Query Graph Research Articles

A Twig-Based Algorithm for Top-k Subgraph Matching in Large-Scale Graph Data

Subgraph matching aims to find similar substructures in a single graph according to a given query graph and is known as a basic query for graph data management. There exist many categories of subgraph matching solutions. Subgraph isomorphism, which is thought of an NP-complete problem, is an initial solution for the subgraph matching task. To speed up the procedure, graph simulation has been presented to match subgraphs with a polynomial complexity of time. Unfortunately, graph simulation usually loses topologies of matched subgraphs because of its loose restrictions. In this paper, we propose an approximation approach named kSGM (top- k S ubraph G raph M atching) for subgraph matching based on twig patterns. First, we transform query graphs into twig patterns and match candidate substructures in graph data. Second, we present an optimized join strategy along with top-k mechanism, including join order selection based on cost evaluation and optimized pruning based on maximum/minimum possible score. Finally, we design experiments on real-life and synthetic graph data to evaluate the performance of our work. The results show that our proposed kSGM obviously reduces the time complexity and guarantee the correctness for answering the queries of subgraph matching compared to existing algorithms.

Incremental type-checking for free: using scope graphs to derive incremental type-checkers

Fast analysis response times in IDEs are essential for a good editor experience. Incremental type-checking can provide that in a scalable fashion. However, existing techniques are not reusable between languages. Moreover, mutual and dynamic dependencies preclude traditional approaches to incrementality. This makes finding automatic approaches to incremental type-checking a challenging but important open question. In this paper, we present a technique that automatically derives incremental type-checkers from type system specifications written in the Statix meta-DSL. We use name resolution queries in scope graphs (a generic model of name binding embedded in Statix) to derive dependencies between compilation units. A novel query confirmation algorithm finds queries for which the answer changed due to an edit in the program. Only units with such queries require reanalysis. The effectiveness of this algorithm is improved by (1) splitting the type-checking task into a context-free and a context-sensitive part, and (2) reusing a generic mechanism to resolve mutual dependencies. This automatically yields incremental type-checkers for any Statix specification. Compared to non-incremental parallel execution, we achieve speedups up to 147x on synthetic benchmarks, and up to 21x on real-world projects, with initial overheads below 10%. This suggests that our framework can provide efficient incremental type-checking to the wide range of languages supported by Statix.

Edge-Aware Graph Neural Network for Multi-Hop Path Reasoning over Knowledge Base.

Multi-hop path reasoning over knowledge base aims at finding answer entities for an input question by walking along a path of triples from graph structure data, which is a crucial branch in the knowledge base question answering (KBQA) research field. Previous studies rely on deep neural networks to simulate the way humans solve multi-hop questions, which do not consider the latent relation information contained in connected edges, and lack of measuring the correlation between specific relations and the input question. To address these challenges, we propose an edge-aware graph neural network for multi-hop path reasoning task. First, a query node is directly added to the candidate subgraph retrieved from the knowledge base, which constructs what we term a query graph. This graph construction strategy makes it possible to enhance the information flow between the question and the nodes for the subsequent message passing steps. Second, question-related information contained in the relations is added to the entity node representations during graph updating; meanwhile, the relation representations are updated. Finally, the attention mechanism is used to weight the contribution from neighbor nodes so that only the information of neighbor nodes related to the query can be injected into new node representations. Experimental results on MetaQA and PathQuestion-Large (PQL) benchmarks demonstrate that the proposed model achieves higher Hit@1 and F1 scores than the baseline methods by a large margin. Moreover, ablation studies show that both the graph construction and the graph update algorithm contribute to performance improvement.

Evolving subgraph matching on temporal graphs

Given a set of query graphs and specified orders between these query graphs, the evolving subgraph matching problem finds all subgraph sets from the temporal graph that not only match the given query graph set but also satisfy the specified orders. This problem motivates many significant application, e.g., social network analysis, financial fraud crimes detection, biological information mining, and so on. To efficiently address this problem, an evolving subgraph matching algorithm based on two key techniques is proposed. First, a memory-efficient index structure called EMI is designed to compactly store all necessary information required for finding all evolving matchings. To make the EMI compact and to construct the EMI efficiently, several techniques, such as four well-designed filters and adaptive filling orders, are proposed. Second, an evolving subgraph matching enumeration method is designed to immediately run on the EMI instead of on the temporal graph. The enumeration method expands evolving matchings in an edge-by-edge manner. Since the EMI is compactly maintained in the main memory and generally smaller than the temporal graph, the enumeration method runs very fast on the EMI. An extensive experimental evaluation has been carried out to evaluate the proposed algorithm. The experimental results show that the enumeration method executed on the EMI is an order of magnitude faster and requires significantly less memory than the baseline algorithms. To the best of our knowledge, there is no prior work on the evolving subgraph matching on temporal graphs.

