• Multi-view contrastive learning overcomes data sparsity • Temporal regularisation stabilises user preferences across sequences • Popularity-aware defence mitigates promotion and camouflage attacks • Achieves stable performance within ± 1% even with 80% malicious clients • Maintain 90% of original user accuracy even when cold-start Federated sequential recommendation (FedSeqRec) allows many user devices to train a shared recommender without sending raw interaction histories to a central server, which is important for privacy. However, existing FedSeqRec methods still suffer from two key limitations: (1) most users have very short or sparse histories, especially when they only show interest in a few items for a short period, leaving the model with too little data to understand their preferences; and (2) in a sequential setting it is normal for interests to change suddenly, but the model may misinterpret these abrupt changes as anomalies. In this paper, we propose FORTRESS , a F ederated c O ntrastive R obus T RE commender for S equential S ystems, designed to address these limitations. To tackle the first issue, FORTRESS generates augmented versions of local interaction sequences on each client, so that the model can observe more plausible behaviour patterns and learn user preferences more reliably even when histories are short or sparse. This extra flexibility also gives adversaries more room to manipulate the training signal, so we complement it with a popularity-aware server-side regularizer that discourages rare or suspicious items from drifting into the same embedding clusters as genuinely popular items. To tackle the second issue, we introduce a temporal regularization term that discourages abrupt changes in user representations across adjacent subsequences, allowing the model to adapt to short-term interest shifts while still preserving stable long-term tastes. Experiments on three real-world datasets show that FORTRESS improves recommendation accuracy for sparse and cold-start users and substantially reduces the success of strong model poisoning attacks compared with competitive centralized and federated baselines.

Abstract The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize centralized recommendations, relying on servers to store client data and dispatch advice to users. However, these conventional approaches raise significant concerns regarding data privacy and often result in computational inefficiencies. E-health recommendation services, distinct from other recommendation domains, demand not only precise and swift analyses but also a stringent adherence to privacy safeguards, given the users’ reluctance to disclose their identities or health information. In response to these challenges, we explore a new paradigm called on-device recommendation tailored to E-health diagnostics, where diagnostic support (such as biomedical image diagnostics), is computed at the client level. We leverage the advances of federated learning to deploy deep learning models capable of delivering expert-level diagnostic suggestions on clients. However, existing federated learning frameworks often deploy a singular model across all edge devices, overlooking their heterogeneous computational capabilities. In this work, we propose an adaptive federated learning framework utilizing BlockNets, a modular design rooted in the layers of deep neural networks, for diagnostic recommendation across heterogeneous devices. Our framework offers the flexibility for users to adjust local model configurations according to their device’s computational power. To further handle the capacity skewness of edge devices, we develop a data-free knowledge distillation mechanism to ensure synchronized parameters of local models with the global model, enhancing the overall accuracy. Through comprehensive experiments across five real-world datasets, against six baseline models, within six experimental setups, and various data distribution scenarios, our architecture demonstrates unparalleled performance and robustness in terms of both accuracy and efficiency.

Modern recommender systems derive predictions from an interaction graph that links users and items. To this end, many of today's state-of-the-art systems use graph neural networks (GNNs) to learn effective representations of these graphs under the assumption of homophily, i.e., the idea that similar users will sit close to each other in the graph. However, recent studies have revealed that real-world recommendation graphs are often heterophilous, i.e., dissimilar users will also often sit close to each other. One of the reasons for this heterophilia is shilling attacks that obscure the inherent characteristics of the graph and make the derived recommendations less accurate as a consequence. Hence, to cope with low homophily in recommender systems, we propose a recommendation model called PGT4Rec that is based on a Partitioned Graph Transformer. The model integrates label information into the learning process, which allows discriminative neighbourhoods of users to be generated. As such, the framework can both detect shilling attacks and predict user ratings for items. Extensive experiments on real and synthetic datasets show PGT4Rec as not only providing superior performance in these two tasks but also significant robustness to a range of adversarial conditions.

The propagation of rumours on social media poses an important threat to societies, so that various techniques for graph-based rumour detection have been proposed recently. Existing works, however, are based on homophilic graphs: entities that are connected to each other often have the same label. However, recent studies found that heterophily is more common in real-world social networks, i.e., entities with different labels are also often linked to each other due to ‘innocent’ retweets or camouflage behaviours by malicious users. Especially, the heterophily problem is even more challenging in multi-modal social graphs, in which neighbouring entities might differ in terms of both labels and modalities. To cope with multi-modal homophily in graph-based rumour detection, we propose a Portable Graph Transformer-based Rumour Detection model (PHAROS) with novel multi-modal homophily measures. It integrates label information in the learning process, which enables us to generate discriminative neighbourhoods of entities. Our model can handle multiple modalities (a natural characteristic of social graphs) and is portable to be combined with existing graph-based models. Extensive experiments on real and synthetic data show the superiority, efficiency, robustness, and portability of PHAROS and its heterophily resilience.

