Graph Embedding Research Articles

Development of the Unbiased Disease Network Using Health Insurance Claims Big Data in South Korea

Abstract Background Research on the overall relationship between disease is needed. The disease network model can comprehensively show the complex relationships between diseases and progression patterns. Existing disease network studies were estimated by univariate relative risks. We aim to develop the unbiased disease network. Methods We used NHIS national data of 50 million patients, collected from 2008 to 2022. We used only the primary diagnosis codes (three-digit codes) and defined 603 disease categories based on the special tabulation list for morbidity in the detailed subcategories of ICD-10, prior research, and the frequency of diseases in the dataset. To develop a more unbiased disease network, we first adjusted for age, sex, period, season, and encounter type, then added the confounding disease exposure according to the Directed Acyclic Graph (DAG) and performed Poisson regression to estimate relative risk (RR) applying Inverse Probability Weighting (IPW). We identified statistically significant disease pairs (RR > 1.10, p < 0.001) and repeated the IPW analysis up to the third round because the network would be updated every round. The significant disease pairs identified in the last round were connected to develop the overall network and subnetworks by age and sex. Results We repeated the process up to the third round for all 346,948 disease pairs and fitted 9,024 pairs as significant. We identified general population disease network and subnetwork per age and sex subgroups. Conclusions These disease networks represented the overall progression relationship between diagnosis codes. Key messages • We developed the unbiased disease network that shows progression patterns by adjusting for confounders and using IPW. • The disease network can be used to extract features from various diagnosis codes in EHR/Claims data, analyze causal effects, and predict disease or healthcare usage using RR or graph embedding.

Face‐simple minimal quadrangulations of surfaces

AbstractFor each surface besides the sphere, projective plane, and Klein bottle, we construct a face‐simple minimal quadrangulation, that is, a simple quadrangulation on the fewest number of vertices possible, whose dual is also a simple graph. Our result answers a question of Liu, Ellingham, and Ye while providing a simpler proof of their main result. The inductive construction is based on an earlier idea for finding near‐quadrangular embeddings of the complete graphs using the diamond sum operation.

Maximizing Influence in Social Networks Using Combined Local Features and Deep Learning-Based Node Embedding.

The influence maximization problem has several issues, including low infection rates and high time complexity. Many proposed methods are not suitable for large-scale networks due to their time complexity or free parameter usage. To address these challenges, this article proposes a local heuristic called Embedding Technique for Influence Maximization (ETIM) that uses shell decomposition, graph embedding, and reduction, as well as combined local structural features. The algorithm selects candidate nodes based on their connections among network shells and topological features, reducing the search space and computational overhead. It uses a deep learning-based node embedding technique to create a multidimensional vector of candidate nodes and calculates the dependency on spreading for each node based on local topological features. Finally, influential nodes are identified using the results of the previous phases and newly defined local features. The proposed algorithm is evaluated using the independent cascade model, showing its competitiveness and ability to achieve the best performance in terms of solution quality. Compared with the collective influence global algorithm, ETIM is significantly faster and improves the infection rate by an average of 12%.

Embedding of Hypercube into Fractal Cubic Network

The implementation of parallel algorithms and the simulation of interconnection networks can be modeled into a graph embedding problem. Several cost parameters are used to assess the quality of an embedding. The wirelength is one of these factors that is frequently taken into account. A fractal cubic network is a new variant of the hypercube graph. In this study, we compute the wirelength of an embedding of the hypercube [Formula: see text] into the fractal cubic network [Formula: see text] using [Formula: see text]-partition.

Knowledge Graph Embedding for Hierarchical Entities Based on Auto-Embedding Size

Knowledge graph embedding represents entities and relations as low-dimensional continuous vectors. Recently, researchers have attempted to leverage the potential semantic connections between entities with hierarchical relationships in the knowledge graph. However, existing knowledge embedding methods that model entity hierarchical structures face the following challenges: (1) Setting the same embedding dimension for entities at different hierarchical levels can lead to overfitting issues and the waste of storage and computational resources; (2) Manually searching the embedding dimension in discrete space makes it easy to miss the optimal parameters and increases training costs. Therefore, we propose a knowledge embedding method, HEAES, that automatically learns the embedding dimensions for entities based on their hierarchical structure. Specifically, we first propose a new modeling method to capture the hierarchical relationships of entities, and we then train the model on a triple classification task. Then, we adaptively learn the pruning threshold to trim the embedding vectors, automatically learning different embedding dimensions for entities at different hierarchical levels. Experiments on the YAGO26K and DB111K datasets verify that introducing entity hierarchical information helps boost the model performance.