AEDNet: Adaptive Edge-Deleting Network For Subgraph Matching

Subgraph matching is to find all subgraphs in a data graph that are isomorphic to an existing query graph. Subgraph matching is an NP-hard problem, yet has found its applications in many areas. Many learning-based methods have been proposed for graph matching, whereas few have been designed for subgraph matching. The subgraph matching problem is generally more challenging, mainly due to the different sizes between the two graphs, resulting in considerable large space of solutions. Also the extra edges existing in the data graph connecting to the matched nodes may lead to two matched nodes of two graphs having different adjacency structures and often being identified as distinct objects. Due to the extra edges, the existing learning based methods often fail to generate sufficiently similar node-level embeddings for matched nodes. This study proposes a novel Adaptive Edge-Deleting Network (AEDNet) for subgraph matching. The proposed method is trained in an end-to-end fashion. In AEDNet, a novel sample-wise adaptive edge-deleting mechanism removes extra edges to ensure consistency of adjacency structure of matched nodes, while a unidirectional cross-propagation mechanism ensures consistency of features of matched nodes. We applied the proposed method on six datasets with graph sizes varying from 20 to 2300. Our evaluations on six open datasets demonstrate that the proposed AEDNet outperforms six state-of-the-arts and is much faster than the exact methods on large graphs.

A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration.

Industry 4.0 era has witnessed that more and more high-tech and precise devices are applied into medical field to provide better services. Besides EMRs, medical data include a large amount of unstructured data such as X-rays, MRI scans, CT scans and PET scans, which is still continually increasing. These massive, heterogeneous multi-modal data bring the big challenge to finding valuable data sets for healthcare researchers and other users. The traditional data warehouses are able to integrate the data and support interactive data exploration through ETL process. However, they have high cost and are not real-time. Furthermore, they lack of the ability to deal with multi-modal data in two phases—data fusion and data exploration. In the data fusion phase, it is difficult to unify the multi-modal data under one data model. In the data exploration phase, it is challenging to explore the multi-modal data at the same time, which impedes the process of extracting the diverse information underlying multi-modal data. Therefore, in order to solve these problems, we propose a highly efficient data fusion framework supporting data exploration for heterogeneous multi-modal medical data based on data lake. This framework provides a novel and efficient method to fuse the fragmented multi-modal medical data and store their metadata in the data lake. It offers a user-friendly interface supporting hybrid graph queries to explore multi-modal data. Indexes are created to accelerate the hybrid data exploration. One prototype has been implemented and tested in a hospital, which demonstrates the effectiveness of our framework.

Distributed graph pattern matching via bounded dual simulation

Graph Pattern Matching (GPM) finds subgraphs of a large data graph that are similar to an input query graph. It has many applications, such as pattern recognition, detecting plagiarism, and finding communities in social networks. Current real-world applications generate massive amounts of data that cannot be stored on the memory of a single machine, which raises the need for distributed storage and processing. Recent relaxed GPM models, although of polynomial time complexity, are nevertheless not distributed by nature. Moreover, the existing relaxed GPM algorithms are limited in terms of scalability. In this paper, we propose Bounded Dual Simulation (BDSim) as a new relaxed model for a scalable evaluation of GPM in massive graphs. BDSim captures more semantic similarities compared to graph simulation, dual simulation, and even strong simulation. It preserves the vertices’ proximity by eliminating cycles of unbounded length from the resulting match graph. Furthermore, we propose distributed vertex-centric algorithms to evaluate BDSim. We prove their effectiveness and efficiency through detailed theoretical validation and extensive experiments conducted on real-world and synthetic datasets. To the best of our knowledge, BDSim is the first relaxed GPM model that captures the cyclic structure of the query graph while being feasible in cubic time.

