Higher-order Information Research Articles

MGDRGCN: A novel framework for predicting metabolite–disease connections using tripartite network and relational graph convolutional network

Metabolites are fundamental to the existence of biomolecules, and numerous studies have demonstrated that uncovering the connections between metabolites and diseases can enhance our understanding of disease pathogenesis. Traditional biological methods can identify potential metabolite–disease relationships, but these approaches often require significant human and material resources. Consequently, computational methods have emerged as a more efficient alternative. However, most computational methods primarily rely on metabolite–disease associations and rarely explore the impact of more biological entities. To address this issue, we propose a novel computational framework based on a metabolite–gene–disease tripartite heterogeneous network and relational graph convolutional network (R-GCN), abbreviated as MGDRGCN. Specifically, we construct three types of similarity networks from multiple data sources, including metabolite and gene functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity for metabolites and diseases. Next, we use principal component analysis to further extract features and construct a tripartite heterogeneous network with genes as the bridge. This network structure comprehensively captures and represents the complex relationships among metabolites, genes and diseases. We employ R-GCN to extract higher-order information from the tripartite heterogeneous network. Finally, we input the embeddings learned from R-GCN into a residual network classifier to predict potential metabolite–disease associations. In five-fold cross-validation experiments, MGDRGCN exhibit outstanding performance, with both AUC (0.9866) and AUPR (0.9865) significantly surpassing other advanced methods. Additionally, case studies further demonstrate MGDRGCN’s superior performance in predicting metabolite–disease associations. Overall, the introduction of MGDRGCN provides new perspectives and methods for future biomedical research, offering promising potential for uncovering the mechanisms of complex biological systems.

A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm

For the protection of information security, link prediction, as a basic problem of network science, has important application significance. However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. Because of this, this paper proposes an algorithm called hypergraph-based link prediction with self-attention (HWA) to solve the above problems. The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. Experiments show that the HWA algorithm proposed in this paper, combined with the GCN framework, shows better link prediction performance than other graph-based neural network benchmark algorithms on eight real networks. This further verifies the validity and reliability of the model in this paper and provides new protection ideas and technical means for information security.

High-Order Local Clustering on Hypergraphs

Graphs are a commonly used model in data mining to represent complex relationships, with nodes representing entities and edges representing relationships. However, graphs have limitations in modeling high-order relationships. In contrast, hypergraphs offer a more versatile representation, allowing edges to join any number of nodes. This capability empowers hypergraphs to model multiple relationships and capture high-order information present in real-world applications. We focus on the problem of local clustering in hypergraphs, which computes a cluster near a given seed node. Although extensively explored in the context of graphs, this problem has received less attention for hypergraphs. Current methods often directly extend graph-based local clustering to hypergraphs, overlooking their inherent high-order features and resulting in low-quality local clusters. To address this, we propose an effective hypergraph local clustering model. This model introduces a novel conductance measurement that leverages the high-order properties of hypergraphs to assess cluster quality. Based on this new definition of hypergraph conductance, we propose a greedy algorithm to find local clusters in real time. Experimental evaluations and case studies on real-world datasets demonstrate the effectiveness of the proposed methods.

Dirichlet latent modelling enables effective learning and sampling of the functional protein design space

Engineering proteins with desired functions and biochemical properties is pivotal for biotechnology and drug discovery. While computational methods based on evolutionary information are reducing the experimental burden by designing targeted libraries of functional variants, they still have a low success rate when the desired protein has few or very remote homologous sequences. Here we propose an autoregressive model, called Temporal Dirichlet Variational Autoencoder (TDVAE), which exploits the mathematical properties of the Dirichlet distribution and temporal convolution to efficiently learn high-order information from a functionally related, possibly remotely similar, set of sequences. TDVAE is highly accurate in predicting the effects of amino acid mutations, while being significantly 90% smaller than the other state-of-the-art models. We then use TDVAE to design variants of the human alpha galactosidase enzymes as potential treatment for Fabry disease. Our model builds a library of diverse variants which retain sequence, biochemical and structural properties of the wildtype protein, suggesting they could be suitable for enzyme replacement therapy. Taken together, our results show the importance of accurate sequence modelling and the potential of autoregressive models as protein engineering and analysis tools.

