In the realm of real-world knowledge graphs, the dynamism of facts is a prevailing characteristic. To illustrate, a popular restaurant was awarded a Michelin star in 2004 and retained this prestigious recognition in 2008, but lost it in 2012 due to changes in management and menu quality. This sequence highlights how neglecting temporal context can lead to misconceptions about factual accuracy. Furthermore, the relations intertwining distinct entities or the same entity across different chronological markers exhibit complexity and hierarchy. Regrettably, existing methods for temporal knowledge graph relation prediction fall short in following challenges: they lack a nuanced, hierarchical comprehension of knowledge structure and fail to adeptly integrate temporal dynamics with static attributes. Addressing these issues, this study introduces Hyperbolic-based Temporal Knowledge Graph Relation Prediction (HTKGP) approach to harness the power of hyperbolic geometry. Our innovation is an attention-guided, learnable curvature mechanism designed to preserve and enrich the intricate semantic hierarchy inherent in data. Besides, we propose a longitudinal information entity embedding strategy due to the plentiful temporal information. This not only captures the enduring impact of past events on present states but also achieves efficiency through parameter reduction. Empirical validation across multiple datasets shows HTKGP efficiently navigates the rich semantic landscape within hyperbolic spaces and yields superior predictive performance. Our implementations are publicly available at: https://github.com/jianruichen/HTKGP .

Federated cross-domain recommendation aims to alleviate the problem of data sparsity and enable collaborative modeling of user behavior data from different platforms or institutions while ensuring data privacy. Most existing federated cross-domain recommendation methods rely on item IDs for modeling, ignoring the mining and utilization of item semantic information. In addition, due to the heterogeneity of data between different domains, the model is prone to domain bias and feature coupling problems during the aggregation process, which negatively impacts the recommendation performance. This paper proposes a dual-target federated cross-domain recommendation framework with semantic enhancement and disentangled contrastive learning. First, to utilize semantic information of items, item IDs features and text semantic features are jointly fused to enhance the item embedding representations. Second, we propose a user representation decoupling mechanism to explicitly decouple users preferences into shared and domain-specific preferences, thereby alleviating domain bias and feature coupling problems. Furthermore, we design a cross-domain contrastive learning module on the server side to enhance the consistency and transferability of shared representations between user representations across different domains. Experimental results show that the proposed algorithm performs significantly better than existing optimal methods on multiple real-world datasets, demonstrating its excellent performance in federated cross-domain recommendations.

Multi-layer networks treat various types of interactions at each level to model complex systems in nature and society, and clustering of them is of great significance for revealing mechanisms of systems. Vast majority of current algorithms focus on identifying the common communities in clear multi-layer networks, and few attempt has been devoted to the detection of layer-specific communities in noised ones. To address these issues, a joint learning algorithm withGraphDenoising andStructureLearning (calledGDSL) for the detection of layer-specific communities in noised multi-layer networks is proposed, which simultaneously integrates graph denoising, structure learning, and module detection. To remove noise of networks, GDSL re-constructs affinity graphs for the original ones by preserving community structure. To enhance robustness and discriminative of features, GDSL explores the relations of features among various layers with the Hilbert-Schmidt Independence Criterion and structure learning. Finally, GDSL joins all these procedures with an objective function, and deduces optimization rules. The results show that GDSL not only significantly outperforms baselines but also enhances the robustness of the algorithm, providing an effective model for community detection in noised multi-layer networks.

Recently, maximizing mutual information has emerged as a powerful tool for unsupervised graph representation learning. Existing methods are typically effective in capturing graph information from the topology view but consistently ignore the node feature view. To circumvent this problem, we propose a novel method by exploiting mutual information maximization across feature and topology views. Specifically, we first construct the feature graph to capture the underlying structure of nodes in feature spaces by measuring the distance between pairs of nodes. Then we use a cross-view representation learning module to capture both local and global information content across feature and topology views on graphs. To model the information shared by the feature and topology spaces, we develop a common representation learning module by using mutual information maximization and reconstruction loss minimization. Here, minimizing reconstruction loss forces the model to learn the shared information of feature and topology spaces. To explicitly encourage diversity between graph representations from the same view, we also introduce a disagreement regularization to enlarge the distance between representations from the same view. Experiments on synthetic and real-world datasets demonstrate the effectiveness of integrating feature and topology views. In particular, compared with the previous supervised methods, the proposed method achieves comparable or even better performance under the unsupervised representation and linear evaluation protocol.

