The rapid advancement of deep learning has turned models into highly valuable assets due to their reliance on massive data and costly training processes. However, these models are increasingly vulnerable to leakage and theft, highlighting the critical need for robust intellectual property protection. Model watermarking has emerged as an effective solution, with black-box watermarking gaining significant attention for its practicality and flexibility. Nonetheless, existing black-box methods often fail to better balance covertness (hiding the watermark to prevent detection and forgery) and robustness (ensuring the watermark resists removal)—two essential properties for real-world copyright verification. In this paper, we propose ComMark, a novel black-box model watermarking framework that leverages frequency-domain transformations to generate compressed, covert, and attack-resistant watermark samples by filtering out high-frequency information. To further enhance watermark robustness, our method incorporates simulated attack scenarios and a similarity loss during training. Comprehensive evaluations across diverse datasets and architectures demonstrate that ComMark achieves state-of-the-art performance in both covertness and robustness.

The growing presence of mobile and IoT devices has led to massive decentralized and evolving data, driving the rise of Federated Learning (FL) to enable collaborative training without data sharing. However, traditional FL assumes static data distributions, which is unrealistic for dynamic real-world environments. To address this challenge, Federated Class-Incremental Learning (FCIL) has emerged as a promising framework that enables flexible adaptation to newly introduced classes over time. Existing FCIL methods typically integrate old knowledge preservation into local client training. However, these methods cannot avoid spatial-temporal client drift caused by data heterogeneity and often incur significant computational and communication overhead, limiting practical deployment. To address these challenges simultaneously, we propose a novel approach, Spatial-Temporal Statistics Aggregation (STSA), which provides a unified framework to aggregate feature statistics both spatially (across clients) and temporally (across stages). The aggregated feature statistics are unaffected by data heterogeneity and can be used to update the classifier in closed form at each stage. Additionally, we introduce STSA-E, a communication-efficient variant that enables the server to approximate global second-order feature statistics using first-order statistics uploaded from clients. Theoretical analysis shows that it achieves similar performance to STSA with much lower communication overhead. Extensive experiments on three widely used FCIL datasets, with varying degrees of data heterogeneity, show that our method outperforms state-of-the-art FCIL methods in terms of performance, flexibility, and both communication and computation efficiency. The code is available at https://github.com/Yuqin-G/STSA.

Graph-structured data is foundational to numerous web applications, and watermarking is crucial for protecting their intellectual property and ensuring data provenance. Existing watermarking methods primarily operate on graph structures or entangled graph representations, which compromise the transparency and robustness of watermarks due to the information coupling in representing graphs and uncontrollable discretization in transforming continuous numerical representations into graph structures. This motivates us to propose DRGW, the first graph watermarking framework that addresses these issues through disentangled representation learning. Specifically, we design an adversarially trained encoder that learns an invariant structural representation against diverse perturbations and derives a statistically independent watermark carrier, ensuring both robustness and transparency of watermarks. Meanwhile, we devise a graph-aware invertible neural network to provide a lossless channel for watermark embedding and extraction, guaranteeing high detectability and transparency of watermarks. Additionally, we develop a structure-aware editor that resolves the issue of latent modifications into discrete graph edits, ensuring robustness against structural perturbations. Experiments on diverse benchmark datasets demonstrate the superior effectiveness of DRGW.

Privacy-preserving content-based image retrieval aims to safeguard the security of outsourced private images while maintaining their searchability. However, existing schemes encounter challenges in striking a balance between security, accuracy, and efficiency, as well as difficulties in scaling to large-scale image retrieval in multi-user settings. In this paper, we propose a novel Efficient Large-Scale Encrypted Image Retrieval (ELSEIR) framework for outsourced data sharing. We first utilize a deep hashing model for image feature extraction. Building upon this, we design an irreversible random hash code generation method that incorporates permutation keys for personalized access and integrates differential privacy to further enhance data security. In our multi-user implementation, we distribute the switch keys to the cloud to standardize each key, enabling the accurate search. In addition, we have theoretically proven that our ELSEIR guarantees both outsourced data security and query user privacy. Extensive experiments on real-world datasets demonstrate that our ELSEIR yields comparable accuracy to the unprotected baseline while outperforming existing methods in terms of both retrieval accuracy and efficiency.

Signed graphs are prevalent data structures containing both positive and negative links. Recently, the fundamental network analysis task on signed graphs, namely link sign prediction, has received careful attention. Existing methods learn two target node representations independently, and the sign between these two nodes is predicted based on similarity. However, such a paradigm is node-centric that cannot distinguish node pairs with distinct contexts, thus lowering the prediction performance. Learning pair-centric representation is therefore a rewarding way to be aware of differences between pairs. There is no study yet on how to build such an appropriate representation that can effectively infer the sign between the target node pair. In this paper, we provide a new perspective to conduct link sign prediction within the paradigm of subgraph classification and propose a novel Subgraph-based link Sign Prediction (SSP) model. Technically, SSP uses importance-based sampling to extract an informative subgraph around each target node pair. For each subgraph, an innovative node labeling scheme is designed to encode its structural and signed information for representation learning. To further utilize the subgraph representation for imbalanced sign classification, SSP employs self-pruning contrastive learning to gain balanced representations. Extensive experiments on real-world datasets demonstrate that SSP consistently and significantly outperforms all the state-of-the-art baselines.