AvantGraph query processing engine

We demonstrate AvantGraph, a graph query processing engine developed by the Database group at TU Eindhoven. Designed for efficient processing of both subgraph matching and navigational graph queries, AvantGraph encompasses innovation in three key areas: the planner, the cardinality estimator, and the execution engine. We present demonstration scenarios covering a wide range of workloads across diverse domains which (1) provides deep insights into the core challenges of complex graph query processing and (2) showcases corresponding critical optimizations via "under-the-hood" operational insights of AvantGraph's key components.

ByteGraph

Most products at ByteDance, e.g., TikTok, Douyin, and Toutiao, naturally generate massive amounts of graph data. To efficiently store, query and update massive graph data is challenging for the broad range of products at ByteDance with various performance requirements. We categorize graph workloads at ByteDance into three types: online analytical, transaction, and serving processing, where each workload has its own characteristics. Existing graph databases have different performance bottlenecks in handling these workloads and none can efficiently handle the scale of graphs at ByteDance. We developed ByteGraph to process these graph workloads with high throughput, low latency and high scalability. There are several key designs in ByteGraph that make it efficient for processing our workloads, including edge-trees to store adjacency lists for high parallelism and low memory usage, adaptive optimizations on thread pools and indexes, and geographic replications to achieve fault tolerance and availability. ByteGraph has been in production use for several years and its performance has shown to be robust for processing a wide range of graph workloads at ByteDance.

Staged query graph generation based on answer type for question answering over knowledge base

Question answering over knowledge base (KBQA) enables users to query over the knowledge base without the need to know the details. A range of existing KBQA approaches treats the entities mentioned in the given question as the starting point to find the answers. While helpful in achieving improvements on the existing benchmarks, they have some limitations on the strategy of query graph generation, which creates too many candidate queries and makes it hard to select the best-matching one to get the answer. In this paper, we propose a staged query graph generation approach based on the answer type, which exploits the correlation between questions and answer types to reduce the size of the candidate set and further improve the performance. Besides, we construct a question/answer-type (QAT) dataset aiming to predict the answer type of a given question. Extensive experiments demonstrate our method outperforms existing methods on both simple questions and complex questions.

JointConf: Jointly autotuning configuration parameters for modularized graph databases

To support different application scenarios, graph databases (GDBs) usually provide a large number of performance-related parameters for developers. Since manually configuring is both time-consuming and cost-intensive, automatically tuning configurations parameters to achieve a better performance has been an urgent need. Besides, considering various graph management requirements, GDBs begin to utilize the modular architecture to interoperate with a wide range of storage and index backends. Due to the complicated interactions among different modules, sequentially tuning each software with previous solutions may fall into a local optimal and it is necessary to jointly autotune the cross-module configuration parameters. Toward this challenging target, we propose JointConf—a new black-box approach of jointly autotuning configuration parameters for modularized GDBs. To address the formulated high-dimensional black-box optimization problem, JointConf utilizes the recently proposed BO_dropout algorithm. Inspired by the dropout algorithm in neural networks, BO_dropout explores efficient dimension dropout to achieve a high-dimensional Bayesian optimization. We evaluate the effectiveness of JointConf on a local distributed JanusGraph cluster with three different graph query benchmark applications and experimental results show its advantages over the four baseline search-based approaches. The necessity of jointly tuning for modularized GDBs is also verified in our experiments.

Structure-sensitive semantic matching for aggregate question answering over knowledge base

Aggregate question answering essentially returns answers for given questions by obtaining query graphs with unique dependencies between values and corresponding objects. Word order dependency, as the key to uniquely identify dependency of the query graph, reflects the dependencies between the words in the question. However, due to the semantic gap caused by the expression difference between questions encoded with word vectors and query graphs represented with logical formal elements, it is not trivial to match the correct query graph for the question. Most existing approaches design more expressive query graphs for complex questions and rank them just by directly calculating their similarities, ignoring the semantic gap between them. In this paper, we propose a novel Structure-sensitive Semantic Matching(SSM) approach that learns aligned representations of dependencies in questions and query graphs to eliminate their gap. First, we propose a cross-structure matching module to bridge the gap between two modalities(i.e., textual question and query graph). Then, we propose an entropy-based gated AQG filter to remove the structural noise caused by the uncertainty of dependencies. Finally, we present a two-channel query graph representation that fuses the semantics of abstract structure and grounding content of the query graph explicitly. Experimental results show that SSM could learn aligned representations of questions and query graphs to eliminate the gaps between their dependencies, and improves up to 12% (F1 score) on aggregation questions of two benchmark datasets.

