Graph Construction Research Articles

Anonymous group structure algorithm based on community structure.

A social network is a platform that users can share data through the internet. With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. To mitigate this problem, an anonymous group structure algorithm based on community structure is proposed in this article. At first, a privacy protection scheme model is designed, which can be adjusted dynamically according to the network size and user demand. Secondly, based on the community characteristics, the concept of fuzzy subordinate degree is introduced, then three kinds of community structure mining algorithms are designed: the fuzzy subordinate degree-based algorithm, the improved Kernighan-Lin algorithm, and the enhanced label propagation algorithm. At last, according to the level of privacy, different anonymous graph construction algorithms based on community structure are designed. Furthermore, the simulation experiments show that the three methods of community division can divide the network community effectively. They can be utilized at different privacy levels. In addition, the scheme can satisfy the privacy requirement with minor changes.

L-FNNG: Accelerating Large-Scale KNN Graph Construction on CPU-FPGA Heterogeneous Platform

Due to the high complexity of constructing exact k -nearest neighbor graphs, approximate construction has become a popular research topic. The NN-Descent algorithm is one of the representative in-memory algorithms. To effectively handle large datasets, existing state-of-the-art solutions combine the divide-and-conquer approach and the NN-Descent algorithm, where large datasets are divided into multiple partitions, and a subgraph is constructed for each partition before all the subgraphs are merged, reducing the memory pressure significantly. However, such solutions fail to address inefficiencies in large-scale k -nearest neighbor graph construction. In this paper, we propose L-FNNG, a novel solution for accelerating large-scale k -nearest neighbor graph construction on CPU-FPGA heterogeneous platform. The CPU is responsible for dividing data and determining the order of partition processing, while the FPGA executes all construction tasks to utilize the acceleration capability fully. To accelerate the execution of construction tasks, we design an efficient FPGA accelerator, which includes the Block-based Scheduling (BS) and Useless Computation Aborting (UCA) techniques to address the problems of memory access and computation in the NN-Descent algorithm. We also propose an efficient scheduling strategy that includes a KD-tree-based data partitioning method and a hierarchical processing method to address scheduling inefficiency. We evaluate L-FNNG on a Xilinx Alveo U280 board hosted by a 64-core Xeon server. On multiple large-scale datasets, L-FNNG achieves, on average, 2.3× construction speedup over the state-of-the-art GPU-based solution.

HyperCLR: A Personalized Sequential Recommendation Algorithm Based on Hypergraph and Contrastive Learning

Sequential recommendations aim to predict users’ next interactions by modeling their interaction sequences. Most existing work concentrates on user preferences within these sequences, overlooking the complex item relationships across sequences. Additionally, these studies often fail to address the diversity of user interests, thus not capturing their varied latent preferences effectively. To tackle these problems, this paper develops a novel recommendation algorithm based on hypergraphs and contrastive learning named HyperCLR. It dynamically incorporates the time and location embeddings of items to model high-order relationships in user preferences. Moreover, we developed a graph construction approach named IFDG, which utilizes global item visit frequencies and spatial distances to discern item relevancy. By sampling subgraphs from IFDG, HyperCLR can align the representations of identical interaction sequences closely while distinguishing them from the broader global context on IFDG. This approach enhances the accuracy of sequential recommendations. Furthermore, a gating mechanism is designed to tailor the global context information to individual user preferences. Extensive experiments on Taobao, Books and Games datasets have shown that HyperCLR consistently surpasses baselines, demonstrating the effectiveness of the method. In particular, in comparison to the best baseline methods, HyperCLR demonstrated a 29.1% improvement in performance on the Taobao dataset.

Multi-sensor temporal-spatial graph network fusion empirical mode decomposition convolution for machine fault diagnosis

Multi-sensor time-series data at different locations contains not only temporal correlation information but also spatial correlation information which is treasure for machine fault diagnosis. Existing graph construction methods mainly apply different data analysis methods to connect nodes and edges. Few works, however, consider the location of the sensor itself and temporal correlation information of multi-sensor time-series data. To mine the relationship between spatial information and temporal information, the multi-sensor temporal-spatial graph is constructed in this paper. Hereinto, the different data points of multi-sensor are severed as different nodes which represents the spatial feature information. The temporal information is contained between different nodes of the same sensor. Moreover, an empirical mode decomposition graph convolution network (EGCN) is proposed to extract the feature. Specifically, the traditional graph convolution operator is changed to empirical mode decomposition which can decompose the input features into multiple intrinsic modal features to achieve adaptive feature extraction and improve the representation capability of the network. Finally, the different fault types can be classified by fully connected layers. Experiments from different test rigs demonstrate that the proposed method achieves a diagnostic accuracy exceeding 99 % under limited fault samples.