GeoEntity-type constrained knowledge graph embedding for predicting natural-language spatial relations

Natural-language spatial relations between geographic entities (geoentities) reflect diverse perceptions influenced by factors like location, culture, and linguistic conventions. These relations play a crucial role in supporting geospatial tasks, such as question answering and cognitive reasoning. While prior studies focused on a limited set of human-selected spatial terms and geometric attributes, they often overlooked essential semantic attributes. To overcome this limitation, we developed a Spatial Relation-based Knowledge Graph Embedding framework, SR-KGE, with new KG fusion functions to predict spatial relation terms among distinct geoentities. This method not only considers graph structures and the diversity of natural language expressions in the embedding and learning process, but also incorporates geoentity types as a constraint to capture spatial and semantic relations more accurately. Our experiments on two knowledge graph datasets, one small-scale and one large-scale, have both shown its superior performance in spatial relation inference compared to popular KGE models, including TransE, RotatE, and HAKE. We hope our research will advance the classic study of natural language described spatial relations in a more automated and intelligent way.

Improved bounds on the cop number when forbidding a minor

AbstractAndreae proved that the cop number of connected ‐minor‐free graphs is bounded for every graph . In particular, the cop number is at most if contains no isolated vertex, where . The main result of this paper is an improvement on this bound, which is most significant when is small or sparse, for instance, when can be obtained from another graph by multiple edge subdivisions. Some consequences of this result are improvements on the upper bound for the cop number of ‐minor‐free graphs, ‐minor‐free graphs and linklessly embeddable graphs.

Node classification based on Attribute Fuse Edge Features and Label Adaptive Adjustment

Most of existing graph representation learning methods only extract information from nodes and ignore the hidden information of edges. Nodes carry weak structural information thus affecting the specificity of node embeddings.To solve these problems, this paper proposes a node classification algorithm based on Attribute Fuse Edge Features and Label Adaptive Adjustment (AFEF_LAA).Firstly, Intimate-Relationship-Attribute of node is designed based on edge embeddings.Rz-Cos rule is constructed to perform the similarity metric between nodes and their neighbors to select intimate neighbor nodes.After that Reverse-TransE is constructed to encode embedding vectors of the edges connected to intimate neighborhood nodes.Secondly, a multi-fusion method based on smoothed neighborhood information is constructed.Each node in the original graph is smoothed together with its neighbor nodes.The smoothed original graph is multi-fused with multiple twin graphs. Finally, a strategy of label adaptive adjustment is proposed to select the label embedding vectors for input to the next-generation trainer by comparing accuracy.This strategy can improve the quality of graph embeddings while effectively avoiding the overfitting problem when processing high-dimensional data.AFEF_LAA is compared with the state-of-the-art algorithms on six graph datasets.Experimental results show that AFEF_LAA can achieve higher node classification accuracy.

A knowledge graph-based inspection items recommendation method for port state control inspection of LNG carriers

The safe navigation and operation of Liquified natural gas (LNG) carriers is crucial in maritime transportation. The Port State Control (PSC) inspection is the primary method for identifying deficiencies in vessels. Different from other vessels, the PSC inspection of LNG carriers requires knowledge of specialized transport equipment. Therefore, effective management of inspection knowledge and precise targeting of inspection items are particularly crucial. Knowledge graph is an efficient way to manage knowledge in the context of artificial intelligence. In this study, an LNG carrier PSC inspection knowledge graph is constructed for fusing multisource knowledge data from PSC inspection. Additionally, a knowledge graph-based recommendation model, namely the improved knowledge graph convolutional network combined with pretrained translation embedding (PT-KGCN), is developed to recommend inspection items and knowledge. The PT-KGCN model first carries out knowledge graph embedding. It then predicts possible deficiencies on the basis of historical PSC inspection data. Finally, it recommends inspection items by correlating the predicted deficiencies with knowledge from the knowledge graph. The results show that more than 87% of defective items in historical data are correctly predicted. The research findings can provide ideas for the practical application of knowledge data in the inspection fields.