KRYSTAL: Knowledge graph-based framework for tactical attack discovery in audit data

Attack graph-based methods are a promising approach towards discovering attacks and various techniques have been proposed recently. A key limitation, however, is that approaches developed so far are monolithic in their architecture and heterogeneous in their internal models. The inflexible custom data models of existing prototypes and the implementation of rules in code rather than declarative languages on the one hand make it difficult to combine, extend, and reuse techniques, and on the other hand hinder reuse of security knowledge – including detection rules and threat intelligence. KRYSTAL tackles these challenges by providing a knowledge graph-based, modular framework for threat detection, attack graph and scenario reconstruction, and analysis based on RDF as a standard model for knowledge representation. This approach provides query options that facilitate contextualization over internal and external background knowledge, as well as the integration of multiple detection techniques, including tag propagation, attack signatures, and graph queries. We implemented our framework in an openly available prototype and demonstrate its applicability on multiple scenarios of the DARPA Transparent Computing dataset. Our evaluation shows that the combination of different threat detection techniques within our framework improved detection capabilities. Furthermore, we find that RDF provenance graphs are scalable and can efficiently support a variety of threat detection techniques.

Interpretable Neural Subgraph Matching for Graph Retrieval

Given a query graph and a database of corpus graphs, a graph retrieval system aims to deliver the most relevant corpus graphs. Graph retrieval based on subgraph matching has a wide variety of applications, e.g., molecular fingerprint detection, circuit design, software analysis, and question answering. In such applications, a corpus graph is relevant to a query graph, if the query graph is (perfectly or approximately) a subgraph of the corpus graph. Existing neural graph retrieval models compare the node or graph embeddings of the query-corpus pairs, to compute the relevance scores between them. However, such models may not provide edge consistency between the query and corpus graphs. Moreover, they predominantly use symmetric relevance scores, which are not appropriate in the context of subgraph matching, since the underlying relevance score in subgraph search should be measured using the partial order induced by subgraph-supergraph relationship. Consequently, they show poor retrieval performance in the context of subgraph matching. In response, we propose ISONET, a novel interpretable neural edge alignment formulation, which is better able to learn the edge-consistent mapping necessary for subgraph matching. ISONET incorporates a new scoring mechanism which enforces an asymmetric relevance score, specifically tailored to subgraph matching. ISONET’s design enables it to directly identify the underlying subgraph in a corpus graph, which is relevant to the given query graph. Our experiments on diverse datasets show that ISONET outperforms recent graph retrieval formulations and systems. Additionally, ISONET can provide interpretable alignments between query-corpus graph pairs during inference, despite being trained only using binary relevance labels of whole graphs during training, without any fine-grained ground truth information about node or edge alignments.

FGDB: a comprehensive graph database of ligand fragments from the Protein Data Bank.

This work presents Fragment Graph DataBase (FGDB), a graph database of ligand fragments extracted and generated from the protein entries available in the Protein Data Bank (PDB). FGDB is meant to support and elicit campaigns of fragment-based drug design, by enabling users to query it in order to construct ad hoc, target-specific libraries. In this regard, the database features more than 17 000 fragments, typically small, highly soluble and chemically stable molecules expressed via their canonical Simplified Molecular Input Line Entry System (SMILES) representation. For these fragments, the database provides information related to their contact frequencies with the amino acids, the ligands they are contained in and the proteins the latter bind to. The graph database can be queried via standard web forms and textual searches by a number of identifiers (SMILES, ligand and protein PDB ids) as well as via graphical queries that can be performed against the graph itself, providing users with an intuitive and effective view upon the underlying biological entities. Further search mechanisms via advanced conjunctive/disjunctive/negated textual queries are also possible, in order to allow scientists to look for specific relationships and export their results for further studies. This work also presents two sample use cases where maternal embryonic leucine zipper kinase and mesotrypsin are used as a target, being proteins of high biomedical relevance for the development of cancer therapies. Database URL: http://biochimica3.bio.uniroma3.it/fragments-web/

Knowledge graph model development for knowledge discovery in dementia research using cognitive scripting and next-generation graph-based database: a design science research approach