Construction of Cultural Heritage Knowledge Graph Based on Graph Attention Neural Network

To address the challenges posed by the vast and complex knowledge information in cultural heritage design, such as low knowledge retrieval efficiency and limited visualization, this study proposes a method for knowledge extraction and knowledge graph construction based on graph attention neural networks (GAT). Using Tang Dynasty gold and silver artifacts as samples, we establish a joint knowledge extraction model based on GAT. The model employs the BERT pretraining model to encode collected textual knowledge data, conducts sentence dependency analysis, and utilizes GAT to allocate weights among entities, thereby enhancing the identification of target entities and their relationships. Comparative experiments on public datasets demonstrate that this model significantly outperforms baseline models in extraction effectiveness. Finally, the proposed method is applied to the construction of a knowledge graph for Tang Dynasty gold and silver artifacts. Taking the Gilded Musician Pattern Silver Cup as an example, this method provides designers with a visualized and interconnected knowledge collection structure.

Co-Clustering by Directly Solving Bipartite Spectral Graph Partitioning.

Bipartite spectral graph partitioning (BSGP) method as a co-clustering method, has been widely used in document clustering, which simultaneously clusters documents and words by making full use of the duality between documents and words. It consists of two steps: 1) graph construction and 2) singular value decomposition on the bipartite graph to compute a continuous cluster assignment matrix, followed by post-processing to get the discrete solution. However, the generated graph is unstructured and fixed. It heavily relies on the quality of the graph construction. Moreover, the two-stage process may deviate from directly solving the primal problem. In order to tackle these defects, a novel bipartite graph partitioning method is proposed to learn a bipartite graph with exactly c connected components (c is the number of clusters), which can obtain clustering results directly. Furthermore, it is experimentally and theoretically proved that the solution of the proposed model is the discrete solution of the primal BSGP problem for a special situation. Experimental results on synthetic and real-world datasets exhibit the superiority of the proposed method.

Pretrainable geometric graph neural network for antibody affinity maturation

Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC50 values of the designed antibody mutants are decreased by up to 17 fold, and KD values by up to 6.1 fold. These promising results underscore the utility of geometric deep learning and effective pretraining in macromolecule interaction modeling tasks.

Secure equitable subdivision number of graphs

A dominating set S of a graph G(V, E) is called a secure dominating set, if for each vertex x belongs to V(G) \ S there exists at least one vertex y in S such that y belongs to the neighbourhood of x and a vertex y can be replaced by a vertex x without losing domination property. A secure dominating set S is a secure equitable dominating set if for each x in V(G) \ S and the vertex y in S, which replaces x, we have |deg(x)-deg(y)| is less than or equal to 1. In this paper, we initiate the study of a new domination parameter, the secure equitable subdivision number of graphs. Also, we introduce the definitions of the secure equitable subdivision critical graphs and the secure equitable subdivision stable graphs. Further, we study the construction of a secure equitable subdivision critical graph.

Homophily-Enhanced Self-Supervision for Graph Structure Learning: Insights and Directions.

Graph neural networks (GNNs) have recently achieved remarkable success on a variety of graph-related tasks, while such success relies heavily on a given graph structure that may not always be available in real-world applications. To address this problem, graph structure learning (GSL) is emerging as a promising research topic where task-specific graph structure and GNN parameters are jointly learned in an end-to-end unified framework. Despite their great progress, existing approaches mostly focus on the design of similarity metrics or graph construction, but directly default to adopting downstream objectives as supervision, which lacks deep insight into the power of supervision signals. More importantly, these approaches struggle to explain how GSL helps GNNs, and when and why this help fails. In this article, we conduct a systematic experimental evaluation to reveal that GSL and GNNs enjoy consistent optimization goals in terms of improving the graph homophily. Furthermore, we demonstrate theoretically and experimentally that task-specific downstream supervision may be insufficient to support the learning of both graph structure and GNN parameters, especially when the labeled data are extremely limited. Therefore, as a complement to downstream supervision, we propose homophily-enhanced self-supervision for GSL (HES-GSL), a method that provides more supervision for learning an underlying graph structure. A comprehensive experimental study demonstrates that HES-GSL scales well to various datasets and outperforms other leading methods. Our code will be available in https://github.com/LirongWu/Homophily-Enhanced-Self-supervision.

