Abstract Social network alignment (SNA) aims to match corresponding users across different platforms, playing a critical role in cross-platform behavior analysis, personalized recommendations, security, and privacy protection. Traditional methods based on attribute and structural features face significant challenges due to the sparsity, heterogeneity, and dynamic nature of social networks, resulting in limited accuracy and efficiency. Recent advances in graph representation learning (GRL) provide promising solutions to these issues by leveraging deep learning to extract network features, effectively addressing sparsity, integrating heterogeneous data, and adapting to network dynamics. This paper presents a comprehensive survey of SNA methods based on GRL. We first introduce key definitions and outline a framework for SNA using GRL. Next, we systematically review state-of-the-art advancements in both static and dynamic networks, considering homogeneous and heterogeneous settings, including emerging approaches integrating large language models (LLMs). We further conduct an in-depth comparative analysis, highlighting the effectiveness of different GRL-based methods, with a particular emphasis on LLM-enhanced techniques. Finally, we discuss open challenges and outline potential future research directions in this rapidly evolving field.

The growing volume of performance-critical parameters in distributed storage systems, coupled with diverse and dynamic workload patterns, has significantly increased the complexity of system configuration. These trends have expanded the parameter space while tightening the time window for tuning convergence, making it challenging to maintain high system performance. Existing tuning strategies often struggle to balance thorough parameter exploration with real-time responsiveness, limiting their effectiveness under fast-evolving workloads and heterogeneous deployment environments. To address these challenges, we propose KGQW, the first framework that formulates automated parameter tuning as a knowledge graph query workflow. KGQW models workload features and system parameters as graph vertices, with performance metrics represented as edges, and constructs an initial knowledge graph through lightweight performance tests. Guided by performance prediction and Bayesian-driven exploration, KGQW progressively expands the graph, prunes insensitive parameters, and refines performance relationships to build an informative and reusable knowledge graph that supports rapid configuration retrieval via graph querying. Moreover, KGQW enables efficient knowledge transfer across clusters, substantially reducing the construction cost for new clusters. Experiments on real-world applications and storage clusters demonstrate that KGQW achieves second-level tuning latency, while maintaining or surpassing the performance of state-of-the-art methods. These results highlight the promise of knowledge-driven tuning in meeting the scalability and adaptability demands of modern distributed storage systems.

Serverless computing relies on keeping functions alive or pre-warming them before invocation to mitigate the cold start problem, stemming from the overhead of initializing function startup environments. However, under constrained cloud resources, accurately predicting the invocation patterns of sparse functions remains challenging. This limits the formulation of effective pre-warm and keep-alive strategies, leading to frequent cold starts and degraded user satisfaction. To address these challenges, we proposeSPFaaS, a hybrid framework based on sparse function prediction. To enhance the learnability of sparse function invocation data,SPFaaStakes into account the characteristics of cloud service workloads along with the features of pre-warm and keep-alive strategies, transforming function invocation records into probabilistic data. It captures the underlying periodicity and temporal dependencies in the data through multiple rounds of sampling and the combined use of Gated Recurrent Units and Temporal Convolutional Networks for accurate prediction. Based on the final prediction outcome and real-time system states,SPFaaSdetermines adaptive pre-warm and keep-alive strategies for each function. Experiments conducted on two real-world serverless clusters demonstrate thatSPFaaSoutperforms state-of-the-art methods in reducing cold starts and improving user satisfaction.

Object cloud storage systems are deployed with diverse applications that have varying latency service level objectives (SLOs), posting challenges for supporting quality of service with limited storage resources. Existing methods provide prediction-based recommendations for dispatching requests from applications to storage devices, but the prediction accuracy can be affected by complex system topology. To address this issue, Graph3PO is designed to combine storage device queue information with system topological information for forming a temporal graph, which can accurately predict device queue states. Additionally, Graph3PO contains the urgency degree model and cost model for measuring SLO violation risks and penalties of scheduling requests on storage device queues. When the urgency degree of a request exceeds a threshold, Graph3PO determines whether to schedule it in the queue or initiate a hedge request to another storage device. Experimental results show that Graph3PO outperforms its competitors, with SLO violation rates 2.8 to 201.1 times lower.

A direction-aware augmented spatial keyword top-$k$query (DAT$k\text{Q}$) returns the top-$k$objects based on a ranking function that considers spatial distance, textual similarity, query numeric attributes, and query direction. When a user initiates a DAT$k\text{Q}$, some user-desired objects (missing objects) may not appear in the query result set, and then the user wonders why they do not appear, which is called the why-not question. This paper focuses on answering why-not questions on DAT$k$Qs. We first discuss how to obtain the refined query direction by analyzing the position relationship between missing objects and original query direction in Polar coordinates. Then a DAPC index structure is designed, which can cut down irrelevant search space based on not only conventional distance pruning, keyword pruning, and attribute pruning but also query direction pruning. Particularly, by comparing the position relationship between the query direction and the sector (sector ring) region segmented by the DAPC-based method, the search space that does not meet the query direction is pruned. In addition, we discuss the applicability of our scheme for handling why-not questions on regional spatial keyword queries (SKQ), ordinary direction-aware top-$k$SKQ queries and complex scoring SKQ queries. Finally, a series of experiments are conducted on two real datasets to show the efficiency of our DAPC-based method.

Structured publish/subscribe (pub/sub) is a promising technique adopted on kinds of vehicle applications of Internet of industrial vehicles (IoIV), which uses Boolean expressions to capture the items with thousands of different attributes, values and spatial locations, and then processes and analyzes the vast amounts of data collected to obtain users' interests. However, existing pub/sub work with Boolean expressions either ignores spatial requirement or focuses on Euclidean space. This paper aims to fill this gap by addressing the issue of fog-based spatial-textual pub/sub problem with Boolean expressions in IoIV. A novel hybrid index called RnetBE is proposed, which exquisitely organizes traffic network structure, Boolean expressions, and spatial information of subscriptions. And RnetBE can prune huge numbers of unqualified subscriptions based on both spatial constraint and Boolean expressions, thus achieving high efficiency in indexing and matching. Moreover, range-tree deletion and orderly group processing optimization techniques are proposed to save storage space and further improve the subscription pruning efficiency. Simulation results show that RnetBE and the proposed algorithm are efficient in terms of memory consumption and matching time.