A unified active learning framework for annotating graph data for regression tasks

In many domains, effectively applying machine learning models requires a large number of annotations and labelled data, which might not be available in advance. Acquiring annotations often requires significant time, effort, and computational resources, making it challenging. Active learning strategies are pivotal in addressing these challenges, particularly for diverse data types such as graphs. Although active learning has been extensively explored for node-level classification, its application to graph-level learning, especially for regression tasks, is not well-explored. We develop a unified active learning framework specializing in graph annotating and graph-level learning for regression tasks on both standard and expanded graphs, which are more detailed representations. We begin with graph collection and construction. Then, we construct various graph embeddings (unsupervised and supervised) into a latent space. Given such an embedding, the framework becomes task agnostic and active learning can be performed using any regression method and query strategy suited for regression. Within this framework, we investigate the impact of using different levels of information for active and passive learning, e.g., partially available labels and unlabelled test data. Despite our framework being domain agnostic, we validate it on a real-world application of software performance prediction, where the execution time of the source code is predicted. Thus, the graph is constructed as an intermediate source code representation. We support our methodology with a real-world dataset to underscore the applicability of our approach. Our real-world experiments reveal that satisfactory performance can be achieved by querying labels for only a small subset of all the data. A key finding is that Graph2Vec (an unsupervised embedding approach for graph data) performs the best, but only when all train and test features are used. However, Graph Neural Networks (GNNs) are the most flexible embedding techniques when used for different levels of information with and without label access. In addition, we find that the benefit of active learning increases for larger datasets (more graphs) and when the graphs are more complex, which is arguably when active learning is the most important.

HGCGE: hyperbolic graph convolutional networks-based knowledge graph embedding for link prediction

HGCGE: hyperbolic graph convolutional networks-based knowledge graph embedding for link prediction

A representation learning-based approach to enhancing manufacturing quality for low-voltage electrical products

In low-voltage electrical product manufacturing, resolving quality issues is heavily reliant on engineering experience, and can be time-consuming and error-prone. Through quality management systems, a large number of historical defect cases can be consolidated for analysis along with relevant causes. However, these defect descriptions are often casually described with a mix of Chinese and English language, containing domain-specific terms. Additionally, defect product features have varieties and complex relationships. Therefore, historical defect cases have not been effectively utilized to support manufacturing quality issues. To address this challenge, this study proposes a representation learning-based approach to enhance manufacturing quality. Key research contributions include: (1) A two-stage word embedding technique based on the pre-trained language model. First, TSDAE is utilized for unsupervised pre-training on a large amount of unlabeled data. Then, Sentence-BERT is utilized for fine-tuning on a small set of labeled similar sentence pairs. This process yields a pre-trained language model specific to low-voltage electrical product defects. (2) NSHPSAGE graph embedding model based on the constructed product feature knowledge graph. We select more valuable neighboring nodes during sampling and explore different aggregation functions to enhance graph embedding performance. This model effectively aggregates product feature information into “Defect_Case” nodes, yielding graph embedding vectors. The model exhibits good Weighted-Precision and Weighted-Recall with a short training duration, and it can handle new nodes, addressing the issue of heterogeneous graph embedding. (3) A defect case recommendation technique that fuses word embedding and graph embedding. We use Multi-Head Attention Fusion in the late-fusion to obtain defect case vectors. This approach comprehensively considers defect description semantic knowledge and complex product feature relationships, enabling accurate defect case recommendation with the prototype system.

Unlocking ETF price forecasting: Exploring the interconnections with statistical dependence-based graphs and xAI techniques

In the complex landscape of financial markets, accurately predicting Exchange-Traded Fund (ETF) price movements requires advanced methodologies. This research introduces a practical approach that integrates network analysis with graph embeddings, specifically utilizing Node2Vec, to enhance financial prediction models' performance and interpretability. By representing the intricate relationships within financial markets in a lower-dimensional space, we improve the efficiency of AI-driven predictions. A key component of our method is applying the SHAP Explainable AI (xAI) framework, which helps interpret our tree-based models' decision-making process. Using six different tree-based models, our approach delivers accurate predictions while maintaining transparency in model interpretation. This combination of graph embeddings and explainability tools enables stakeholders to understand better the factors influencing financial market behavior, improving decision-making based on AI models.

