading to sub-optimal generalization abilities across different downstream tasks. To bridge this gap, we present Multi-granularity Graph Semantic Ensemble via Knowledge Distillation, namely MGSE, a plug-and-play graph knowledge distillation framework that can be applied to any existing graph SSL framework to enhance its performance by incorporating the concept of multi-granularity. Specifically, MGSE captures multi-granular knowledge by employing multiple student models to learn from a single teacher model, conditioned by probability distributions with different granularities. We apply it to six state-of-the-art graph SSL frameworks and evaluate their performances over multiple graph datasets across different domains, the experimental results show that MGSE can consistently boost the performance of these existing graph SSL frameworks with up to 9.2% improvement.

Among the existing self-supervised learning (SSL) methods for graphs, graph contrastive learning (GCL) frameworks usually automatically generate supervision by transforming the same graph into different views through graph augmentation operations. The computation-efficient augmentation techniques enable the prevalent usage of GCL to alleviate the supervision shortage issue. Despite the remarkable performance of those GCL methods, the InfoMax principle used to guide the optimization of GCL has been proven to be insufficient to avoid redundant information without losing important features. In light of this, we introduce the Graph Contrastive Learning with Cross-View Reconstruction (GCVR), aiming to learn robust and sufficient representation from graph data. Specifically, GCVR introduces a cross-view reconstruction mechanism based on conventional graph contrastive learning to elicit those essential features from raw graphs. Besides, we introduce an extra adversarial view perturbed from the original view in the contrastive loss to pursue the intactness of the graph semantics and strengthen the representation robustness. We empirically demonstrate that our proposed model outperforms the state-of-the-art baselines on graph classification tasks over multiple benchmark datasets.

Drug-drug interaction (DDI), as a possible result of drug combination treatment, could lead to adverse physiological reactions and increasing mortality rates of patients. Therefore, predicting potential DDI has always been an important and challenging issue in medical health applications. Owing to the extensive pharmacological research, we can get access to various drug-related features for DDI predictions; however, most of the existing works on DDI prediction do not incorporate comprehensive features to analyze the DDI patterns. Despite the high performance that the existing works have achieved, the incomplete and noisy information generated from limited sources usually leads to sub-optimal performance and poor generalization ability on the unknown DDI pairs. In this work, we propose a holistic framework, namely Multi-modality Feature Optimal Fusion for Drug-Drug Interaction Prediction (MOF-DDI), that incorporates the features from multiple data sources to resolve the DDI predictions. Specifically, the proposed model jointly considers DDIs literature descriptions, biomedical knowledge graphs, and drug molecular structures to make the prediction. To overcome the issue induced by directly aggregating features in different modalities, we bring a new insight by mapping the representations learned from different sources to a unified hidden space before the combination. The empirical results show that MOF-DDI achieves a large performance gain on different DDI datasets compared with multiple state-of-the-art baselines, especially under the inductive setting.

Graph Neural Networks (GNNs) are powerful models for non-Euclidean data, but their training is often accentuated by massive unnecessary computation: on the one hand, training on non-Euclidean data has relatively high computational cost due to its irregular density properties; on the other hand, the class imbalance property often associated with non-Euclidean data cannot be alleviated by the massiveness of the data, thus hindering the generalisation of the models. To address the above issues, theoretically, we start with a hypothesis about the effectiveness of using a subset of training data for GNNs, which is guaranteed by the gradient distance between the subset and the full set. Empirically, we also observe that a subset of the data can provide informative gradients for model optimization and which changes over time dynamically. We name this phenomenon dynamic data sparsity. Additionally, we find that pruned sparse contrastive models may miss valuable information, leading to a large loss value on the informative subset. Motivated by the above findings, we develop a unified data model dynamic sparsity framework called Data Decantation (DataDec) to address the above challenges. The key idea of DataDec is to identify the informative subset dynamically during the training process by applying sparse graph contrastive learning. The effectiveness of DataDec is comprehensively evaluated on graph benchmark datasets and we also verify its generalizability on image data.

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most existing GSL works apply a joint learning framework where the estimated adjacency matrix and GNN parameters are optimized for downstream tasks. However, as GSL is essentially a link prediction task, whose goal may largely differ from the goal of the downstream task. The inconsistency of these two goals limits the GSL methods to learn the potential optimal graph structure. Moreover, the joint learning framework suffers from scalability issues in terms of time and space during the process of estimation and optimization of the adjacency matrix. To mitigate these issues, we propose a graph structure refinement (GSR) framework with a pretrain-finetune pipeline. Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks. Then, the graph structure is refined by adding and removing edges according to the edge probabilities estimated by the pre-trained model. Finally, the fine-tuning GNN is initialized by the pre-trained model and optimized toward downstream tasks. With the refined graph structure remaining static in the fine-tuning space, GSR avoids estimating and optimizing graph structure in the fine-tuning phase which enjoys great scalability and efficiency. Moreover, the fine-tuning GNN is boosted by both migrating knowledge and refining graphs. Extensive experiments are conducted to evaluate the effectiveness (best performance on six benchmark datasets), efficiency, and scalability (13.8 times faster using 32.8% GPU memory compared to the best GSL baseline on Cora) of the proposed model.

Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. In this paper, we propose a novel model RxNet, which builds 1) a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various patients, 2) an RxLSTM network to explore the dynamic Rx-refill behavior and medical condition variation of patients, and 3) a dosing-adaptive network to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a one-year state-wide PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse.