Fast adaptively balanced min-cut clustering

Min-cut clustering is a typical graph clustering method, which has been extensively applied for pattern recognition, data analysis and image processing. However, min-cut has trivial solutions, which leads to skewed clustering performance. Spectral clustering (SC) alleviates this by relaxing indicator matrix into continuous embedding and then discretizes the embedding. However, there are two primary challenges in SC, which are high computational complexity and two-stage process. To resolve these issues, a fast adaptively balanced min-cut clustering model (FBMC) is proposed, which directly solves the discrete indicator matrix without any post-processing. We utilize the bipartite graph to accelerate the construction of affinity graph and process of optimization. Besides, the balance factors are added in the model, which could alleviate skewed clustering results. Two specific methods are proposed, in which one adds a balance factor to all clusters while the other assigns balance factors to each cluster separately, referred to as FBMC1 and FBMC2. What is more, a one-step optimization is proposed to solve FBMC1 and FBMC2, the complexity of which is linear of time. Finally, comprehensive experiments results highlight the superior performance of our proposed method.

Ontology-Driven Knowledge Graph Construction in the Mathematics Semantic Library

Ontology-Driven Knowledge Graph Construction in the Mathematics Semantic Library

Graph-based insider threat detection: A survey

Insider threat detection has been a significant topic in recent years. However, as network technology develops, the intranet becomes more complex. Therefore, simply matching attack patterns or using traditional machine learning methods (Logistic Regression, Gaussian-NB, Random Forest, etc.) does not work well. On the other hand, the graph structure can better adapt to intranet data, thus graph-based insider threat detection methods have become mainstream. In order to study the design and effectiveness of graph-based insider threat detection, in this paper, we conduct a systematic and comprehensive survey of existing related research. Specifically, we provide a framework and a taxonomy based on the detection process, classifying existing work from three aspects: data collection, graph construction, and graph anomaly detection. We conduct a quantitative analysis of existing representative graph methods and find that the models with more information have better performance. In particular, we discuss the scalability of existing methods to large-scale networks and their feasibility in real environments. Based on the survey results, we propose 7 pain points in this field and provide specific future research directions. Our survey will provide future researchers with a complete solution.

GCN-Transformer-Based Spatio-Temporal Load Forecasting for EV Battery Swapping Stations under Differential Couplings

To address the challenge of power absorption in grids with high renewable energy integration, electric vehicle battery swapping stations (EVBSSs) serve as critically important flexible resources. Current research on load forecasting for EVBSSs primarily employs Transformer models, which have increasingly shown a lack of adaptability to the rapid growth in scale and complexity. This paper proposes a novel data-driven forecasting model that combines the geographical feature extraction capability of graph convolutional networks (GCNs) with the multitask learning capability of Transformers. The GCN-Transformer model first leverages Spearman’s rank correlation to create a multinode feature set encompassing date, weather, and historical load data. It then employs data-adaptive graph generation for dynamic spatio-temporal graph construction and graph convolutional layers for spatial aggregation tailored to each node. Unique swapping patterns are identified through node-adaptive parameter learning, while the temporal dynamics of multidimensional features are managed by the Transformer’s components. Numerical results demonstrate enhanced accuracy and efficiency in load forecasting for multiple and widely distributed EVBSSs.

Overview of knowledge graph construction in the field of fully electronic computer interlocking systems

As a core technology in railway signal control, the fully electronic computer interlocking system achieves the control of railway signals and switches through computerization, ensuring the safety and efficiency of train operations. With the increasing complexity of railway systems, traditional management and maintenance face significant challenges. Knowledge graphs, as an advanced data management and representation method, can effectively organize and utilize large-scale domain knowledge, enhancing the system's intelligence. This paper introduces the construction methods, core concepts, and applications of knowledge graphs in the field of fully electronic computer interlocking systems, providing references for research and practice in this area.

ProcSAGE: an efficient host threat detection method based on graph representation learning

Advanced Persistent Threats (APTs) achieves internal networks penetration through multiple methods, making it difficult to detect attack clues solely through boundary defense measures. To address this challenge, some research has proposed threat detection methods based on provenance graphs, which leverage entity relationships such as processes, files, and sockets found in host audit logs. However, these methods are generally inefficient, especially when faced with massive audit logs and the computational resource-intensive nature of graph algorithms. Effectively and economically extracting APT attack clues from massive system audit logs remains a significant challenge. To tackle this problem, this paper introduces the ProcSAGE method, which detects threats based on abnormal behavior patterns, offering high accuracy, low cost, and independence from expert knowledge. ProcSAGE focuses on processes or threads in host audit logs during the graph construction phase to effectively control the scale of provenance graphs and reduce performance overhead. Additionally, in the feature extraction phase, ProcSAGE considers information about the processes or threads themselves and their neighboring nodes to accurately characterize them and enhance model accuracy. In order to verify the effectiveness of the ProcSAGE method, this study conducted a comprehensive evaluation on the StreamSpot dataset. The experimental results show that the ProcSAGE method can significantly reduce the time and memory consumption in the threat detection process while improving the accuracy, and the optimization effect becomes more significant as the data size expands.

StaRS: Learning a Stable Representation Space for Continual Relation Classification.

