A service-recommendation method for the Internet of Things leveraging implicit social relationships

Graph Neural Networks (GNNs) have become the state-of-the-art method for many applications on graph structured data. GNNs are a model for graph representation learning, which aims at learning to generate low dimensional node embeddings that encapsulate structural and feature-related information. GNNs are usually trained in an end-to-end fashion, leading to highly specialized node embeddings. While this approach achieves great results in the single-task setting, the generation of node embeddings that can be used to perform multiple tasks (with performance comparable to single-task models) is still an open problem. We propose the use of meta-learning to allow the training of a GNN model capable of producing multi-task node embeddings. In particular, we exploit the properties of optimization-based meta-learning to learn GNNs that can produce general node representations by learning parameters that can quickly (i.e. with a few steps of gradient descent) adapt to multiple tasks. Our experiments show that the embeddings produced by a model trained with our purposely designed meta-learning procedure can be used to perform multiple tasks with comparable or, surprisingly, even higher performance than both single-task and multi-task end-to-end models.

In the era of Internet of Things (IoT), intelligent recommendation is playing an important role in our daily life. How to provide personalized information to users is the core concern of Internet content service providers. To improve the recommendation quality, it is a hot topic to go beyond merely user–item interaction records and take social relations into account in IoT. Recently, emerged graph neural networks (GNNs) shine a light on simulating the recursive social diffusion process, to refine user embedding learning. Nevertheless, two key issues have not been well studied in previous studies: 1) they usually model user preference and social influence within a same semantic space and fail to simultaneously inject high-order connectivity information reflected in both user–item interaction graph and user–user social graph and 2) they typically rely on negative sampling to optimize a recommendation model, which makes them highly sensitive to the design of the sampler and hardly makes full use of GPU’s computing ability. In light of this, we propose a novel framework for item recommendation, namely, an efficient adaptive graph convolutional network (EAGCN). Specifically, we introduce a space-adaptive graph convolutional module, which could jointly explore the propagation process of user interest and social influence. Furthermore, a user-specific gating mechanism is designed to aggregate user representations from both spaces. To make EAGCN practical in social IoT, we devise a fast nonsampling leaner to optimize EAGCN’s parameters with better leveraging matrix computing of GPU. Extensive experiments under four scenarios show that our solution consistently and significantly outperforms strong baseline methods in both model effectiveness and training efficiency.

People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexible and adaptive way to complete social relationship discovery. Using the term-define capability of ontologies and the graphical structure of the conceptual graph, this paper presents a social relationship description with formal syntax and semantics. The reasoning procedure working on this formal representation can exploit the capability of ontology reasoning and graph homomorphism-based reasoning. A social relationship graph constructed from the Lehigh University Benchmark (LUBM) is used to test the efficiency of the relationship discovery method.

Path-aware multi-hop graph towards improving graph learning

The “Social Internet of Things (SIoT)” is a combination of the Internet of Things (IoT) and social networks to form a new paradigm. The SIoT promotes the development of smart cities, smart transportation, and many other fields. In SIoT, the openness and mobility of objects are enhanced. However, this tends to lead to network data sparsity problems. By distinguishing explicit and implicit social relationships, we introduce an implicit social relationship-based trust management model (IRTM) for reliable service delivery in SIoT. IRTM establishes implicit social relationships among nodes by mining their latent characteristics and trust transitivity. It models SIoT by creating sub-networks for each social relationship as a way to fuse the impact of different types of social relationships on trust management. To address the problem of malicious attacks by malicious nodes in the network to protect their interests, it considers two metrics, node relationship strength, and recommendation reliability, to filter malicious recommendations. Experiments conducted in the presence of data sparsity and malicious objects show that IRTM can improve the accuracy and convergence of trust evaluation compared to other methods that ignore implicit social relationships when computing trust. In addition, our scheme can improve resistance to trust-related attacks.

With the rapid development of technologies in the field of service computing, and increasing of complex business requirements, more and more large-scale service ecosystem emerges. Thus, many researches of service ecosystem focus on issues related to optimization such as service recommendation and load balancing, so the API popularity prediction problem studied in this paper, which is basis for this service ecosystem optimization, becomes a research hotspot in this field. However, many existing researches are predicting the popularity of APIs based on API functions, QoS, history usage patterns and social relationships, which are difficult to obtain and cannot reflect the overall structure of the underlying service ecosystem. Therefore, we propose an innovative API popularity prediction method in service ecosystem based on Graph Neural Network (GNN). Concretely, a Global-Service Ecosystem Network (GSEN) model is proposed firstly, for modeling a given service ecosystem to a network that can depict the complex structure of service ecosystem and the functions, QoS, history usage patterns and social relationships of APIs. Then, a Graph Heterogeneous Spatiotemporal Convolutional Network (GHSCN) model is proposed to predict the popularity of APIs based on GSEN, and for getting better prediction accuracy, four different Heterogeneous Spatiotemporal Convolutional Kernels are proposed to extract the features of different elements which have different mechanisms to affect the popularity of target API. Finally, extensive experiments based on the data crawled from ProgrammableWeb.com show that our method achieves a superior performance in API popularity prediction, and the importance of the introduction of our model to service ecosystems.