Sequential recommendations can dynamically model user interests, which has great value since users' interests may change rapidly with time. Traditional sequential recommendation methods assume that the user behaviors are rigidly ordered and sequentially dependent. However, some user behaviors have flexible orders, meaning the behaviors may occur in any order and are not sequentially dependent. Therefore, traditional methods may capture inaccurate user interests based on wrong dependencies. Motivated by this, several methods identify flexible orders by continuity or similarity. However, these methods fail to comprehensively understand the nature of flexible orders since continuity or similarity do not determine order flexibilities. Therefore, these methods may misidentify flexible orders, leading to inappropriate recommendations. To address these issues, we propose a Flexible Order aware Sequential Recommendation (FOSR) method to identify flexible orders comprehensively. We argue that orders' flexibilities are highly related to the frequencies of item pair co-occurrences. In light of this, FOSR employs a probabilistic based flexible order evaluation module to simulate item pair frequencies and infer accurate order flexibilities. The frequency labeling module extracts labels from the real item pair frequencies to guide the order flexibility measurement. Given the measured order flexibilities, we develop a flexible order aware self-attention module to model dependencies from flexible orders comprehensively and learn dynamic user interests effectively. Extensive experiments on four benchmark datasets show that our model outperforms various state-of-the-art sequential recommendation methods.

Heterogeneous side information has been widely used in recommender systems to alleviate the data sparsity problem. However, the heterogeneous side information in existing methods provides insufficient guidance for predicting user preferences as its effect is inevitably weakened during utilization. Furthermore, most existing methods cannot effectively utilize the heterogeneous side information to understand users and items. They often neglect the interrelation among various types of heterogeneous side information of a user or an item. As a result, it is difficult for existing methods to comprehensively understand users and items so that the recommender system recommends inappropriate items to users. To overcome the above drawbacks, we propose an interrelation learning-based recommendation method with iterative heterogeneous side information guidance (ILIG). ILIG includes two modules: 1) Iterative Heterogeneous Side Information Guidance Module. It uses heterogeneous side information to iteratively guide the prediction of user preferences, which effectively enhances the effect of the heterogeneous side information. 2) Interrelation Learning-based Portrait Construction Module. It captures the interrelation among various types of heterogeneous side information to comprehensively learn the representations of users and items. To demonstrate the effectiveness of ILIG, we conduct extensive experiments on Movielens-100K, Movielens-1M, and BookCrossing datasets. The experimental results show that ILIG outperforms the state-of-the-art recommender systems.

Given a network and a set of seeds related to each other, the problem of relation discovery from focusing seeds aims to discover the relations among all the seeds. Due to its wide applications, the task has been well studied in the literature. However, when facing applications where maybe not all the seeds relate to each other, methods for the task will discover many vertices unrelated to the seeds. To support such applications, a new problem called adaptive relation discovery from focusing seeds (A-RDFS) is proposed and studied in this article. Given a network and a set of seeds that may not be related to each other, discover additional vertices to reveal the relations among the seeds which are related to each other. To solve the A-RDFS problem, a relation sensitive subgraph structure called the α- relation core is proposed to find vertices related to a subset of the seeds. Thereafter, a metric called the relation quality is proposed to measure the quality of discovered relations. The metric is positively correlated with the α value of each discovered α-relation core. Hence, by maximizing the relation quality, a set of α-relation cores with large α values can be discovered, which reveals the relations among the seeds related to each other. Two algorithms are developed to optimize the relation quality. Then, using the algorithms as subroutines, the OPT-A-RDFS method is designed for the A-RDFS problem. Extensive experimental results demonstrate the performance of our methods.

Cross-modal hashing has attracted considerable attention for large-scale multimodal retrieval task. A majority of hashing methods have been proposed for cross-modal retrieval. However, these methods inadequately focus on feature learning process and cannot fully preserve higher-ranking correlation of various item pairs as well as the multi-label semantics of each item, so that the quality of binary codes may be downgraded. To tackle these problems, in this paper, we propose a novel deep cross-modal hashing method, called Adversary Guided Asymmetric Hashing (AGAH). Specifically, it employs an adversarial learning guided multi-label attention module to enhance the feature learning part which can learn discriminative feature representations and keep the cross-modal invariability. Furthermore, in order to generate hash codes which can fully preserve the multi-label semantics of all items, we propose an asymmetric hashing method which utilizes a multi-label binary code map that can equip the hash codes with multi-label semantic information. In addition, to ensure higher-ranking correlation of all similar item pairs than those of dissimilar ones, we adopt a new triplet-margin constraint and a cosine quantization technique for Hamming space similarity preservation. Extensive empirical studies show that AGAH outperforms several state-of-the-art methods for cross-modal retrieval.

Facing up to the incessant growth of complex networks, more and more researchers start turning to a multilevel computing paradigm with high scalability for clustering. By virtue of iterative coarsening level by level, the clustering results which are obtained from the coarsest network and then projected to the original network, is superior to the ones from mining the original complex network explicitly. Empirical works reflect that the local-aggregation characteristic is a key point for multilevel clustering algorithms, thus techniques like modularity, label propagation etc. are used to discover the micro-clusters for coarsening. In this paper, we propose a scalable clustering algorithm via a triangle folding processing for complex networks(SCAFT). Based on the strong cluster property of triangle, we fold each traversed triangle of the network into a superverex to realize coarsening. And each generated coarsened network by iteration is capable of reserving the cluster structures of last level network, or even the intrinsic cluster structures of original complex network, improving the computational accuracy. What's more, a streaming algorithm is embedded in our novel approach to generate a serial input sequence of vertices, reducing the heavy burdens of memory usage of system. Experimental results on real-world complex networks show that, SCAFT outperforms the state-of-the-art multilevel clustering algorithms in terms of clustering accuracy, running time, especially in memory usage.