Maximal frequent subgraph mining (MFSM) is the task of mining only maximal frequent subgraphs, i.e., subgraphs that are not a part of other frequent subgraphs. Although many intelligent systems require MFSM, MFSM is challenging compared to frequent subgraph mining (FSM), as maximal frequent subgraphs lie in the middle of graph lattice, and FSM algorithms must explore an exponential space and an NP-hard subroutine of frequency counting. Different from prior research, which primarily focused on optimal solutions, we introduce pmMine, a progressive graph neural framework designed for MFSM in a single large graph to attain an approximate solution. The framework combines isomorphic graph embedding, non-parametric partitioning, and an efficiently top-down pattern searching strategy. The critical insight that makes pmMine work is to define the concepts of rooted subgraph and isomorphic graph embedding, in which the costly isomorphism subroutine can be efficiently performed using similarity estimation in embedding space. In addition, pmMine returns the patterns identified during the mining process in a progressive manner. We validate the efficiency and effectiveness of our technique through extensive experiments on a variety of datasets spanning various domains.

The goal of subgraph matching is to determine the presence of a particular query pattern within a large collection of data graphs. Despite being a hard problem, subgraph matching is essential in various disciplines, including bioinformatics, text matching, and graph retrieval. Although traditional approaches could provide exact solutions, their computations are known to be NP-complete, leading to an overwhelmingly querying latency. While recent neural-based approaches have been shown to improve the response time, the oversimplified assumption of the first-order network may neglect the generalisability of fully capturing patterns in varying sizes, causing the performance to drop significantly in datasets in various domains. To overcome these limitations, this paper proposes xDualSM, a dual matching neural network model with interleaved diffusion attention. Specifically, we first embed the structural information of graphs into different adjacency matrices, which explicitly capture the intra-graph and cross-graph structures between the query pattern and the target graph. Then, we introduce a dual matching network with interleaved diffusion attention to carefully capture intra-graph and cross-graph information while reducing computational complexity. Empirically, our proposed framework not only boosted the speed of subgraph matching more than 10x compared to the fastest baseline but also achieved significant improvements of 47.64% in Recall and 34.39% in F1-score compared to the state-of-the-art approximation approach on COX2 dataset. In addition, our results are comparable with exact methods.

The task of discovering equivalent entities in knowledge graphs (KGs), so‐called KG entity alignment, has drawn much attention to overcome the incompleteness problem of KGs. The majority of existing techniques learns the pointwise representations of entities in the Euclidean space with translation assumption and graph neural network approaches. However, real vectors inherently neglect the complex relation structures and lack the expressiveness of embeddings; hence, they may guide the embeddings to be falsely generated which results in alignment performance degradation. To overcome these problems, we propose a novel KG alignment framework, ComplexGCN, which learns the embeddings of both entities and relations in complex spaces while capturing both semantic and neighborhood information simultaneously. The proposed model ensures richer expressiveness and more accurate embeddings by successfully capturing various relation structures in complex spaces with high‐level computation. The model further incorporates relation label and direction information with a low degree of freedom. To compare our proposal against the state‐of‐the‐art baseline techniques, we conducted extensive experiments on real‐world datasets. The empirical results show the efficiency and effectiveness of the proposed method.

Location-based social networks (LBSNs) have emerged over the past few years. Their exponential network effects depend on the fact that each user can share her daily digital footprints with different communities, in different places, and at different times (for example in the form of check-in activities). Unlike other types of social networks, activities in an LBSN can potentially be performed by several users in a collaborative way. Existing studies of representation learning for LBSNs often consider them as regular graphs and ignore these high-order, dynamic, and multi-role contexts, since their holistic interactions are quite difficult to capture. In this paper, we propose a model in which these holistic interactions can be learned and transferred into node embeddings derived from a hypergraph representation and a persona decomposition process. More specifically, the model learns from friendship edges, check-in hyperedges, and node personas at the same time, and devises multiple presentations for each user that reflects their multiple roles in a social context. The embedding learning process also exploits useful patterns such as user co-location and sequential effects through a carefully designed point-of-interest splitting step. Extensive experiments on real and synthetic datasets show that our model outperforms alternative state-of-the-art embedding methods on friendship and location prediction tasks.