Graph Neural Networks (GNNs) have demonstrated significant success in various graph-related tasks. However, their performance is highly dependent on the availability of sufficient, class-balanced annotated data and an accurate graph structure. GNNs are vulnerable to substantial performance degradation when confronted with limited annotated samples, class imbalance, or noisy graph structures. First, a Dual GNN learning framework is introduced for semi-supervised node classification under limited supervision. The framework comprises two GNN-based node prediction modules: a primary module, which uses the input graph structure to induce standard node embeddings and predictions, and an auxiliary module, which leverages spectral clustering to construct a new graph structure and learn new node embeddings and predictions. These modules collaborate to enable end-to-end learning of discriminative node representations. Next, a graph augmentation method called Graph Dual Mixup (GDM) is proposed for graph classification with scarce labels. GDM employs a graph structural auto-encoder to learn structural embeddings, applying mixup in the learned structural embedding space to generate new graph structures. Additionally, mixup is applied to input node features, generating node features for new graph instances. Together, these generated node features and graph structures contribute new graphs that increase the size and diversity of the labeled dataset, improving classification performance. To enhance robustness against adversarial attacks on graph structures, an Efficient Low-Rank GNN is introduced. This approach learns robust low-rank sparse graph structures through a two-stage process. In the first stage, Singular Value Decomposition is used to estimate a low-rank approximation of the graph structure. This estimate is refined in the second stage by jointly learning a low-rank sparse graph structure alongside the GNN model, resulting in enhanced robustness. Finally, to address class-imbalanced node classification, a Unified GNN Learning (Uni-GNN) framework is proposed. Uni-GNN integrates structural and semantic connectivity representations to extend the propagation of embeddings to non-adjacent structural neighbors and semantically similar nodes, facilitating the diffusion of discriminative information. Additionally, it incorporates a balanced pseudo-label generation mechanism to improve the representations of minority classes. Comprehensive evaluations on benchmark datasets demonstrate that these methods outperform state-of-the-art approaches and hold significant potential for advancing GNN learning with limited supervision.

Recently, Graph Neural Networks (GNNs) have been widely used for fraud detection. GNNs first generate node embedding by aggregating neighboring information under different relations, and then use the final node embedding to detect the node’s suspiciousness. However, traditional GNNs employing only a single type of aggregator fail to capture neighbor information from multiple perspectives and treating different relations equally inevitably weakens the semantic information of heterogeneous graphs. Meanwhile, expressive ability of GNNs is limited by using conventional concatenating or averaging operations to update the center node. Also, camouflaged entities could damage GNN-based models. To handle these problems, a novel heterogeneous GNN model called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Multiple Aggregators and Feature Interactions Network</i> (MAFI) is proposed in this paper to conduct fraud detection tasks. Concretely, multiple types of aggregators are applied on different relations to aggregate neighbor information and aggregator-level attention is utilized to learn the importance of different aggregators. Also, relation-level attention is leveraged to learn the importance of each relation. Besides, conventional update operations are replaced with vector-wise implicit and explicit feature interactions. Moreover, a trainable neighbor sampler is employed to filter camouflaged fraudsters. Comprehensive experiments on two real-world fraud datasets indicate that the proposed MAFI outperforms existing GNN-based fraud detectors.

This study proposes a joint identification algorithm based on multi-source data fusion and Graph Neural Networks (GNN) to simultaneously detect adolescent internet addiction and psychological disorders. With the rapid development of internet technology, adolescent internet addiction has become increasingly prominent, and there exists a complex bidirectional relationship between internet addiction and psychological abnormalities. Traditional single-source data modeling methods struggle to accurately capture these intricate psychological and behavioral patterns. To address this, the study integrates multi-source heterogeneous data, including online behavior logs, psychological assessment data, physiological signals, and social relationships, to construct a Heterogeneous Graph Neural Network (H-GNN) model. Through a multi-task learning mechanism, the model simultaneously predicts the risks of internet addiction and psychological disorders. Experimental results demonstrate that the model outperforms traditional machine learning and deep learning models in terms of accuracy and AUC, effectively capturing the complex interactions between adolescent behaviors, psychological states, and social relationships. Ablation studies further validate the critical role of psychological features in prediction, and the model's potential in real-world applications is highlighted.

Social recommendation with Graph Neural Networks(GNNs) learns to represent cold users by fusing user-user social relations with user-item interactions, thereby alleviating the cold-start problem associated with recommender systems. Despite being well adapted to social relations and user-item interactions, these supervised models are still susceptible to popularity bias. Contrastive learning helps resolve this dilemma by identifying the properties that distinguish positive from negative samples. In its previous combinations with recommender systems, social relationships and cold-start cases in this context are not considered. Also, they primarily focus on collaborative features between users and items, leaving the similarity between items under-utilized. In this work, we propose socially-aware dual contrastive learning for cold-start recommendation, where cold users can be modeled in the same way as warm users. To take full advantage of social relations, we create dynamic node embeddings for each user by aggregating information from different neighbors according to each different query item, in the form of user-item pairs. We further design a dual-branch self-supervised contrastive objective to account for user-item collaborative features and item-item mutual information, respectively. On one hand, our framework eliminates popularity bias with proper negative sampling in contrastive learning, without extra ground-truth supervision. On the other hand, we extend previous contrastive learning methods to provide a solution to cold-start problem with social relations included. Extensive experiments on two real-world social recommendation datasets demonstrate its effectiveness.

