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.

With growing concerns over user privacy, federated recommendation (FedRec) has emerged as a mainstream solution for personalized recommendation services. FedRec trains user-private parameters on local clients while collaboratively updating global parameters on a centralized server. However, despite advances in optimizing these local and global parameters, existing methods overlook two key challenges: tradeoff training and distribution discrepancy . Tradeoff training balances timely local updates with diverse global parameters, limiting the model’s learning ability. Distribution discrepancy arises from the divergence between locally trained global parameters and those aggregated by the server, corrupting inference performance. To fill in the gap, we propose FedSC , a principled federated recommendation framework that boosts FedRec’s training and inference processes with minimal yet nontrivial efforts. During training, FedSC employs a stochastic aggregation strategy where all users participate in every round, while only a random subset is selected for aggregation, preserving the diversity of global parameters and ensuring timely local updates. During inference, FedSC makes recommendations with a consistency inference mechanism that uses the most recent locally trained global parameters of each user to improve the model’s understanding of user preferences. Extensive experiments on multiple benchmark datasets demonstrate the superiority of FedSC, achieving up to a 20% improvement in most evaluation scenarios.

Graph Neural Networks (GNNs) hold promise in various application domains, but their limited explainability hinders widespread adoption, impacting customer satisfaction and loyalty. This issue intensifies when addressing diverse explanation needs of different user groups. Current GNN explanation models focus on a single objective, neglecting varied and potential conflicting user requirements, resulting in suboptimal outcomes. Moreover, existing models prioritize explanation objectives during multi-objective explanations, disrupting the intrinsic hierarchical structures and distant relationships within the graphs, further diminishing their effectiveness. To tackle these challenges, this paper introduces a novel multi-objective explanatory framework with hierarchical structure attribution for GNNs, termed HM-Explainer. This framework constructs a multi-objective explanation generation module based on Pareto theory to balance different and potentially conflicting explanatory objectives. Additionally, to embed hierarchical information into explanations, HM-Explainer designs node-level and cluster-level attribution modules to analyze the impact of input data on GNN decisions hierarchically. Furthermore, a self-attention mechanism is integrated into the node-level attribution module to account for the influence of distant neighbors. Ultimately, the efficacy of HM-Explainer is validated across multiple datasets for different GNN models through experimentation.

Federated Recommendation (FedRec) systems are recognized as privacy-preserving solutions for collaboratively training recommender models without sharing users’ private data. However, recent studies have revealed that FedRec systems are vulnerable to interaction-level membership inference attacks. In such attacks, a semi-honest server can employ crafted methods to infer users’ interacted items. In this article, we identify that user preference information is predominantly stored in the user-uploaded parameters rather than in the local parameters after local training. Leveraging this insight, we expose a new interaction vulnerability and introduce the PubPara attack. Our experiments show that PubPara improves the inference performance by at least 40% over existing attacks, while requiring minimal inference time and remaining robust against current defense methods. To safeguard user privacy without compromising recommender performance, we propose MultiVerse, a novel countermeasure. MultiVerse utilizes untrained items outside the user’s local training data to obfuscate the server’s inference of interacted items. It includes a four-step strategy (training, optimization, refinement, and denoising) to achieve robust defense. Extensive experiments on three representative FedRec models (F-NCF, F-LightGCN, and FedRAP) across three real-world datasets validate that MultiVerse significantly degrades the attack’s inference performance to near the level of random guess while maintaining lossless recommender performance.

Crowdsourcing is a promising human-in-the-loop paradigm for processing computer hard tasks by harnessing crowd intelligence. However, canonical crowdsourcing systems mostly need to aggregate/transmit worker data and may lead to privacy-leakage. To tackle this problem, we propose a novel approach, called FedCS (Federated CrowdSourcing), to achieve privacy protection while ensuring quality. FedCS aggregates model parameters from clients to build a shared server model while keeping the training data locally on worker devices to protect data privacy. To mitigate the staleness of stragglers and boost efficiency, we introduce a semi-asynchronous federated crowdsourcing mechanism, where the parameter server performs global aggregation periodically. Moreover, due to the different frequencies of workers participating in asynchronous update, FedCS uses a staleness-aware grouping and weighted aggregation heuristic to balance the training process. To speed up the convergence rate and improve the training accuracy, FedCS deploys adaptive learning step size for worker devices by their participation frequency. We further present a task assignment algorithm to help workers choose worthy and suitable tasks for annotations and to save the budget. Extensive experiments on benchmark datasets and a real-world crowdsourcing project show that FedCS can complete secure crowdsourcing projects with high quality and low budget.

