Explore the vast range of titles available.

Regarding the software development, MDA (Model Driven Architecture) of OMG can be regarded as the concept of making an independently-designed model according to the development environment and language and reusing it according to the desired development environment and language by expanding the reusable unit into the software model when developing software. The problem with these traditional research methods, but the first model, design model for checking the information with the model by defining a formal representation in the form of an abstract syntax tree, as you’ve shown how to perform validation of UML design model. Additional steps need to define more complex due to a software problem that is not the way to the model suitable for model transformation verification. In this paper, as defined in the verification based meta model for input and target model. And we also suggest how to perform model transformation verification using property matching based transformation similarity and graph comparison algorithm. This paper proposes model transformation verification using verification meta information and transformation similarity by property matching. In addition, in order to support verification of the target model generated from the source model, we define verification meta model for UML model, RDBMS model and RT-UML model. Recent researches from model-based architecture did partial tests focusing on phrase-correctness about the re-use in the perspective of converted software model. To overcome such limitations, this study suggests the ways to define transformation profiles using property information of system structure models as the test-based meta-model and transformation rules, improve graph comparison algorithm, and even supports the correctness of meanings. There were problems in existing methods of model transformation verification such as graph comparison or the one considering only syntax-correctness through pattern-matching. To remedy such problems, this study suggests a new verification method by defining the meta-model which has additional structural attributes and property information and the transformation profile, and using graph comparison algorithm which checks whether the information acquired from transformation is correct.

Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work.

Graph theoretical approach has proved an effective tool to understand, characterize, and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths, and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a “network of networks” that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications.

Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect. However, in practical recommendation scenarios, user-item interaction data is often sparse and exhibits a skewed distribution. To address these issues, some contrastive learning methods based on data augmentation are applied to recommender systems to enhance the representation of users and items. Nevertheless, many data enhancements solely rely on graph topology, missing crucial structural information and potentially biasing the model. In this paper, we propose a contrastive learning recommendation framework called GDA-GCL based on a generative model data augmentation strategy. Specifically, we use the Conditional Variational Autoencoder(CVAE) generative model to learn the distribution of neighbor node features conditioned on the features of the central node. Due to the randomness of resampling, we design a mirror graph comparison strategy to generate different comparison views, which introduces additional high-quality training signals into the GNN paradigm. Experimental results on three real-world public datasets demonstrate that GDA-GCL achieves significant improvement in performance over various baseline methods. Extensive analysis, including ablation studies, has demonstrated the effectiveness and robustness of our proposed generative data-augmented contrastive recommendation framework in addressing the data sparsity issue in recommendation systems.

Graph comparison is a certain type of condition on the metric space encoded by a finite graph. We show that each nontrivial graph comparison implies one of Alexandrov’s comparisons. The proof gives a complete description of graphs with trivial graph comparisons.

The comparison of multiple (labeled) graphs with unrelated vertex sets is an important task in diverse areas of applications. Conceptually, it is often closely related to multiple sequence alignments since one aims to determine a correspondence, or more precisely, a multipartite matching between the vertex sets. There, the goal is to match vertices that are similar in terms of labels and local neighborhoods. Alignments of sequences and ordered forests, however, have a second aspect that does not seem to be considered for graph comparison, namely the idea that an alignment is a superobject from which the constituent input objects can be recovered faithfully as well-defined projections. Progressive alignment algorithms are based on the idea of computing multiple alignments as a pairwise alignment of the alignments of two disjoint subsets of the input objects. Our formal framework guarantees that alignments have compositional properties that make alignments of alignments well-defined. The various similarity-based graph matching constructions do not share this property and solve substantially different optimization problems. We demonstrate that optimal multiple graph alignments can be approximated well by means of progressive alignment schemes. The solution of the pairwise alignment problem is reduced formally to computing maximal common induced subgraphs. Similar to the ambiguities arising from consecutive indels, pairwise alignments of graph alignments require the consideration of ambiguous edges that may appear between alignment columns with complementary gap patterns. We report a simple reference implementation in Python/NetworkX intended to serve as starting point for further developments. The computational feasibility of our approach is demonstrated on test sets of small graphs that mimimc in particular applications to molecular graphs.