NetBoost: Towards efficient distillation and service differentiation of network information exposure

Exposing network information such as distances between end hosts is useful to improve the quality of experiences for network users. Network providers calculate such information based on private topology and routing data and share it with users through well-established protocols such as Application-Layer Traffic Optimization. However, it is usually not intended to expose the original model, which can face scalability, user heterogeneity, efficacy & efficiency challenges.In this paper, we introduce NetBoost, a system that efficiently distills and differentiates ALTO-based network information to address these challenges concurrently. NetBoost significantly reduces the size of exposed network information by orders of magnitude. In particular, by utilizing a gradient boosting algorithm for classification and regression based on IP prefix matching, NetBoost provides high-order information exposure models, allowing network providers to offer differentiated services to clients with privacy-preserving. Our experimental results demonstrate that NetBoost performs effectively in resource-constrained scenarios, surpassing state-of-the-art lossy compression algorithms and achieving greater accuracy than the XGBoost gradient boosting algorithm, while maintaining a comparable compression rate. Furthermore, in simulation experiments conducted using the real-world networking software BIND, NetBoost achieved 6.96% and 5.2% higher accuracy compared to XGBoost under the same number of rules, with NetBoost’s accuracy set at 95% and 90%, respectively. Additionally, NetBoost reduced resolve time by 44.35% and 52.47%, respectively.

Combine multi-order representation learning and frame optimization learning for skeleton-based action recognition

Skeleton-based action recognition has broad application prospects in many fields such as virtual reality. Currently, the most popular way is to employ Graph Convolutional Networks (GCNs) or Hypergraph Convolutional Networks (HGCNs) for this task. However, GCN-based methods may heavily rely on the physical connectivity relationship between joints while lack the capture of higher-order information about interactions among distant joints, and HGCN-based methods usually introduce unnecessary noise when capturing low-order information of skeleton structures with simple topology. Besides, the current methods do not deal well with redundant frames and confusing frames. These limitations hinder the improvement of recognition accuracy. In this paper, we propose a novel network, called Hyper-Net, which combines multi-order representation learning and frame optimization learning for skeleton-based action recognition. Specifically, the proposed Hyper-Net contains Temporal-Channel Aggregation Graph Convolution (TCA-GC), Spatial-Temporal Aggregation Hypergraph Convolution (STA-HC) and Frame Optimization Learning (F-OL) modules. The TCA-GC aggregates low-order and local information from simple joint and bone topologies across different temporal and channel dimensions. The STA-HC captures high-order and global information from complex motion streams as well as solving the problem of spatial-temporal weight imbalance. The F-OL can adaptively extract key frames and distinguish confusing frames, thus improving the ability of the network to recognize confusing actions. A large number of experiments are conducted on the NTU RGB+D, NTU RGB+D 120 and NW-UCLA datasets for action recognition task. Experimental results demonstrate the superiority and effectiveness of the proposed network.

Heterogeneous propagation graph convolution network for a recommendation system based on a knowledge graph

Recently, there has been a surge of interest in recommendation systems that leverage knowledge graphs, primarily because of their effectiveness in addressing sparsity and cold-start challenges inherent in collaborative filtering approaches. In most previous studies, researchers have focused on the way knowledge associations are encoded in knowledge graphs, but have not sufficiently highlighted the signals of collaboration that are implicit in the interaction between users and items. As a result, the learned embeddings do not provide a complete representation of the semantic information. In this paper, we describe a new model called a heterogeneous propagation graph convolution network for a recommendation system combined with a knowledge graph (HP-GCN). It adopts a heterogeneous propagation to generate user embedding representations, thereby combining encoded collaborative signals and auxiliary knowledge in knowledge graphs. Furthermore, we incorporate an attention mechanism to differentiate the contributions made by diverse neighbors as opposed to those made by users. Since most graph convolutions tend to suffer from over-smoothing when the number of convolutional layers increases, leading to insufficient utilization of high-order information, this paper uses an improved graph convolution strategy to generate item embeddings. This strategy has two different aggregation mechanisms embedding into different subgraphs, which can more fully utilize high-order information and mitigate the over-smoothing problem. Thus, we are able to efficiently prevent negative information originating from higher-order neighbors into the process of embedding learning. In extensive experiments, we applied HP-GCN to four large-scale real datasets for music, books, movies, and restaurants. The experimental outcomes revealed that HP-GCN generally surpassed the baseline methods in both recommendation accuracy and diversity, showing superior recommendation performance overall.