Driven by the exponential increase of software and the advent of the pull-based development system Git, a large amount of open-source software has emerged on various social coding platforms. GitHub, as the largest platform, not only attracts developers and researchers to contribute legitimate software and research-related source code but has also become a popular platform for an increasing number of cybercriminals to perform continuous cyberattacks. Hence, some tools have been developed to learn representations of repositories on GitHub for various related applications (e.g., malicious repository detection) recently. However, most of them merely focus on code content while ignoring the rich relational data among repositories. In addition, they usually require a mass of resources to obtain sufficient labeled data for model training while ignoring the usefully handy unlabeled data. To this end, we propose a novel model Rep2Vec which integrates the code content, the structural relations, and the unlabeled data to learn the repository representations. First, to comprehensively model the repository data, we build a repository heterogeneous graph (Rep-HG) which is encoded by a graph neural network. Afterwards, to fully exploit unlabeled data in Rep-HG, we introduce adversarial attacks to generate more challenging contrastive pairs for the contrastive learning module to train the encoder in node view and meta-path view simultaneously. To alleviate the workload of the encoder against attacks, we further design a dual-stream contrastive learning module that integrates contrastive learning on adversarial graph and original graph together. Finally, the pre-trained encoder is fine-tuned to the downstream task, and further enhanced by a knowledge distillation module. Extensive experiments on the collected dataset from GitHub demonstrate the effectiveness of Rep2Vec in comparison with state-of-the-art methods for multiple repository tasks.

Opioids (e.g., oxycodone and morphine) are highly addictive prescription (aka Rx) drugs which can be easily overprescribed and lead to opioid overdose. Recently, the opioid epidemic is increasingly serious across the US as its related deaths have risen at alarming rates. To combat the deadly opioid epidemic, a state-run prescription drug monitoring program (PDMP) has been established to alleviate the drug over-prescribing problem in the US. Although PDMP provides a detailed prescription history related to opioids, it is still not enough to prevent opioid overdose because it cannot predict over-prescribing risk. In addition, existing machine learning-based methods mainly focus on drug doses while ignoring other prescribing patterns behind patients' historical records, thus resulting in suboptimal performance. To this end, we propose a novel model DDHGNN - Disentangled Dynamic Heterogeneous Graph Neural Network, for over-prescribing prediction. Specifically, we abstract the PDMP data into a dynamic heterogeneous graph which comprehensively depicts the prescribing and dispensing (P&D) relationships. Then, we design a dynamic heterogeneous graph neural network to learn patients' representations. Furthermore, we devise an adversarial disentangler to learn a disentangled representation which is particularly related to the prescribing patterns. Extensive experiments on a 1-year anonymous PDMP data demonstrate that DDHGNN outperforms state-of-the-art methods, revealing its promising future in preventing opioid overdose.

Graph contrastive learning (GCL), leveraging graph augmentations to convert graphs into different views and further train graph neural networks (GNNs), has achieved considerable success on graph benchmark datasets. Yet, there are still some gaps in directly applying existing GCL methods to real-world data. First, handcrafted graph augmentations require trials and errors, but still can not yield consistent performance on multiple tasks. Second, most real-world graph data present class-imbalanced distribution but existing GCL methods are not immune to data imbalance. Therefore, this work proposes to explicitly tackle these challenges, via a principled framework called \textit{\textbf{C}o-\textbf{M}odality \textbf{G}raph \textbf{C}ontrastive \textbf{L}earning} (\textbf{CM-GCL}) to automatically generate contrastive pairs and further learn balanced representation over unlabeled data. Specifically, we design inter-modality GCL to automatically generate contrastive pairs (e.g., node-text) based on rich node content. Inspired by the fact that minority samples can be ``forgotten'' by pruning deep neural networks, we naturally extend network pruning to our GCL framework for mining minority nodes. Based on this, we co-train two pruned encoders (e.g., GNN and text encoder) in different modalities by pushing the corresponding node-text pairs together and the irrelevant node-text pairs away. Meanwhile, we propose intra-modality GCL by co-training non-pruned GNN and pruned GNN, to ensure node embeddings with similar attribute features stay closed. Last, we fine-tune the GNN encoder on downstream class-imbalanced node classification tasks. Extensive experiments demonstrate that our model significantly outperforms state-of-the-art baseline models and learns more balanced representations on real-world graphs. Our source code is available at https://github.com/graphprojects/CM-GCL.

Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the United States has been developed to reduce Overprescribing. However, PDMP has limited capability in detecting patients' potential overprescribing behaviors, impairing its effectiveness in preventing drug abuse and overdose in patients. Despite a few machine-learning-based methods that have been proposed for detecting overprescribing, they usually ignore the patient prescribing behavior and their performances are not satisfying. In light of this, we propose a novel model RxNet for overprescribing detection in PDMP. RxNet builds a dynamic heterogeneous graph to model Rx refills that are essentially prescribing and dispensing (P&D) relationships among various Rx entries (e.g., patients) whose representations are encoded by graph neural network. In addition, to explore the dynamic Rx-refill behavior and medical condition variation of patients, an RxLSTM network is designed to update representations of patients. Based on the output of RxLSTM, a dosing-adaptive network is leveraged to extract and recalibrate dosing patterns and obtain the refined patient representations which are finally utilized for overprescribing detection. The extensive experimental results on a 1-year Ohio PDMP data demonstrate that RxNet consistently outperforms state-of-the-art methods in predicting patients at high risk of opioid overdose and drug abuse, with an average of 5.7% and 7.3% improvement on F1 score respectively.