Graph Neural Network (GNN) is capable of applying deep neural networks to graph domains. Recently, Message Passing Neural Networks (MPNNs) have been proposed to generalize several existing graph neural networks into a unified framework. For graph representation learning, MPNNs first generate discriminative node representations using the message passing function and then read from the node representation space to generate a graph representation using the readout function. In this paper, we analyze the representation capacity of the MPNNs for aggregating graph information and observe that the existing approaches ignore the self-loop for graph representation learning, leading to limited representation capacity. To alleviate this issue, we introduce a simple yet effective propagation enhanced extension, Self-Connected Neural Message Passing (SC-NMP), which aggregates the node representations of the current step and the graph representation of the previous step. To further improve the information flow, we also propose a Densely Self-Connected Neural Message Passing (DSC-NMP) that connects each layer to every other layer in a feed-forward fashion. Both proposed architectures are applied at each layer and the graph representation can then be used as input into all subsequent layers. Remarkably, combining these two architectures with existing GNN variants can improve these models’ performance for graph representation learning. Extensive experiments on various benchmark datasets strongly demonstrate the effectiveness, leading to superior performance for graph classification and regression tasks.

Graph matching endeavors to find corresponding nodes across two or more graphs, which plays a fundamental role in many vision and pattern matching tasks. However, existing graph matching algorithms often meet abnormal graphs with missing node features and suffer from numerous cluttered outliers in practical applications. To address these, we propose a novel deep graph matching method called Random Deep Graph Matching (RDGM). Different from the deterministic affinity inference in existing deep graph matching methods, RDGM performs message passing in a random manner during model training through randomly masking some available node features in the source or target graph, so that the affinity inference between nodes is insensitive to specific neighborhoods. In addition, a hierarchical attention graph neural network framework is devised in the node embedding process of RDGM, which can obtain more sufficient high-order structural information to reduce the impact of latent noise on affinity learning. Extensive experiments suggest that the proposed RDGM outperforms state-of-the-art graph matching methods, and demonstrates strong robustness and generalization performance.

Representing nodes in a network as low-dimensional dense vectors can facilitate the analysis of complex networks, which is a challenging task and has attracted increasing attention. However, in the real world, networks are changing over time, such as cooperation in citation networks and communication in email networks. Most of the recent embedding methods only focus on static networks. Thus they ignore the critical temporal information, which serves as a supplement to structure information and has been proved to improve the quality of node embedding. In this work, we propose an unsupervised deep learning model called DTINE, which explores temporal information for further enhancing the robustness of node representations in dynamic networks. To preserve network topology, we pertinently design a temporal weight and sampling strategy to extract features from the neighborhoods. An attention mechanism will be applied on the recurrent neural network to measure the contributions of historical information and capture the evolution of the networks. Experimental results on four real-world networks demonstrate that the proposed method achieves better performance than state-of-the-art methods.

Network embedding has been widely used to solve the network analytics problem. Existing methods mainly focus on networks with single-layered homogeneous or heterogeneous networks. However, many real-world complex systems can be naturally represented by multilayer networks, which is another term of heterogeneous networks with multiple edge/relation types. The problem of how to capture and utilize rich interaction information of multi-type relations causes a major challenge of multilayer network embedding. To address this problem, we propose a fast and scalable multilayer network embedding model, called HMNE, to efficiently preserve and learn information of multi-type relations into a unified embedding space. We develop a heuristic 3D interactive walk technique dedicated for multilayer networks, which can leverage rich interactions among distinct layers and effectively capture important information contained in the layered structure. We evaluate our proposed model HMNE on two downstream analytic applications: node classification and link prediction. Experimental results on seven social and biological multilayer network datasets demonstrate that the proposed model outperforms existing competitive baselines with reduced time and memory occupations.

Many complex systems derived from nature and society consist of multiple types of entities and heterogeneous interactions, which can be effectively modeled as heterogeneous information network (HIN). Structural analysis of heterogeneous networks is of great significance by leveraging the rich semantic information of objects and links in the heterogeneous networks. And, clustering heterogeneous networks aims to group vertices into classes, which sheds light on revealing the structure–function relations of the underlying systems. The current algorithms independently perform the feature extraction and clustering, which are criticized for not fully characterizing the structure of clusters. In this study, we propose a learning model by joint Graph Embedding and Nonnegative Matrix Factorization (aka GEjNMF ), where feature extraction and clustering are simultaneously learned by exploiting the graph embedding and latent structure of networks. We formulate the objective function of GEjNMF and transform the heterogeneous network clustering problem into a constrained optimization problem, which is effectively solved by l 0 -norm optimization. The advantage of GEjNMF is that features are selected under the guidance of clustering, which improves the performance and saves the running time of algorithms at the same time. The experimental results on three benchmark heterogeneous networks demonstrate that GEjNMF achieves the best performance with the least running time compared with the best state-of-the-art methods. Furthermore, the proposed algorithm is robust across heterogeneous networks from various fields. The proposed model and method provide an effective alternative for heterogeneous network clustering.