Improving Hybrid Regularized Diffusion Processes with the Triple-Cosine Smoothness Constraint for Re-Ranking

In the last few decades, diffusion processes have been widely used to solve visual re-ranking problems. The key point of these approaches is that, by diffusing the baseline similarities in the context of other samples, more reliable similarities or dissimilarities can be learned. This was later found to be achieved by solving the optimization problem underlying the framework of the regularized diffusion process. In this paper, the proposed model differs from previous approaches in two aspects. Firstly, by taking the high-order information of the graph into account, a novel smoothness constraint, named the triple-cosine smoothness constraint, is proposed. The triple-cosine smoothness constraint is generated using the cosine of the angle between the vectors in the coordinate system, which is created based on a group of three elements: the queries treated as a whole and two other data points. A hybrid fitting constraint is also introduced into the proposed model. It consists of two types of predefined values, which are, respectively, used to construct two types of terms: the squared L2 norm and the L1 norm. Both the closed-form solution and the iterative solution of the proposed model are provided. Secondly, in the proposed model, the learned contextual dissimilarities can be used to describe “one-to-many” relationships, making it applicable to problems with multiple queries, which cannot be solved by previous methods that only handle “one-to-one” relationships. By taking advantage of these “one-to-many” contextual dissimilarities, an iterative re-ranking process based on the proposed model is further provided. Finally, the proposed algorithms are validated on various databases, and comprehensive experiments demonstrate that retrieval results can be effectively improved using our methods.

A semi-supervised framework fusing multiple information for knowledge graph entity alignment

Entity alignment (EA) is a fundamental task for cross linguistic knowledge graphs (KGs) understanding and interaction, which is committed to matching entities from different graphs based on their inherent semantics. Methods based on graph neural networks (GNNs) dominate the EA task, however, the majority of them ignore the higher-order information among entities in the KGs. Meanwhile, as important auxiliary information, the relational semantics, string information of entity names and attribute information of entities are insufficiently exploited during the inference phase. In addition, labeled alignment data is universally insufficient across various datasets, which limits the performance of the model. In this paper, we propose a Semi-supervised EA framework that Comprehensively considers both Structural and Attribute information within KGs (SCSA) to address these problems above. Specifically, our approach first leverages hypergraph neural networks (HGNN) to aggregate relational semantic information and graph convolutional networks (GCNs) with a highway filtering strategy to acquire the embedding representation of entities precisely. Then, we propose a bidirectional filtering technique with a combination of entity, attribute and string values to create pseudo-labeled data and lead the model for iteratively training. We implement our proposed framework on several publicly recognized cross-lingual datasets. The experimental results indicate that our framework outperforms almost all state-of-the-art (SOTA) methods.

Kpop: a kernel balancing approach for reducing specification assumptions in survey weighting

Abstract With the precipitous decline in response rates, researchers and pollsters have been left with highly nonrepresentative samples, relying on constructed weights to make these samples representative of the desired target population. Though practitioners employ valuable expert knowledge to choose what variables X must be adjusted for, they rarely defend particular functional forms relating these variables to the response process or the outcome. Unfortunately, commonly used calibration weights—which make the weighted mean of X in the sample equal that of the population—only ensure correct adjustment when the portion of the outcome and the response process left unexplained by linear functions of X are independent. To alleviate this functional form dependency, we describe kernel balancing for population weighting (kpop). This approach replaces the design matrix X with a kernel matrix, K encoding high-order information about X. Weights are then found to make the weighted average row of K among sampled units approximately equal to that of the target population. This produces good calibration on a wide range of smooth functions of X, without relying on the user to decide which X or what functions of them to include. We describe the method and illustrate it by application to polling data from the 2016 US presidential election.