Multi-level discriminator based contrastive learning for multiplex networks

Graph embedding is a technique for obtaining low-dimensional representations of nodes across diverse networks, which may then be used for various downstream tasks and applications. When it applies to heterogeneous networks, it is hard to handle heterogeneous networks because they usually contain different types of nodes and edges with more semantic and structural information. Recently, contrastive learning has developed as the preferred strategy for dealing with unsupervised heterogeneous graph embedding to reduce the cost of human label annotation. However, most multi-view contrastive learning approaches calculate the model’s loss only based on the mutual dependence between the node representation and graph representation. These approaches ignore that both node attributes and node clustering contain discriminative content. To solve this issue, we propose a model called Multi-Level Discriminator-based Contrastive Learning for Multiplex Networks (MLDCL). This model adopts a multi-level multi-discriminator-based approach that can simultaneously learn the global-level structural information, node-level attribute information, and local-level clustering information. Moreover, an augmentation strategy in the contrast learning process from the spectral domain is proposed to improve the representation and discriminative ability of MLDCL. Numerous tests with node clustering and classification tasks on widely used datasets demonstrate the efficacy of the proposed approach.

An adaptive imbalance robust graph embedding broad learning system fault diagnosis for imbalanced batch processes data

Modern batch processes develop toward higher levels, and devices usually work at normal conditions with generally very few faults. Therefore, fewer fault data are collected than normal data, which makes the normal and fault modes imbalanced, and the fault diagnosis model is incapable focusing on the minority of fault samples as much as on most normal samples, consequently leading to insufficient generalization ability for the model. For that, we propose an adaptive imbalance-robust graph embedding broad learning system (AI-RGEBLS) in this paper. Firstly, it achieves adaptive correction of unbalanced samples by niche technique and synthetic minority over-sampling technique (SMOTE) with improved Mahalanobis distance. Then, broad learning system (BLS) is regularized by graph embedding, and further introduced the L2,1 norm constraint, which makes it possible to enhance the robustness of the model while considering the local manifold information of the data in the feature extraction process. Finally, the incremental learning approach is applied to the model to avoid the disastrous forgetting problem caused by training the whole model from zero. The effectiveness of the proposed method is verified by penicillin fermentation process and semiconductor etching process. It can effectively improve the speed of model training and provide better fault diagnosis for imbalanced batch processes compared with the existing methods.

PAGE: Prototype-Based Model-Level Explanations for Graph Neural Networks.

Aside from graph neural networks (GNNs) attracting significant attention as a powerful framework revolutionizing graph representation learning, there has been an increasing demand for explaining GNN models. Although various explanation methods for GNNs have been developed, most studies have focused on instance-level explanations, which produce explanations tailored to a given graph instance. In our study, we propose Prototype-bAsed GNN-Explainer ([Formula: see text]), a novel model-level GNN explanation method that explains what the underlying GNN model has learned for graph classification by discovering human-interpretable prototype graphs. Our method produces explanations for a given class, thus being capable of offering more concise and comprehensive explanations than those of instance-level explanations. First, [Formula: see text] selects embeddings of class-discriminative input graphs on the graph-level embedding space after clustering them. Then, [Formula: see text] discovers a common subgraph pattern by iteratively searching for high matching node tuples using node-level embeddings via a prototype scoring function, thereby yielding a prototype graph as our explanation. Using six graph classification datasets, we demonstrate that [Formula: see text] qualitatively and quantitatively outperforms the state-of-the-art model-level explanation method. We also carry out systematic experimental studies by demonstrating the relationship between [Formula: see text] and instance-level explanation methods, the robustness of [Formula: see text] to input data scarce environments, and the computational efficiency of the proposed prototype scoring function in [Formula: see text].

MultiplexSAGE: A Multiplex Embedding Algorithm for Inter-Layer Link Prediction.

