Attributed Graphs Research Articles

Managing data of sensor-equipped transportation networks using graph databases

Abstract. In this paper, we are concerned with data pertinent to transportation networks, which model situations in which objects move along a graph-like structure. We assume that these networks are equipped with sensors that monitor the network and the objects moving along it. These sensors produce time series data, resulting in sensor networks. Examples are river, road, and electricity networks. Geographical information systems are used to gather, store, and analyse data, and we focus on these tasks in the context of data emerging from transportation networks equipped with sensors. While tailored solutions exist for many contexts, they are limited for sensor-equipped networks at this moment. We view time series data as temporal properties of the network and approach the problem from the viewpoint of property graphs. In this paper, we adapt and extend the theory of the existing property graph databases to model spatial networks, where nodes and edges can contain temporal properties that are time series data originating from the sensors. We propose a language for querying these property graphs with time series, in which time series and measurement patterns may be combined with graph patterns to describe, retrieve, and analyse real-life situations. We demonstrate the model and language in practice by implementing both in Neo4j and explore questions hydrology researchers pose in the context of the Internet of Water, including salinity analysis in the Yser river basin.

Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving

We present the Process Engineering Operations Assistant (PEOA), an AI-driven framework designed to solve complex problems in the chemical and process industries. The framework employs a modular architecture orchestrated by a meta-agent, which serves as the central coordinator, managing an action generator and instruction-tuned small-scale language models (expert models). The action generator decomposes complex problems into sub-tasks and identifies suitable expert models to execute each, delivering precise solutions for multi-step problem-solving. Key techniques include advanced knowledge modeling using property graphs for improved information retrieval, facilitating more accurate and contextually relevant solutions. Additionally, the framework utilizes a teacher-student transfer-learning approach with GPT-4 (Omni) to fine-tune the action generator and expert models for domain adaptation, alongside an iterative problem-solving mechanism with sophisticated error handling. Custom datasets were developed to evaluate the framework against leading proprietary language models on various engineering tasks. The results demonstrate the framework’s effectiveness in automating calculations, accelerating prototyping, and providing AI-augmented decision support for industrial processes, marking a significant advancement in process engineering capabilities.

Identifying Local Useful Information for Attribute Graph Anomaly Detection

Identifying Local Useful Information for Attribute Graph Anomaly Detection

Vul-LMGNNs: Fusing language models and online-distilled graph neural networks for code vulnerability detection

Code Language Models (codeLMs) and Graph Neural Networks (GNNs) are widely used in code vulnerability detection. However, a critical yet often overlooked issue is that GNNs primarily rely on aggregating information from adjacent nodes, limiting structural information transfer to single-layer updates. In code graphs, nodes and relationships typically require cross-layer information propagation to fully capture complex program logic and potential vulnerability patterns. Furthermore, while some studies utilize codeLMs to supplement GNNs with code semantic information, existing integration methods have not fully explored the potential of their collaborative effects.To address these challenges, we introduce Vul-LMGNNs that integrates pre-trained CodeLMs with GNNs, leveraging knowledge distillation to facilitate cross-layer propagation of both code semantic knowledge and structural information. Specifically, Vul-LMGNNs utilizes Code Property Graphs (CPGs) to incorporate code syntax, control flow, and data dependencies, while employing gated GNNs to extract structural information in the CPG. To achieve cross-layer information transmission, we implement an online knowledge distillation (KD) program that enables a single student GNN to acquire structural information extracted from a simultaneously trained counterpart through an alternating training procedure. Additionally, we leverage pre-trained CodeLMs to extract semantic features from code sequences. Finally, we propose an ”implicit-explicit” joint training framework to better leverage the strengths of both CodeLMs and GNNs. In the implicit phase, we utilize CodeLMs to initialize the node embeddings of each student GNN. Through online knowledge distillation, we facilitate the propagation of both code semantics and structural information across layers. In the explicit phase, we perform linear interpolation between the CodeLM and the distilled GNN to learn a late fusion model. The proposed method, evaluated across four real-world vulnerability datasets, demonstrated superior performance compared to 17 state-of-the-art approaches. Our source code can be accessed via GitHub: https://github.com/Vul-LMGNN/vul-LMGGNN.