PathMLP: Smooth path towards high-order homophily

Real-world graphs exhibit increasing heterophily, where nodes no longer tend to be connected to nodes with the same label, challenging the homophily assumption of classical graph neural networks (GNNs) and impeding their performance. Intriguingly, from the observation of heterophilous data, we notice that certain high-order information exhibits higher homophily, which motivates us to involve high-order information in node representation learning. However, common practices in GNNs to acquire high-order information mainly through increasing model depth and altering message-passing mechanisms, which, albeit effective to a certain extent, suffer from three shortcomings: (1) over-smoothing due to excessive model depth and propagation times; (2) high-order information is not fully utilized; (3) low computational efficiency. In this regard, we design a similarity-based path sampling strategy to capture smooth paths containing high-order homophily. Then we propose a lightweight model based on multi-layer perceptrons (MLP), named PathMLP, which can encode messages carried by paths via simple transformation and concatenation operations, and effectively learn node representations in heterophilous graphs through adaptive path aggregation. Extensive experiments demonstrate that our method outperforms baselines on 16 out of 20 datasets, underlining its effectiveness and superiority in alleviating the heterophily problem. In addition, our method is immune to over-smoothing and has high computational efficiency. The source code will be available in https://github.com/Graph4Sec-Team/PathMLP.

ARFIS: An adaptive robust model for regression with heavy-tailed distribution

As heavy-tailed distributions are ubiquitous in many real applications, robust regression has been extensively applied in machine learning and exhibits the superiority in deal with heavy-tailed distribution. Current existing robust methods for regression are based on independence assumption of features and ignore interaction between them, which may lead to poor generalization due to most datasets unsatisfying the assumption. Actually, interaction features formed by the composition of two or more features are particularly helpful to obtain the high-order information in many applications. In this paper, we propose a novel adaptive robust feature interaction selection model for regression with heavy-tailed distributions, termed Adaptive Robust Feature Interaction Selection (ARFIS). Firstly, we consider pairwise feature interaction by augmenting a feature vector with product of features for regression with heavy tailed distribution. Secondly, we propose feature interaction selection models based on quantile loss with different regularizers to learn parameters. The consistency of the proposed model ARFIS is theoretically proven, and an efficient algorithm is presented for solving proposed model. Finally, experimental results on simulation data, UCI datasets and a real-world dataset validate good accuracy, interpretability and robustness of our proposed models.

A hyper-distance-based method for hypernetwork comparison.

Hypernetwork is a useful way to depict multiple connections between nodes, making it an ideal tool for representing complex relationships in network science. In recent years, there has been a marked increase in studies on hypernetworks; however, the comparison of the difference between two hypernetworks has received less attention. This paper proposes a hyper-distance (HD)-based method for comparing hypernetworks. The method is based on higher-order information, i.e, the higher-order distance between nodes and Jensen-Shannon divergence. Experiments carried out on synthetic hypernetworks have shown that HD is capable of distinguishing between hypernetworks generated with different parameters, and it is successful in the classification of hypernetworks. Furthermore, HD outperforms current state-of-the-art baselines to distinguish empirical hypernetworks when hyperedges are randomly perturbed.

Ricci curvature based volumetric segmentation

The level set method has played a critical role among many image segmentation approaches. Several edge detectors, such as the gradient, have been applied to its regularisation term. However, traditional edge detectors lack high-order information and are sensitive to image noise. To tackle this problem, we introduce a method to calculate the Ricci curvature, a vital curvature in three-dimensional Riemannian geometry. In addition, we propose incorporating the curvature into the regularisation term. Experiments suggest that our method outperforms the state-of-the-art level set methods and achieves a comparable result with the Swin UNETR and Segment Anything.