Research on graph representation learning has received great attention in recent years. However, most of the studies so far have focused on the embedding of single-layer graphs. The few studies dealing with the problem of representation learning of multilayer structures rely on the strong hypothesis that the inter-layer links are known, and this limits the range of possible applications. Here we propose MultiplexSAGE, a generalization of the GraphSAGE algorithm that allows embedding multiplex networks. We show that MultiplexSAGE is capable to reconstruct both the intra-layer and the inter-layer connectivity, outperforming competing methods. Next, through a comprehensive experimental analysis, we shed light also on the performance of the embedding, both in simple and multiplex networks, showing that both the density of the graph and the randomness of the links strongly influences the quality of the embedding.

Dual De-confounded Causal Intervention method for knowledge graph error detection

Due to the Knowledge Graph (KG) construction process, erroneous triples are virtually inevitable to be introduced into real-world KGs. Since these errors hinder the expressiveness and applicability of KGs, the development of knowledge graph error detection (KGED) methods is necessary. Despite the overall effectiveness of current KGED methods, their capacity to identify challenging errors is limited. In this work, we conduct empirical studies and find that previous works introduce structural and semantic bias, impeding the identification of erroneous triples, especially in challenging cases. To address this issue, we design a causal graph for the KGED task and propose a Dual De-confounded Causal Intervention (DuDCI) method for debiasing. Firstly, DuDCI utilizes the neighborhood and textual descriptions of triples to calculate their graph and text embeddings. Next, a Causal De-confounded Module is constructed to mitigate the impact of shortcuts caused by the bias through the front-door adjustment. Furthermore, we introduce Disentanglement Constraints to disentangle the information expressed by each embedding, thereby facilitating further bias mitigation. Experimental results on three widely used KGED datasets validate the effectiveness of DuDCI and demonstrate that DuDCI outperforms current KGED methods, with an improvement of at least 2.2%, especially in more challenging noise scenarios.

Motif Graph Neural Network.

Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, the existing motif-based GNNs still often suffer from less discriminative power on high-order structures. To overcome the above limitations, we propose motif GNN (MGNN), a novel framework to better capture high-order structures, hinging on our proposed motif redundancy minimization operator and injective motif combination. First, MGNN produces a set of node representations with respect to each motif. The next phase is our proposed redundancy minimization among motifs which compares the motifs with each other and distills the features unique to each motif. Finally, MGNN performs the updating of node representations by combining multiple representations from different motifs. In particular, to enhance the discriminative power, MGNN uses an injective function to combine the representations with respect to different motifs. We further show that our proposed architecture increases the expressive power of GNNs with a theoretical analysis. We demonstrate that MGNN outperforms state-of-the-art methods on seven public benchmarks on both the node classification and graph classification tasks.

Dual semi-supervised hypergraph regular multi-view NMF with anchor graph embedding

Graph regularized nonnegative matrix factorization (GNMF) has been widely used in multi-view clustering tasks due to its good clustering properties. However, it uses a simple graph to describe the complex data relationship of multiple views and tries to obtain a consistent low-dimensional representation, which undoubtedly brings challenges to its clustering performance. In addition, clustering a large amount of high-dimensional data from multiple views undoubtedly faces a huge computational burden. In order to effectively improve the performance and efficiency of GNMF based multi-view clustering algorithm, this paper proposes a dual semi-supervised hypergraph regular multi-view clustering method with anchor graph embedding (DSSHMNMFAE). Specifically, DSSHMNMFAE develops a new anchor selection method to generate anchors and the anchor bipartite graph is constructed to embed the matrix factorization process. DSSHMNMFAE constructs a hypergraph to effectively learn the high-order relationship between data from multiple views. In order to perform semi-supervised learning more efficiently, DSSHMNMFAE integrates pairwise constraint information and label constraint information into the clustering process as dual label information. In addition, DSSHMNMFAE considers the learning of both consistency information and complementarity information, and adopts adaptive measures to distinguish the contributions of different views. We use the alternating iterative algorithm to optimize the objective function of DSSHMNMFAE. The experimental results on eight real datasets show that the performance of DSSHMNMFAE is comparable to other algorithms.