With recent advancements in graph neural networks (GNN), GNN-based recommender systems (gRS) have achieved remarkable success in the past few years. Despite this success, existing research reveals that gRSs are still vulnerable to poison attacks , in which the attackers inject fake data to manipulate recommendation results as they desire. This might be due to the fact that existing poison attacks (and countermeasures) are either model-agnostic or specifically designed for traditional recommender algorithms (e.g., neighborhood-based, matrix-factorization-based, or deep-learning-based RSs) that are not gRS. As gRSs are widely adopted in the industry, the problem of how to design poison attacks for gRSs has become a need for robust user experience. Herein, we focus on the use of poison attacks to manipulate item promotion in gRSs. Compared to standard GNNs, attacking gRSs is more challenging due to the heterogeneity of network structure and the entanglement between users and items. To overcome such challenges, we propose GSPAttack —a generative surrogate-based poison attack framework for gRSs. GSPAttack tailors a learning process to surrogate a recommendation model as well as generate fake users and user-item interactions while preserving the data correlation between users and items for recommendation accuracy. Although maintaining high accuracy for other items rather than the target item seems counterintuitive, it is equally crucial to the success of a poison attack. Extensive evaluations on four real-world datasets revealed that GSPAttack outperforms all baselines with competent recommendation performance and is resistant to various countermeasures.

Hundreds of thousands of rumours emerge every day. Algorithmic models shall therefore support users of social platforms and provide alerts to prevent users from accidentally spreading rumours. However, existing alerting mechanisms are limited to post-hoc classification, and rumours are often detected after the damage has been done. This paper presents exRumourLens, a system that enables tracking and auditing of potential rumours as they emerge. To this end, it identifies local anomalies related to individual entities, as well as global anomalies on the level of subgraphs of a network of entities. exRumourLens provides various views on such local and global anomalies, thereby providing detailed explanations on emerging rumours and supporting their critical exploration. The source code is available at https://rumourlens.github.io/.

The Internet of Things (IoT) often generates large volumes of messy data which are difficult to process efficiently. While deep learning models have demonstrated their suitability in processing this data, the memory and processing requirements makes it difficult to deploy on edge nodes while achieving viable throughput results. Current solutions involve deploying the model in the cloud, but this leads to increased network costs due to the transfer of raw data. However, the layer based design of deep learning models allows for a model to be split into sub-models and deployed separately across IoT nodes. By deploying parts of the model on the edge node and in the cloud, the edge node is able to transmit an intermediate layer's feature output to the following sub-model instead of the raw input data. This reduces the size of the data being transmitted and results in a lower cost to the network. However, selecting the best layer to split the model becomes a multi-objective optimisation problem. In this paper, we propose an optimisation method that considers the network cost, input rate and processing overhead in selecting the best layer for splitting a model across an IoT network. We profile several popular model architectures to highlight their performance using this split deployment. Results from simulated and physical tests of the optimal layers are provided to demonstrate the method's effectiveness in real-world applications.

Reliable lane detection is a key component of autonomous vehicles supporting navigation in urban environments. This paper introduces the GOLDIE(Geometric Overture for Lane Detection by Intersections Entirety) system, a vision-based software architecture that uses an on-board single camera to determine the position of road lanes with respect to the vehicle. We propose an efficient vision-based lane-detection system that combines an appearance-based analysis with salient point tracking. The appearance-based analysis consists of segmenting high contrast areas that fit inside a Region-Of-Interest(ROI) on the frame. The salient point tracker selects interesting points based in a reference line, that guides a dynamic ROI. The tracking ROI look for paint lane marks close to the last lane reference found, where road marks are likely to emerge, in order to maintain the usability of the salient point tracker. The tracking is performed with the Lucas-Kanade algorithm and the lane points candidates are selected according to a predefined triangular model. Once such lanes points are detected, the vehicle position is estimated based on the intersection of linearised lanes determined through a vanishing point approach. Experiments and comparisons with other algorithms illustrate the applicability of the method.

This paper presents the components and overall architecture of the ubiquitous robot (Ubibot) system developed to demonstrate ubiquitous robotics, a new paradigm for integrated services. The system has been developed on the basis of the definition of the ubiquitous robot as that of encompassing the software robot Sobot, embedded robot Embot and the mobile robot Mobot. This tripartite partition, which independently manifests intelligence, perception and action, enables the abstraction of intelligence through the standardization of sensory data and motor or action commands. The Ubibot system itself is introduced along with its component subsystems of Embots, the position Embot, vision Embot and sound Embot, the Mobots of Mybot and HSR, the Sobot, Rity, a virtual pet modeled as an artificial creature, and finally the middleware which seamlessly enables interconnection between other components. Three kinds of experiments are devised to demonstrate the fundamental features, of calm sensing, context awareness and seamless service transcending the spatial limitations in the abilities of earlier generation personal robots. The experiments demonstrate the proof of concept of this powerful new paradigm which shows great promise.