Using query semantic and feature transfer fusion to enhance cardinality estimating of property graph queries

With the increasing complexity and diversity of query tasks, cardinality estimation has become one of the most challenging problems in query optimization. In this study, we propose an efficient and accurate cardinality estimation method to address the cardinality estimation problem in property graph queries, particularly in response to the current research gap regarding the neglect of contextual semantic features. We first propose formal representations of the property graph query and define its cardinality estimation problem. Then, through the query featurization, we transform the query into a vector representation that can be learned by the estimation model, and enrich the feature vector representation by the context semantic information of the query. We finally propose an estimation model for property graph queries, specifically introducing a feature information transfer module to dynamically control the information flow meanwhile achieving the model’s feature fusion and inference. Experimental results on three datasets show that the estimation model can accurately and efficiently estimate the cardinality of property graph queries, the mean Q_error and RMSE are reduced by about 30% and 25% than the state-of-art estimation models. The context semantics features of queries can improve the model’s estimation accuracy, the mean Q_error result is reduced by about 20% and the RMSE result is about 5%.

PRSC: From PG to RDF and back, using schemas

Property graphs (PG) and RDF graphs are two popular database graph models, but they are not interoperable: data modeled in PG cannot be directly integrated with other data modeled in RDF. This lack of interoperability also impedes the use of the tools of one model when data are modeled in the other. In this paper, we propose PRSC, a configurable conversion to transform a PG into an RDF graph. This conversion relies on PG schemas and user-defined mappings called PRSC contexts. We also formally prove that a subset of PRSC contexts, called well-behaved contexts, can be used to reverse back to the original PG, and provide the related algorithm. Algorithms for conversion and reversion are available as open-source implementations.

DTGraph: Declarative Transformations of Property Graphs

Current graph query languages, including the standards SQL/PGQ and GQL, define their semantics in terms of sets of tuples. This is largely inadequate for data interoperability tasks such as data migration or data integration which require queries to output new property graphs. This demonstration showcases DTGraph, an open-source declarative rule-based framework for easily specifying and efficiently executing property graph transformations. We describe a novel comprehensive system that allows the declarative specification of property graph transformations, by extending openCypher queries with a new GENERATE clause for creating new property graphs. The system includes several modules: a parser, a compiler for translating the transformation logic into an efficient executable openCypher script, and an interface assisting users in developing their transformations. The demonstration showcases the ability of our framework to scale to large graph data, and its suitability for transforming real-world datasets.

Property Graph Partition Algorithm Based on Improved Barnacle Mating Optimization

Abstract Distributed graph processing systems have been used more frequently in various fields, and graph partitioning is the basis of these systems. Graph partitioning algorithms generally aim to minimize the communication cost while the number of vertices or edges reach the load balance. But the vertices and/or edges of property graphs require some storage volume, so the traditional graph partition algorithms will lead to an unbalanced storage volume load. This paper proposes an edge-cut graph partitioning algorithm to generate partitions with equal size and storage volume as well as low cut-edge ratio. Initially, it partitions the graph into k partitions with equal size and storage volume. It then migrates vertices based on the improved Barnacles mating optimizer. The experiments on real-world graphs show that the proposed algorithm can achieve the partition size of volume balance, and the cut-edge ratio is also very low.