As considerable amounts of POI check-in data have been accumulated, successive point-of-interest (POI) recommendation is increasingly popular. Existing successive POI recommendation methods only predict where user will go next, ignoring when this behavior will occur. In this work, we focus on predicting POIs that will be visited by users in the next 24 hours. As check-in data is very sparse, it is challenging to accurately capture user preferences in temporal patterns. To this end, we propose a category-aware deep model CatDM that incorporates POI category and geographical influence to reduce search space to overcome data sparsity. We design two deep encoders based on LSTM to model the time series data. The first encoder captures user preferences in POI categories, whereas the second exploits user preferences in POIs. Considering clock influence in the second encoder, we divide each user’s check-in history into several different time windows and develop a personalized attention mechanism for each window to facilitate CatDM to exploit temporal patterns. Moreover, to sort the candidate set, we consider four specific dependencies: user-POI, user-category, POI-time and POI-user current preferences. Extensive experiments are conducted on two large real datasets. The experimental results demonstrate that our CatDM outperforms the state-of-the-art models for successive POI recommendation on sparse check-in data.

The response time for multi-tenant queries is one of the most important indicators in the service level agreements (SLA). The service provider tries to optimize query scheduling strategy to finish queries of different tenants before the deadline to avoid penalty due to jeopardizing the SLA. With continuous expansion of tenants scale, peer-to-peer (P2P) structure becomes more and more popular in organizing and managing multi-tenant data. In this paper we propose an acentric scheduling approach for SLA-based multi-tenant queries according to the distribution characteristics of multi-tenant data. Our scheduling approach deploys multiple scheduling engines on the computing nodes in the cloud, where the computing node of each engine schedules its assigned queries, estimates whether these queries could be finished before the deadline, and migrates the queries that might jeopardize the SLA to another engine which can respond to it before the deadline. Since the high efficiency of the scheduling process is critical, we improve the balanced binary tree and use it to organize queries on each computing node. Using this structure, the online time complexity of the scheduling strategy is [Formula: see text]. Our extensive experiments demonstrate that our scheduling strategy is sufficient to meet the high scheduling efficiency requirement, while the penalty cost can be reduced up to [Formula: see text] compared with the benchmarking solution and with a better scalability.

The rapid growth of data makes it possible for us to study human behavior patterns. Knowing the patterns of human behavior is of great use to help us detect the unusual fraud human behavior. Existing fraud detection methods can be divided into two categories: pattern based and outlier detection based methods. However, because of the sparsity and complex granularity of big data, these methods have high false positive in fraud detection. In this paper, we propose an effective hybrid fraud detection method. We propose SSIsomap which improves isomap to cluster behaviors into behavior classes and propose SimLOF which improves LOF to conduct outlier detection, then we use Dempster-Shafer evidence Theory for combining behavior pattern evidence and outlier evidence, which yields a degree of belief of fraud to the new coming claim. The experiment result shows our method has significantly higher accuracy than exsiting methods in medical insurance fraud detection.

With the tremendous growth of the Web, it has become a huge challenge for the single-process crawlers to locate the resources that are precise and relevant to some topics in an appropriate amount of time, so it is increasingly important to use the parallel crawler. However, due to the parallelism of crawlers, one headache problem we have to face is how to distribute the URLs to crawlers to make the parallel system work coordinately and thereby make sure that the Web pages fetched are of high quality. In this paper, a novel URL assignment model for the parallel crawler is described, which is based on multi-objective decision making method and considers multiple factors synthetically such as load balance, overlap and so on. Extensive experiments test and validate our techniques.

SaaS is a novel software model that data and applications of service are outsourced to service provider. Although SaaS model offers many benefits for small and medium enterprises, data privacy issue is the most challenge for the development of SaaS. In this paper we propose a new hybrid fragmentation approach which is different from traditional data encryption to protect data. We define three kinds of privacy constraints to support finger-grained privacy customization. We also give a heuristic hybrid fragmentation algorithm which considers query efficiency to produce a hybrid fragmentation. We make some experiments to analyze our approach in the paper.

As an important supporting technology of Market Intelligence (MI), Web data integration is facing new challenges, such as the integrity of data acquisition, the quality of data extraction and data consolidation. To solve such problems, we propose an MI-oriented web data integration system (MI-WDIS), which achieves excellent performances in integrating Surface Web and Deep Web data with much less manual work. Based on MI-WDIS, we have developed a platform for intelligent analysis of job data. The platform collects tens of thousands of job data daily and provides personalized services for job seekers through diversified channels. Besides, it provides other advanced services, including intelligence analysis, automatic monitoring and alerting, for various organizations, such as enterprises, training institutions and recruitment agencies.