In this paper, we propose a novel loop closure detection algorithm that uses graph attention neural networks to encode semantic graphs to perform place recognition and then use semantic registration to estimate the 6 DoF relative pose constraint. Our place recognition algorithm has two key modules, namely, a semantic graph encoder module and a graph comparison module. The semantic graph encoder employs graph attention networks to efficiently encode spatial, semantic and geometric information from the semantic graph of the input point cloud. We then use self-attention mechanism in both node-embedding and graph-embedding steps to create distinctive graph vectors. The graph vectors of the current scan and a keyframe scan are then compared in the graph comparison module to identify a possible loop closure. Specifically, employing the difference of the two graph vectors showed a significant improvement in performance, as shown in ablation studies. Lastly, we implemented a semantic registration algorithm that takes in loop closure candidate scans and estimates the relative 6 DoF pose constraint for the LiDAR SLAM system. Extensive evaluation on public datasets shows that our model is more accurate and robust, achieving 13% improvement in maximum F1 score on the SemanticKITTI dataset, when compared to the baseline semantic graph algorithm. For the benefit of the community, we open-source the complete implementation of our proposed algorithm and custom implementation of semantic registration at https://github.com/crepuscularlight/SemanticLoopClosure .

The graph traversal edit distance (GTED), introduced by Ebrahimpour Boroojeny et al. (2018), is an elegant distance measure defined as the minimum edit distance between strings reconstructed from Eulerian trails in two edge-labeled graphs. GTED can be used to infer evolutionary relationships between species by comparing de Bruijn graphs directly without the computationally costly and error-prone process of genome assembly. Ebrahimpour Boroojeny et al. (2018) propose two ILP formulations for GTED and claim that GTED is polynomially solvable because the linear programming relaxation of one of the ILPs always yields optimal integer solutions. The claim that GTED is polynomially solvable is contradictory to the complexity results of existing string-to-graph matching problems. We resolve this conflict in complexity results by proving that GTED is NP-complete and showing that the ILPs proposed by Ebrahimpour Boroojeny et al. do not solve GTED but instead solve for a lower bound of GTED and are not solvable in polynomial time. In addition, we provide the first two, correct ILP formulations of GTED and evaluate their empirical efficiency. These results provide solid algorithmic foundations for comparing genome graphs and point to the direction of heuristics. The source code to reproduce experimental results is available at https://github.com/Kingsford-Group/gtednewilp/ .

Content generation that is both relevant and up to date with the current threats of the target audience is a critical element in the success of any cyber security exercise (CSE). Through this work, we explore the results of applying machine learning techniques to unstructured information sources to generate structured CSE content. The corpus of our work is a large dataset of publicly available cyber security articles that have been used to predict future threats and to form the skeleton for new exercise scenarios. Machine learning techniques, like named entity recognition and topic extraction, have been utilised to structure the information based on a novel ontology we developed, named Cyber Exercise Scenario Ontology (CESO). Moreover, we used clustering with outliers to classify the generated extracted data into objects of our ontology. Graph comparison methodologies were used to match generated scenario fragments to known threat actors’ tactics and help enrich the proposed scenario accordingly with the help of synthetic text generators. CESO has also been chosen as the prominent way to express both fragments and the final proposed scenario content by our AI-assisted Cyber Exercise Framework. Our methodology was assessed by providing a set of generated scenarios for evaluation to a group of experts to be used as part of a real-world awareness tabletop exercise.

The existing learning resource recommendation systems suffer from data sparsity and missing data labels, leading to the insufficient mining of the correlation between users and courses. To address these issues, we propose a learning resource recommendation method based on graph contrastive learning, which uses graph contrastive learning to construct an auxiliary recommendation task combined with a main recommendation task, achieving the joint recommendation of learning resources. Firstly, the interaction bipartite graph between the user and the course is input into a lightweight graph convolutional network, and the embedded representation of each node in the graph is obtained after compilation. Then, for the input user–course interaction bipartite graph, noise vectors are randomly added to each node in the embedding space to perturb the embedding of graph encoder node, forming a perturbation embedding representation of the node to enhance the data. Subsequently, the graph contrastive learning method is used to construct auxiliary recommendation tasks. Finally, the main task of recommendation supervision and the constructed auxiliary task of graph contrastive learning are jointly learned to alleviate data sparsity. The experimental results show that the proposed method in this paper has improved the Recall@5 by 5.7% and 11.2% and the NDCG@5 by 0.1% and 6.4%, respectively, on the MOOCCube and Amazon-Book datasets compared with the node enhancement methods. Therefore, the proposed method can significantly improve the mining level of users and courses by using a graph comparison method in the auxiliary recommendation task and has better noise immunity and robustness.