KGFabric: A Scalable Knowledge Graph Warehouse for Enterprise Data Interconnection

Based on the diversified application scenarios at Ant Group, we built the Ant Knowledge Graph Platform (AKGP). It has constructed numerous domain-specific knowledge graphs related to merchants, companies, accounts, products, and more. AKGP manages trillions of structured knowledge graphs, serving search, recommendation, risk control and other businesses. However, as the demand increasing for various workloads such as graph pattern matching, graph representation learning, and cross-domain knowledge reuse, the existing warehouse systems based on relational DBMS or graph databases are unable to meet the requirements. To address these issues, we propose KGFabric, an industrial-scale knowledge graph management system built on the distributed file system (DFS). KGFabric offers a nearline knowledge storage engine that utilizes a Semantic-enhanced Programmable Graph (SPG) model, which is compatible with the Labeled Property Graph (LPG) model. The data is persistently stored in DFS, such as HDFS, which leverages the POSIX file system API, making it suitable for deployment in multi-cloud environment at low cost. KGFabric provides a native graph-based and hybrid storage format that can serve as a shared backend for parallel graph computing systems, significantly accelerating the analysis of multi-workload. Additionally, KGFabric includes a graph fabric framework that minimizes data duplication and guarantees data security. KGFabric is able to manage Peta-scale data and has supported graph fabric and analysis with over 100 billion relations at Ant Group. We conduct experiments on various datasets to evaluate the performance of KGFabric. Compared with popular relational DBMS and graph databases, the storage space for semantic relations is reduced by over 90%. The performance of graph fabric improves by 21× in real-world workloads. In multi-hop semantic graph analysis, KGFabric enhances performance by 100×.

Hidden code vulnerability detection: A study of the Graph-BiLSTM algorithm

Context:The accelerated growth of the Internet and the advent of artificial intelligence have led to a heightened interdependence of open source products, which has in turn resulted in a rise in the frequency of security incidents. Consequently, the cost-effective, fast and efficient detection of hidden code vulnerabilities in open source software products has become an urgent challenge for both academic and engineering communities. Objectives:In response to this pressing need, a novel and efficient code vulnerability detection model has been proposed: the Graph-Bi-Directional Long Short-Term Memory Network Algorithm (Graph-BiLSTM). The algorithm is designed to enable the detection of vulnerabilities in Github’s code commit records on a large scale, at low cost and in an efficient manner. Methods:In order to extract the most effective code vulnerability features, state-of-the-art vulnerability datasets were compared in order to identify the optimal training dataset. Initially, the Joern tool was employed to transform function-level code blocks into Code Property Graphs (CPGs). Thereafter, structural features (degree centrality, Katz centrality, and closeness centrality) of these CPGs were computed and combined with the embedding features of the node sequences to form a two-dimensional feature vector space for the function-level code blocks. Subsequently, the BiLSTM network algorithm was employed for the automated extraction and iterative model training of a substantial number of vulnerability code samples. Finally, the trained algorithmic model was applied to code commit records of open-source software products on GitHub, achieving effective detection of hidden code vulnerabilities. Conclusion:Experimental results indicate that the PrimeVul dataset represents the most optimal resource for vulnerability detection. Moreover, the Graph-BiLSTM model demonstrated superior performance in terms of accuracy, training cost, and inference time when compared to state-of-the-art algorithms for the detection of vulnerabilities in open-source software code on GitHub. This highlights the significant value of the model for engineering applications.

Expanding Irreducible Element models: beams and composites

Population-based Structural Health Monitoring's (PBSHM) aspiration is to accumulate a deeper knowledge of a structure's health, by harvesting available data across a range of similar structures (or substructures). Whilst each structure is represented via a unified language called an Irreducible Element (IE) model, the comparison and similarity metrics are computed within a graph space. As such, each model is subsequently converted into an Attributed Graph (AG) before being placed into the PBSHM Framework's comparison graph space; the network. This paper looks into the application of using the PBSHM Framework's comparison graph space as a complex network and determining if the theories of communities and community structures from network science, can be applied to the PBSHM Framework's network, to determine similarity and define yet unknown populations.

