Recently, knowledge graphs have been utilised in recommendation systems to improve accuracy by integrating item-side auxiliary information. However, structural user-side knowledge is difficult to construct and integrate due to inherent scarcity and improper granularity. This paper introduces a graph contrastive learning with Semantic transitions-Enhanced DIffusion architecture based on Large Language Models (LLMs) for user-side knowledge-aware Recommendation (SEDIRec). Specifically, our SEDIRec first leverages LLMs to infer user interests from historical behaviors, integrating this user-side information with item-side and collaborative data to construct main views. Then, two contrastive views are generated using diffusion models with semantic transitions: one at the user-side level and the other at the item-side level. For both contrastive views, we integrate user-side or item-side information with collaborative data to generate a user-item graph. Subsequently, each user-item graph is transformed into collaborative data spaces via diffusion models for generating contrastive views. This procedure not only enhances the alignment between user/item-side information and the semantic spaces of collaborative data but also effectively eliminates noise. Extensive experiments on three datasets reveal the superiority of SEDIRec, especially for users with sparse interactions.

Multimodal-based recommendation integrates visual, textual, and acoustic modalities of items to comprehensively capture user preferences, thereby playing a crucial role in modern multimedia online platforms. In this paper, we propose aDiffusion-based Multi-agent withReinforcement Learning forMultimodal-basedRecommendation (DRMRec). Specifically, our DRMRec first leverages a hierarchicalDiffusion-basedMulti-Agent (DMA) to reconstruct the user-item interaction graph. Subsequently, these reconstructed graphs are fused with multimodal information to form contrastive views. During the reconstruction, each agent is injected with multimodal information through aCross-ModalAligner (CMA), thereby bringing cross-modal information closer to interaction embeddings and facilitating alignment across modalities. Then, we introduce aMetric-AwareDiffusionReinforcer (MADR), a reinforcement learning framework that leverages validation-set recommendation metrics as reward signals to enable dynamic and individual fine-tuning of each agent, thereby actively aligning model optimization with recommendation tasks. Next, we apply cross-modal graph contrastive learning to contrastive views, alleviating data sparsity while further enhancing cross-modal alignment. Extensive experiments on three real-world multimedia platform datasets demonstrate that DRMRec consistently outperforms state-of-the-art approaches in multimodal-based recommendation. It is noteworthy that the performance improvements across all three metrics on both the TikTok and Sports datasets exceed 10%.

Recommendation systems have been widely applied across various domains. However, they have consistently been hindered by the cold-start problem. One of the recent solutions involves extracting knowledge from other domains and applying it to the target domain’s recommendations. This is also one of the issues that cross-domain recommendation (CDR) aims to address. Mapping methods serve as a means to translate data across distinct domains, thereby boosting the performance of CDR. Meanwhile, diffusion probabilistic models can also be regarded as a superior data transformation model. They have achieved significant breakthroughs in image synthesis tasks, capable of recovering images from noise-added samples. To further improve the recommendation performance of CDR, we propose a novel CDR diffusion model [guided diffusion with bilateral alignment cross-domain recommendation (GDBACDR)]. First, a diffusion model (DM) is designed to guide the feature vectors of target domain users in the source and target domains. Simultaneously, a bilateral alignment module between the source and target domains is utilized to enhance the stability introduced by the DM, mitigate the negative impacts of randomness, and ensure the consistency of the reconstructed user embeddings with the target domain. The loss function of GDBACDR is formulated to achieve specific tasks. Finally, extensive experiments were conducted on existing datasets, and the results showed that the proposed method outperformed baseline models in every CDR task under the cold-start scenarios, demonstrating the effectiveness and adaptability of the proposed method.

Multi-relational graph learning aims to embed entities and relations in knowledge graphs into low-dimensional representations, which has been successfully applied to various multi-relationship prediction tasks, such as information retrieval, question answering, and etc. Recently, contrastive learning has shown remarkable performance in multi-relational graph learning by data augmentation mechanisms to deal with highly sparse data. In this paper, we present a Multi-Relational Graph Contrastive Learning architecture (MRGCL) for multi-relational graph learning. More specifically, our MRGCL first proposes a Multi-relational Graph Hierarchical Attention Networks (MGHAN) to identify the importance between entities, which can learn the importance at different levels between entities for extracting the local graph dependency. Then, two graph augmented views with adaptive topology are automatically learned by the variant MGHAN, which can automatically adapt for different multi-relational graph datasets from diverse domains. Moreover, a subgraph contrastive loss is designed, which generates positives per anchor by calculating strongly connected subgraph embeddings of the anchor as the supervised signals. Comprehensive experiments on multi-relational datasets from three application domains indicate the superiority of our MRGCL over various state-of-the-art methods. Our datasets and source code are published at https://github.com/Legendary-L/MRGCL.