Meta-learning on dynamic node clustering knowledge graph for cold-start recommendation

Meta-learning has been introduced in the recommendation domain, and a possible direction to extend graph-based meta-learning is how to exploit higher-order information between nodes. The current studies primarily rely on pre-embedding methods to utilize user attributes, inadvertently overlooking the crucial co-occurrence patterns among users’ features. This oversight hinders the discovery of implicit connections between users. Furthermore, in scenarios of infrequent interactions, the reliance on initial edge information constrains connections between nodes in the same category. This constraint compromises the update capability of node embeddings, leading to reduced recommendation accuracy. In this paper, a meta-learning recommendation method with a dynamic node clustering knowledge graph(DCKG) is proposed. By introducing user attribute nodes and a triple-gated attention networks, this approach facilitates connections between users based on attribute relationships and enables the balancing of information across various pathways. Additionally, the integration of a dynamic aggregation module frees nodes of the same category from initial connection constraints, fostering more direct connections and further augmenting the propagation capability of higher-order information. The experimental results show that DCKG achieves the excellent results in the cold-start recommendation. DCKG can extend the use of higher-order features in the graph by direct connection.

Explicitly Exploiting Implicit User and Item Relations in Graph Convolutional Network (GCN) for Recommendation

Most existing collaborative filtering-based recommender systems rely solely on available user–item interactions for user and item representation learning. Their performance often suffers significantly when interactions are sparse, as limited user and item interactions are insufficient for learning robust representations. To address this issue, recent research has explored additional information between users and items by leveraging the user–item bipartite graph. However, these methods have not fully exploited high-order neighborhood information, primarily using sampled interactions to enrich training data rather than integrating this information directly into representation learning. In this paper, we propose a novel model, EIR-GCN (Embedding Integration with Relational Graph Convolutional Network), which directly incorporates various types of collaborative relations, such as user–user and item–item interactions, into the embedding function for user preference modeling. Specifically, our model employs advanced graph convolutional network (GCN) techniques to integrate user–item, user–user, and item–item relations for comprehensive representation learning. EIR-GCN initially selects the most influential second-order neighbors from the user–item bipartite graph to form user–user and item–item connections. With these enriched connections, a message-passing method is adopted to learn node representations by aggregating messages from directly linked nodes, including first-order item neighbors and selected second-order user neighbors. Extensive experiments on several public datasets demonstrate that EIR-GCN outperforms strong baselines, including recent GCN-based models and those exploiting high-order information. Our results show that EIR-GCN achieves state-of-the-art performance and effectively addresses the sparsity issue, highlighting its robustness and efficacy in recommendation tasks.

Single-Frame Lagrangian Tracking Of 3-D Acoustic Streaming Flows Using Digital Defocusing Micro Particle Streak Velocimetry

In this study, a single-frame particle streak velocimetry technique is applied to the classic DDPIV(Digital Defocusing Particle Image Velocimetry) concept to achieve a single-frame 3-D Lagrangian tracking of particles in microscopic flows. The shortcoming of images suffering low signal to noise ratio due to pinholes can be avoided. With the streak resolving algorithm, it allows extended exposure time when streak images are taken, and the single-frame approach also eliminates the need for high-cost and complicated synchronization hardware for double-pulse frame-straddling light sources. For each long-exposure, color-coded images received from each pinhole equipped with red, green or blue color filters, the streak-resolving algorithm is applied first to find each streak on the separate color channel, followed by an one-time triplet-matching process to group the red, green and blue streak projections. From the streak triplets, 3-D reconstruction can be done on all the resolved points on the streak, and trajectory fitting can then be applied to resolve the tracer particle trajectories in the field of view with temporal history. This procedure was applied for visualizing a microscale 3-D acoustic streaming flow pattern induced by a longitudinal spine-shaped fin oscillating at 12kHz. The height of the channel is 1mm with the spine of 0.3mm height and a 30 。 tip angle. Resolved flow fields show that the particle streak images can be resolved with the same level of accuracy to the particle tracking method, while the throughput of velocity vectors can be significantly higher due to multiple velocity vectors can be resolved from each streak triplet. The methodology has the potential to be applied to various applications that can capture long-exposure images and possible to resolve higher-order information such as acceleration and forces applied on the particles or the flow.