An efficient approach for discovering Graph Entity Dependencies (GEDs)

Graph entity dependencies (GEDs) are novel graph constraints, unifying keys and functional dependencies, for property graphs. They have been found useful in many real-world data quality and data management tasks, including fact checking on social media networks and entity resolution. In this paper, we study the discovery problem of GEDs—finding a minimal cover of valid GEDs in a given graph data. We formalise the problem, and propose an effective and efficient approach to overcome major bottlenecks in GED discovery. In particular, we leverage existing graph partitioning algorithms to enable fast GED-scope discovery, and employ effective pruning strategies over the prohibitively large space of candidate dependencies. Furthermore, we define an interestingness measure for GEDs based on the minimum description length principle, to score and rank the mined cover set of GEDs. Finally, we demonstrate the scalability and effectiveness of our GED discovery approach through extensive experiments on real-world benchmark graph data sets; and present the usefulness of the discovered rules in different downstream data quality management applications.

Efficient Static Vulnerability Analysis for JavaScript with Multiversion Dependency Graphs

While static analysis tools that rely on Code Property Graphs (CPGs) to detect security vulnerabilities have proven effective, deciding how much information to include in the graphs remains a challenge. Including less information can lead to a more scalable analysis but at the cost of reduced effectiveness in identifying vulnerability patterns, potentially resulting in classification errors. Conversely, more information in the graph allows for a more effective analysis but may affect scalability. For example, scalability issues have been recently highlighted in ODGen, the state-of-the-art CPG-based tool for detecting Node.js vulnerabilities. This paper examines a new point in the design space of CPGs for JavaScript vulnerability detection. We introduce the Multiversion Dependency Graph (MDG), a novel graph-based data structure that captures the state evolution of objects and their properties during program execution. Compared to the graphs used by ODGen, MDGs are significantly simpler without losing key information needed for vulnerability detection. We implemented Graph.js, a new MDG-based static vulnerability scanner specialized in analyzing npm packages and detecting taint-style and prototype pollution vulnerabilities. Our evaluation shows that Graph.js outperforms ODGen by significantly reducing both the false negatives and the analysis time. Additionally, we have identified 49 previously undiscovered vulnerabilities in npm packages.

Formulate an Adaptive Technique to Validate Near Field Communication Technology using Attributed Graph Grammar-AGG tool

Recently, developments in such Near field Communication Technology (NFC) and associated integrated devices have pace out an acceleration the demand of transactions in electronic devices system such wearable readers which have short-power consumption and proficiency in high transferring data. NFC has showed the alternative active key solution to existing smart electronic devices that depend on huge batteries to supply the energy in paying bills though such devices. NFC widely utilized via wireless power transfer, where devices share and connect with each other, to increase the efficiency of transferring data, with high superb properties such as permeability. NFC has emerged as a powerful technology and its apparatus devices. The ongoing of NFC growth could be related to its extensibility and more sustainable. NFC can deliver different shapes at providing software application capabilities to connect with each other. NFCs demonstrates an adaptable method to enhance and offer current elements accessible to applications across the Internet. NFCs are a new fad and modern technology that shares embedded and ubiquitous devices with others, various corporations are finding the advantage by the NFC. This research will propose a formulation technique to explore and adapt an approach of developing and validating the NFC which permits to exchange data via technology wireless (NFC) between the parties. This technique will facilitate the feasibility of having e.g., a system to reduce of losing duration time and cost for users, as well as, an agile technique for validating the suggested approach via Attributed Graph Grammar (AGG) tools.

Semantic units: organizing knowledge graphs into semantically meaningful units of representation

BackgroundIn today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs.ResultsWe introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource.ConclusionsSemantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.