Relation classification (RC) aims to detect the semantic relation between two annotated entities in a piece of sentence, serving as an essential task in automatic knowledge graph construction. Due to the emergence of new relations, there is a recent trend to train RC models in continual settings. To overcome the catastrophic forgetting problem in continual learning, existing research is devoted in a two-stage training paradigm, fast adaptation to novel relations, and memory replay for all historical relations. These memory-replay-based methods explore different techniques to mitigate the forgetting problem of continual RC (CRC) models during the memory replay stage. However, we find that the representation space undergoes distortion due to the incoming of fresh relations in the fast adaptation phase. To address this issue, we propose using a knowledge distillation strategy and designing a margin loss, aiming to maintain the stability of the RC model during adaptation to new relations. In addition, in the second stage, with a limited number of typical memory instances available, we introduce a self-contrastive learning objective to facilitate learning a balanced decision boundary for RC. Through training in two stages, our objective is to acquire a stable representation space to encode instances for CRC. We experimentally demonstrate the superiority of our model over competing methods in various settings, and the results suggest that our tailored designs can achieve better performance in CRC.

Construction of Knowledge Graphs: Current State and Challenges

Construction of Knowledge Graphs: Current State and Challenges

Graph Transformer for 3D point clouds classification and semantic segmentation

Recently, graph-based and Transformer-based deep learning have demonstrated excellent performances on various point cloud tasks. Most of the existing graph-based methods rely on static graph, which take a fixed input to establish graph relations. Moreover, many graph-based methods apply maximizing and averaging to aggregate neighboring features, so that only a single neighboring point affects the feature of centroid or different neighboring points own the same influence on the centroid’s feature, which ignoring the correlation and difference between points. Most Transformer-based approaches extract point cloud features based on global attention and lack the feature learning on local neighbors. To solve the above issues of graph-based and Transformer-based models, we propose a new feature extraction block named Graph Transformer and construct a 3D point cloud learning network called GTNet to learn features of point clouds on local and global patterns. Graph Transformer integrates the advantages of graph-based and Transformer-based methods, and consists of Local Transformer that use intra-domain cross-attention and Global Transformer that use global self-attention. Finally, we use GTNet for shape classification, part segmentation and semantic segmentation tasks in this paper. The experimental results show that our model achieves good learning and prediction ability on most tasks. The source code and pre-trained model of GTNet will be released on https://github.com/NWUzhouwei/GTNet.

Adversarial Entity Graph Convolutional Networks for multi-hop inference question answering

Multi-hop reasoning is critical for natural language understanding but poses challenges for current models, requiring models capable of aggregating and reasoning across multiple sources of information. We propose an Adversarial Entity Graph Convolutional Network (AEGCN) to improve multi-hop inference performance. Unlike previous GNNs-based models, AEGCN places a greater emphasis on the construction of rich entity graph which focuses on identifying the related entities from support document and connecting these entities with innovative edge relationships. The entity graph built by AEGCN preserves both the semantic information and structure of the original text. Further, adversarial training is adopted to generate challenging embeddings for the entity graph, increasing the model’s robustness against the interference. The experiments evaluated on WIKIHOP and MEDHOP dataset indicate that AEGCN achieves 6.8 % and 7.7 % accuracy improvement over baseline model respectively, confirming the model’s advanced capability in multi-hop reasoning tasks.

MAC: Maximal Cliques for 3D Registration.

This paper presents a 3D registration method with maximal cliques (MAC) for 3D point cloud registration (PCR). The key insight is to loosen the previous maximum clique constraint and mine more local consensus information in a graph for accurate pose hypotheses generation: 1) A compatibility graph is constructed to render the affinity relationship between initial correspondences. 2) We search for maximal cliques in the graph, each representing a consensus set. 3) Transformation hypotheses are computed for the selected cliques by the SVD algorithm and the best hypothesis is used to perform registration. In addition, we present a variant of MAC if given overlap prior, called MAC-OP. Overlap prior further enhances MAC from many technical aspects, such as graph construction with re-weighted nodes, hypotheses generation from cliques with additional constraints, and hypothesis evaluation with overlap-aware weights. Extensive experiments demonstrate that both MAC and MAC-OP effectively increase registration recall, outperform various state-of-the-art methods, and boost the performance of deep-learned methods. For instance, MAC combined with GeoTransformer achieves a state-of-the-art registration recall of 95.7% / 78.9% on 3DMatch / 3DLoMatch. We perform synthetic experiments on 3DMatch-LIR / 3DLoMatch-LIR, a dataset with extremely low inlier ratios for 3D registration in ultra-challenging cases. Code will be available at: https://github.com/zhangxy0517/3D-Registration-with-Maximal-Cliques.