DPHT-ANet: Dual-path high-order transformer-style fully attentional network for monaural speech enhancement

Dual-path Transformer-style models have demonstrated significant effectiveness in speech enhancement. However, extensive parameterization and computational complexity present challenges for practical applications. This study presents an encoder-decoder-based dual-path high-order transformer-style fully-attentional network (DPHT-ANet) to address the speech enhancement problem with a smaller parameter size and reduced computational complexity. The DPHT-ANet incorporates a high-order information interaction module and replaces the multi-head attention module with a recursive gated convolution (GnConv). This enables the DPHT-ANet to effectively capture deep-level information across time and frequency dimensions, improving its ability to capture complex temporal and spectral patterns. Furthermore, DPHT-ANet uses a unified activation and attention mechanism in the convolutional encoder-decoder layers, resulting in a fully attentional network that prioritizes relevant high-level features at earlier stages. The DPHT-ANet uses interactive feature learning and fusion of varying lengths and dimensions with pre-trained features from a large-scale dataset to further enhance its robustness. Experimental results on the VCTK+DEMAND and WSJ0-SI84 datasets demonstrate the effectiveness of the proposed approach. On the WSJ0-SI84 dataset, the DPHT-ANet significantly improves ESTOI (38.28%), PESQ (1.21), and SDR (10.83 dB) over the noisy mixture. Similarly, on the VCTK+DEMAND, the DPHT-ANet improves STOI (3.50%), PESQ (1.22), and SegSNR (9.93 dB) over the noisy mixture, showcasing superior performance in speech enhancement.

Multi-Interest Sequential Recommendation with Simplified Graph Convolution and Multiple Item Features

Multi-interest sequential recommendations leverage users’ historical behavior to provide recommendations that match multiple interests. Most of these methods have not fully extracted higher-order information hidden in users’ interactions and have overlooked the multiple features of items. To this end, this paper proposes a multi-interest model called “multi-interest sequential recommendation with simplified graph convolution and item multi-features (SGCMF)”. Firstly, a simplified graph convolution module is designed based on bipartite graphs, which utilizes mean pooling to aggregate neighboring information and employs a feedforward neural network (FNN) for nonlinear transformations and combinations. This method reduces redundant information and captures higher-order relationships, thereby simplifying the complexity of modeling high-order interactions and improving prediction accuracy. Secondly, an item multi-feature extraction module is proposed, which represents item features with multiple vectors, and analyzes each feature from multiple perspectives while preserving important relationships between features. The model correlates multiple features of the item with user interests, thereby achieving a fine-grained analysis of user interests. Extensive experiments are conducted on five real-world scenarios, and the results are compared with state-of-the-art methods. The experimental results show that SGCMF outperforms other baselines.

Super-resolution of digital rock images with hybrid attention multi-branch neural network

High-resolution digital rock images are vital in studying microstructure and fluid flow characterization of oil and gas reservoirs. Computed tomography (CT) is currently the primary tool to acquire digital rock images because of its non-destructiveness and three-dimensional imaging capability. However, the restriction of imaging capability of CT sets a compromise between image resolution and field of view (FOV). The Hybrid Attention Multi-branch Super-Resolution Network (HAMSR) proposed in this study aims to remedy this deficiency. HAMSR employs a limited discrete distribution to capture all possible high-order information and generate reliable high-resolution images. The network is built to prioritize the recovery of pores, cracks, and textures in digital rock images by introducing channel and spatial attention mechanisms. The structure together with modules of HAMSR have been designed and optimized to yield superior performance with fewer parameters and less training time. HAMSR and several contemporary advanced models are trained on the DeepRock-SR 2D dataset which contains lots of sandstone and carbonate images. It is evident that HAMSR-generated images have minimal noise, clearer edges, and more micropores. Quantitative calculations show that compared to traditional bicubic interpolation, HAMSR acquires a 2.669 dB and 1.376 dB increase in peak signal-to-noise ratio (PSNR) respectively on the test sets (27%–38% reduction in relative error). Furthermore, the high-resolution (HR) images reconstructed by HAMSR are generally consistent with real HR images concerning statistical characteristics such as porosity, pore size distribution, shape factor distribution, and two-point correlation function. This work provides a new super-resolution model to generate high-quality digital rock images for further analysis.