Quasi-clique is one of the most fundamental models for characterizing cohesive subgraphs in network analysis. However, existing quasi-clique definitions and identification algorithms are designed for unsigned graphs, while many real-world networks are modeled as signed graphs with positive and negative edges representing cooperative and adversarial interactions between entities. Therefore, it remains an open problem to define a quasi-clique model tailored for signed graphs. Motivated by this, we propose the maximal balanced \( (\gamma_{1},\gamma_{2}) \) -quasi-clique (MBQC) model, which not only preserves the essence of quasi-completeness but also aligns with the foremost structural balance theory for signed graphs. Specifically, we formulate the problem of MBQCs enumeration in a given signed graph and prove its NP-hardness. To address this problem, we devise a novel branch-and-bound algorithm to efficiently enumerate all MBQCs in a signed graph, which is further optimized with several carefully-crafted techniques to prune unpromising search spaces and enhance enumeration efficiency. Extensive experiments on real-world datasets demonstrate the efficiency, scalability, and effectiveness of our MBQC model and algorithms.

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.

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.

With the accumulation of check-in data from location-based services, next Point-of-Interest (POI) recommendations are gaining increasing attention. It is well known that the spatio-temporal contextual information of user check-in behavior plays a crucial role in handling vital and inherent challenges in next POI recommendation, including capture of user dynamic preferences and the sparsity problem of check-in data. However, many studies either ignore or simply stack the context features with the embedding of POIs while relying only on POI recommendation loss to optimize the entire model, therefore failing to take full advantage of the potential information in contexts. Additionally, users’ interests are usually unstable and evolve over time, and accordingly recent studies have proposed various approaches to predict users’ next POIs by incorporating contextual information and modeling both their long- and short-term preferences, respectively. Yet many studies overemphasize the final POI recommendation performance, and the association between POI sequences and contextual information is not well embodied in data representations. In this article, we focus on the preceding problems and propose a unified attention framework for next POI recommendation by modeling users’ Long- and Short-term Preferences via Self-supervised Learning (LSPSL). Specifically, based on the self-attention network and two self-supervised optimization objectives, LSPSL first deeply exploits the intrinsic correlations between POI sequences and contextual information through pre-training, which strengthens data representations. Then, supported by pre-trained contextualized embeddings, LSPSL models and fuses users’ complex long- and short-term preferences in a unified way. Extensive experiments on real-world datasets demonstrate the superiority of our model compared with other state-of-the-art approaches.

Multiple clusterings can explore the same set of data from different perspectives by discovering different and meaningful clusterings. However, most, if not all, of the existing approaches overwhelmingly focus on the diversity between clustering subspaces, and pay much less attention on the salience of the subspaces. As a consequence, the quality of the produced clusterings is an understudied aspect of the problem. Furthermore, existing methods cannot explain the unique internal subspace structure of each clustering, and cannot incorporate multi-facet knowledge to generate different clusterings. In this paper, we propose a solution namediMClusts(interpretableMultipleClusteringsby diversified attention). iMClusts makes use of the expressive representational power of deep autoencoders and multi-head attention to generate multiple salient embedding matrices, and multiple clusterings therein. In addition, it leverages multi-facet knowledge and enhances the diversity between heads to boost the quality and diversity of multiple clusterings. Experimental results on benchmark datasets show that iMClusts can generate multiple clusterings with quality, interpretability, and diversity.

Abstract Nowadays recording and sharing personal lives using mobile devices on the Internet is becoming increasingly popular, and successive POI recommendation is gaining growing attention from academia and industry. In mobile scenarios, multiple influencing factors including the diversity of user preferences, the changeability of user behavior and the dynamic of spatiotemporal context bring great challenges to the POI recommender system. In order to accurately capture both the stable and the contextual preferences of mobile users in dynamic contexts, we propose a fusion framework JANICP (Joint Attention Networks with Inherent and Contextual Preferences) for successive POI recommendation by jointly training an offline/nearline user inherent interest perception model and an online user contextual interest prediction model. The offline model is trained based on the global historical behavior data to achieve stable interest representation, while the online model is trained based on the instantly selected context-sensitive data to achieve dynamic interest perception. An attention aggregation and matching module is used to fully connect the two kinds of preference representations and generate the final POI recommendation. Extensive experiments were conducted on three real datasets and experimental results show that the proposed JANICP outperforms existing state-of-the-art methods.

Partial multi-label learning (PML) aims at learning a robust multi-label classifier by training on ambiguous data, where each sample is associated with a set of candidate labels, among which only a subset are valid labels. A basic premise of existing PML solutions is to obtain enough partial multi-label samples for inducing the classification model. However, when dealing with new tasks, we may only have a few PML samples for those tasks. Furthermore, existing few-shot learning approaches assume the support (training) samples are precisely labeled; as such, irrelevant labels in the candidate label set may seriously mislead the meta-learner and thus result in a compromised performance. How to achieve PML with limited few-shot support samples is an important and practical problem, but not yet well studied. In this paper, we propose an approach called FsPML (Few-shot PML) to tackle this problem. Specifically, FsPML first performs adaptive distance metric learning via an embedding network using both sample features and label semantics in the embedding space. Next it rectifies the positive and negative prototypes of each new label of the target task in the embedding space. An unseen example can then be classified via its distances to the positive and to the negative prototypes. Experimental results on widely-used multi-label datasets (MS COCO and NUS-WIDE) demonstrate that our FsPML outperforms competitive baselines across different settings, and it can quickly generalize to new tasks with fewer training samples.

Today, Pareto-optimal objects finding has been applied in various fields, such as group decision making and opinion collection. Many of the existing solutions to this problem require explicit attributes for objects. However, these attributes cannot be obtained sometimes. To address this issue, we propose an algorithm, which uses preference relations given by crowdsourcing, to find Pareto-optimal objects with shorter latency and lower monetary costs. It employs two multi-pairwise-comparison question models: BEST-form and BETTER-form questions. Multiple BEST (or BETTER) questions can be sent to crowds concurrently. Extensive experimental results show that the number of questions reduces greatly. In addition, the numerical results show that the latency is significantly shortened at a reasonable monetary cost, compared with the existing methods.