Cardinality estimation for property graph queries with gated learning approach on the graph database

Cardinality estimation for property graph queries with gated learning approach on the graph database

Reasoning on property graphs with graph generating dependencies

Data dependencies are a key concept in data management and have been researched in data integration, data quality and query optimization. With the increasing use of graph-structured data in diverse applications, there is also an increasing interest in the study of graph data dependencies. In this scenario, different classes of graph data dependencies have been proposed in the literature. In this work, we study the class of Graph Generating Dependencies (GGDs). Graph Generating Dependencies (GGDs) informally express constraints between two (possibly different) graph patterns which enforce relationships on both graph's data (via property value constraints) and its structure (via topological constraints). While most of previously proposed classes of graph data dependencies focus on generalizing equality-generating dependencies for graph data, Graph Generating Dependencies (GGDs) can express tuple- and equality-generating dependencies on property graphs, both of which find broad application in graph data management. Given this new class of dependency, in this paper, we discuss the reasoning behind GGDs on Property Graphs. We propose algorithms to solve three main reasoning problems: the satisfiability, implication, and validation problems for GGDs and analyze their complexity. By studying these problems, we can understand the expressiveness and the limitations of GGDs in practical applications. To demonstrate the practical use of GGDs, we propose an algorithm that finds inconsistencies in data through validation of GGDs. Our experiments show that even though the validation of GGDs has high computational complexity, GGDs can be used to find data inconsistencies in a feasible execution time on both synthetic and real-world data.

Predicting an Optimal Virtual Data Model for Uniform Access to Large Heterogeneous Data

ABSTRACT The growth of generated data in the industry requires new efficient big data integration approaches for uniform data access by end-users to perform better business operations. Data virtualization systems, including Ontology-Based Data Access (ODBA) query data on-the-fly against the original data sources without any prior data materialization. Existing approaches by design use a fixed model e.g., TABULAR as the only Virtual Data Model — a uniform schema built on-the-fly to load, transform, and join relevant data. While other data models, such as GRAPH or DOCUMENT, are more flexible and, thus, can be more suitable for some common types of queries, such as join or nested queries. Those queries are hard to predict because they depend on many criteria, such as query plan, data model, data size, and operations. To address the problem of selecting the optimal virtual data model for queries on large datasets, we present a new approach that (1) builds on the principal of OBDA to query and join large heterogeneous data in a distributed manner and (2) calls a deep learning method to predict the optimal virtual data model using features extracted from SPARQL queries. OPTIMA — implementation of our approach currently leverages state-of-the-art Big Data technologies, Apache-Spark and Graphx, and implements two virtual data models, GRAPH and TABULAR, and supports out-of-the-box five data sources models: property graph, document-based, e.g., wide-columnar, relational, and tabular, stored in Neo4j, MongoDB, Cassandra, MySQL, and CSV respectively. Extensive experiments show that our approach is returning the optimal virtual model with an accuracy of 0.831, thus, a reduction in query execution time of over 40% for the tabular model selection and over 30% for the graph model selection.

Deep Self-Supervised Attributed Graph Clustering for Social Network Analysis

Deep graph clustering is an unsupervised learning task that divides nodes in a graph into disjoint regions with the help of graph auto-encoders. Currently, such methods have several problems, as follows. (1) The deep graph clustering method does not effectively utilize the generated pseudo-labels, resulting in sub-optimal model training results. (2) Each cluster has a different confidence level, which affects the reliability of the pseudo-label. To address these problems, we propose a Deep Self-supervised Attribute Graph Clustering model (DSAGC) to fully leverage the information of the data itself. We divide the proposed model into two parts: an upstream model and a downstream model. In the upstream model, we use the pseudo-label information generated by spectral clustering to form a new high-confidence distribution with which to optimize the model for a higher performance. We also propose a new reliable sample selection mechanism to obtain more reliable samples for downstream tasks. In the downstream model, we only use the reliable samples and the pseudo-label for the semi-supervised classification task without the true label. We compare the proposed method with 17 related methods on four publicly available citation network datasets, and the proposed method generally outperforms most existing methods in three performance metrics. By conducting a large number of ablative experiments, we validate the effectiveness of the proposed method.

Efficient and Privacy-Preserving Aggregate Query Over Public Property Graphs

Efficient and Privacy-Preserving Aggregate Query Over Public Property Graphs