Graph neural networks (GNNs) have known an increasing success recently, with many GNN variants achieving state-of-the-art results on node and graph classification tasks. The proposed GNNs, however, often implement complex node and graph embedding schemes, which makes it challenging to explain their performance. In this paper, we investigate the link between a GNN’s expressiveness, that is, its ability to map different graphs to different representations, and its generalization performance in a graph classification setting. In particular, we propose a principled experimental procedure where we (i) define a practical measure for expressiveness, (ii) introduce an expressiveness-based loss function that we use to train a simple yet practical GNN that is permutation-invariant, (iii) illustrate our procedure on benchmark graph classification problems and on an original real-world application. Our results reveal that expressiveness alone does not guarantee a better performance, and that a powerful GNN should be able to produce graph representations that are well separated with respect to the class of the corresponding graphs.

The session-based recommendation aims to predict users' immediate next actions based on their short-term behaviors reflected by past and ongoing sessions. Graph neural networks (GNNs) recently dominated the related studies, yet their performance heavily relies on graph structures, which are often predefined, task-specific, and designed heuristically. Furthermore, existing graph-based methods either neglect implicit correlations among items or consider explicit and implicit relationships altogether in the same graphs. We propose to decouple explicit and implicit relationships among items. As such, we can capture the prior knowledge encapsulated in explicit dependencies and learned implicit correlations among items simultaneously in a flexible and more interpretable manner for effective recommendations. We design a dual graph neural network that leverages the feature representations extracted by two GNNs: a graph neural network with a single gate (SG-GNN) and an adaptive graph neural network (A-GNN). The former models explicit dependencies among items. The latter employs a self-learning strategy to capture implicit correlations among items. Our experiments on four real-world datasets show our model outperforms state-of-the-art methods by a large margin, achieving 18.46% and 70.72% improvement in [email protected], and 49.10% and 115.29% improvement in [email protected] on Diginetica and LastFM datasets.

GNN-MgrPool: Enhanced graph neural networks with multi-granularity pooling for graph classification

Graph Neural Networks (GNNs) have achieved promising results in numerous applications. To ensure accurate predictions, GNNs may exploit user data beyond the scope of consent, raising significant concerns regarding data usage and privacy. A critical task, therefore, is data usage privacy auditing, which aims to infer whether a data subject's information was improperly used during model training. While Membership Inference Attacks (MIA) are commonly used to audit data usage in fully supervised settings, most GNNs operate under a semi-supervised paradigm, where nodes fall into three distinct categories with varying degrees of involvement in training. This heterogeneity introduces a key challenge for applying traditional binary MIA methods. To address this, we study ternary data usage privacy auditing for semi-supervised GNNs in a black-box setting using a shadow training-based MIA framework. We identify that the main obstacle lies in the effects of message passing, which entangles node representations across categories. To tackle this, we propose MPMIA--a novel Message Passing-aware Membership Inference Attack--which leverages point-wise KL divergence to quantify and exploit the influence of message passing on node embeddings. Extensive experiments validate the effectiveness of MPMIA, showing clear improvements over baseline methods in accurately auditing data usage within semi-supervised GNNs.

Graph clustering aiming to partition nodes into several disjoint subsets is a fundamental task for graph-structured learning. Traditional graph clustering methods only consider the adjacency information. In recent years, inspired by the homophily assumption that the adjacent nodes tend to have similar features and labels, most existing graph clustering approaches leverage node attribute information to improve graph clustering performance. These works have mainly focused on node embedding learning via the various combinations of auto-encoder and graph neural networks. As for clustering learning, they introduce a self-optimizing strategy that assumes that all clusters are homogeneous. However, this assumption usually does not hold since the size and variance of different clusters can be quite different, and self-optimizing strategy is incompetent in dealing with this heterogeneous clusters. In this work, we propose a novel method named Adaptive Harmony Learning and Optimization (AHLO) for attributed graph clustering, which models the node embeddings with the mixture of von Mises-Fisher distributions on the unit hypersphere and develops an alternating learning strategy. Specifically, we take the node embeddings as the supervisory signals for the update of the mixture parameters, and the mixture distribution as the supervisory signals for the update of the node embeddings. To prevent small clusters from annexing by large clusters, we develop the regularized harmony loss to enhance the prediction on small clusters. In the mixture parameter optimization stage, we utilize EM algorithm and heuristically design a center update scheme with consideration of the posterior probability confidence and the impact of other centers. Hence, AHLO can simultaneously improve the intra-cluster compactness and inter-cluster separability. Extensive experiments on four benchmark attributed graph datasets have demonstrated the effectiveness of our proposed AHLO.