The importance of social network alignment (SNA) for various downstream applications, such as social network information fusion and e-commerce recommendation, has prompted numerous professionals to develop and share SNA tools. However, malicious actors can exploit these tools to integrate sensitive user information, thereby posing cybersecurity risks. Although many researchers have explored attacking SNA (ASNA) through network modification attacks to protect users, practical feasibility remains challenging. In this study, we propose an effective node injection attack via a dynamic programming framework (DPNIA) to address the problem of modeling and solving ASNA within a limited time and balancing the costs and benefits. DPNIA models ASNA as a problem of maximizing the number of confirmed incorrect correspondent node pairs with greater similarity scores than the pairs between existing nodes, thereby making ASNA solvable. A cross-network evaluation method is employed directly to identify node vulnerabilities, facilitating progressive attacking from easy to difficult. In addition, an optimal injection strategy searching method based on dynamic programming is used to determine which links should be added between the injected and existing nodes, thereby enhancing the effectiveness of the attack at a low cost. Experiments on four real-world datasets demonstrated that DPNIA consistently and significantly surpasses various baselines when attacking both multiple networks simultaneously and a single network.

Multimedia recommendations aim to use rich multimedia content to enhance historical user-item interaction information, which can not only indicate the content relatedness among items but also reveal finer-grained preferences of users. In this paper, we propose aKnowledge-awareDiffusion-Enhanced architecture using contrastive learning paradigms (KDiffE) for multimedia recommendations. Specifically, we first utilize original user-item graphs to build an attention-aware matrix into graph neural networks, which can learn the importance between users and items for main view construction. The attention-aware matrix is constructed by adopting a random walk with a restart strategy, which can preserve the importance between users and items to generate aggregation of attention-aware node features. Then, we propose a guided diffusion model to generate strongly task-relevant knowledge graphs with less noise for constructing a knowledge-aware contrastive view, which utilizes user embeddings with an edge connected to an item to guide the generation of strongly task-relevant knowledge graphs for enhancing the item's semantic information. We perform comprehensive experiments on three multimedia datasets that reveal the effectiveness of our KDiffE and its components on various state-of-the-art methods. Our source codes are available.

The introduction of energy harvesting technology into wireless sensor nodes has advanced the realization of autonomous wireless sensor networks. Enabling Energy harvesting wireless sensor(EHWS) nodes to maintain an Energy Neutral Operation(ENO) state by dynamically adjusting energy consumption to match the varying collected energy effectively enhances the node’s continuous survival time and energy efficiency. This paper introduces for the first time the concept of Battery Energy Neutral Operation (BENO) and proposes an Adaptive Energy Management method based on Actor-Critic for energy harvesting wireless sensor nodes (ACEM) based on this concept. BENO thoroughly considers the role of rechargeable batteries as energy buffers in EHWS energy systems in maintaining the ENO state of the node. Building upon BENO, we designs a reward function to develop the ACEM algorithm, dynamically adjusting the duty cycle to match energy consumption with collected energy, thereby sustaining the ENO state of the node. Experimental results show that under different initial energy settings, the ACEM method based on BENO achieves an average duty cycle increase of 61.11% and 43.97%, respectively compared to the AQL and FQL benchmark methods. The waste energy rates have been reduced by 0.64% and 5.53%, respectively. Achieving node BENO significantly affects maintaining the ENO state of the node.

In this paper, we propose a self-supervised learning method for robust facial pose estimation. Conventional methods usually split the coherent head motion into discrete and finite outputs, likely leading to bias prediction because the performance of head pose estimation highly relies on structural facial appearance. To address this issue, our model achieves structural equivariance to poses through a self-supervised learning strategy from extrinsic attributes of face neighbors and underlying local associations. Specifically, we construct a complete neighbor graph to capture the extrinsic properties of face neighbors, where different latent semantic attributes are assigned to each subgraph. Accordingly, we design a set of proxy tasks based on different attribute subgraphs, where the model is encouraged to learn the underlying relation of local features under pose variation. Extensive experimental results on the challenging, widely evaluated datasets indicate the effectiveness of our model compared with the state of the arts.

Facial age estimation aims to rank the face aging data by taking in the correlation among age categories. Conventional age estimation models are trained based on assumed high-quality training annotations in a totally-supervised manner. However, noisy data in a sparse distribution collected from unconstrained environment usually account for the corruption of produced gradients and ordinal relationships, which may fail to fairly describe the correlated face aging data. In this paper, we propose a meta-set learning (MSL) approach for exploiting the unfairness of face aging datasets, thus achieving unbiased age classification in unconstrained conditions. To address this, we elaborately create an unfairness filtration network under the meta-learning paradigm, which exceeds a reliable margin-reweighting initialization suffering from class variance, simultaneously exploiting the meta-reweighting intervention to minimize the training bias caused by class imbalance. Moreover, our proposed model leverages the learned instance-level margin between logits and develops a unimodal constrained logits loss, further surviving age regression models from unfairness. Experimental results on multiple in-the-wild datasets demonstrate that our proposed method achieves superior results compared to existing state-of-the-art methods.

Abstract Temporal networks are networks that edges evolve over time, hence link prediction in temporal networks aims at inferring new edges based on a sequence of network snapshots. In this paper, we propose a graph wavelet neural network (TT-GWNN) framework using topological and temporal features for link prediction in temporal networks. To capture topological and temporal features, we develope a second-order weighted random walk sampling algorithm. It combines network snapshots with both first-order and second-order weights into one weighted graph. Moreover, it incorporates a damping factor to assign greater weights to more recent snapshots. Next, we adopt graph wavelet neural networks to embed the vertices and use gated recurrent units for predicting new links. Extensive experiments demonstrate that TT-GWNN can effectively predict links on temporal networks.