Recent studies report doubling numbers of deaths due to dementia. With such an escalating mortality rate related to cognitive decline diseases, like dementia, timely information on contributing factors and knowledge discovery from evidence-based repositories is warranted. A large amount of scholarly knowledge extracted from research findings on dementia can be understood only using human intelligence for arriving at quality inferences. Due to the unstructured data presented in such a massive dataset of scientific articles available online, gaining insights from the knowledge hidden in the literature is complex and time-consuming. Hence, there is a need for developing a knowledge management model to create, query and maintain a knowledge repository of key elements and their relationships extracted from scholarly articles in a structured manner. In this paper, an innovative knowledge discovery computing model to process key findings from unstructured data from scholarly articles by using the design science research (DSR) methodology is proposed. The solution caters to a novel composition of the cognitive script of crucial knowledge related to dementia and its subsequent transformation from unstructured into a structured format using graph-based next-generation infrastructures. The computing model contains three phases to assist the research community to have a better understanding of the related knowledge in the existing unstructured research articles: (i) article collection and construction of cognitive script, (ii) generation of Cypher statements (a knowledge graph query language) and (iii) creation of graph-based repository and visualization. The performance of the computing model is demonstrated by visualizing the outcome of various search criteria in the form of nodes and their relationships. Our results also demonstrate the effectiveness of visual query and navigation highlighting its usability.

Personalized query techniques in graphs: A survey

Graph is a famous data structure that has prevalent applications in the real world, including social networks, biological networks, and computer networks. In these applications, graph management operators are powerful tools for mining important information hidden in large-scale graphs. As important graph data management operators, personalized graph queries are playing an increasingly significant role in providing users with effective decision support. In particular, the purpose of personalized graph queries is to compute personalized results which can meet the preferences of different users from the three aspects of specified query vertices, structures, and attributes. In this paper, we conduct a survey to offer a comprehensive view of the current personalized graph queries which need users to specify query vertices over simple and attributed graphs, respectively. These queries fall into three categories, including point-related, path-related, and subgraph-related graph queries, whose query results have distinct structures. We analyze existing approaches to personalized graph queries and highlight current challenges. In addition, we also offer guidelines for future graph queries.

Banyan

Graph query services (GQS) are widely used today to interactively answer graph traversal queries on large-scale graph data. Existing graph query engines focus largely on optimizing the latency of a single query. This ignores significant challenges posed by GQS, including fine-grained control and scheduling during query execution, as well as performance isolation and load balancing in various levels from across user to intra-query. To tackle these control and scheduling challenges, we propose a novel scoped dataflow for modeling graph traversal queries, which explicitly exposes concurrent execution and control of any subquery to the finest granularity. We implemented Banyan, an engine based on the scoped dataflow model for GQS. Banyan focuses on scaling up the performance on a single machine, and provides the ability to easily scale out. Extensive experiments on multiple benchmarks show that Banyan improves performance by up to three orders of magnitude over state-of-the-art graph query engines, while providing performance isolation and load balancing.

Continuous multi-query optimization for subgraph matching over dynamic graphs

There is a growing need to perform real-time analytics on dynamic graphs in order to deliver the values of big data to users. An important problem from such applications is continuously identifying and monitoring critical patterns when fine-grained updates at a high velocity occur on the graphs. A lot of efforts have been made to develop practical solutions for these problems. Despite the efforts, existing algorithms showed limited running time and scalability in dealing with large and/or many graphs. In this paper, we study the problem of continuous multi-query optimization for subgraph matching over dynamic graph data. (1) We propose annotated query graph, which is obtained by merging the multi-queries into one. (2) Based on the annotated query, we employ a concise auxiliary data structure to represent partial solutions in a compact form. (3) In addition, we propose an efficient maintenance strategy to detect the affected queries for each update and report corresponding matches in one pass. (4) Extensive experiments over real-life and synthetic datasets verify the effectiveness and efficiency of our approach and confirm a two orders of magnitude improvement of the proposed solution.

Incremental Graph Computations: Doable and Undoable

The incremental problem for a class \( {\mathcal {Q}} \) of graph queries aims to compute, given a query \( Q \in {\mathcal {Q}} \) , graph G , answers Q ( G ) to Q in G and updates ΔG to G as input, changes ΔO to output Q ( G ) such that Q ( G ⊕ ΔG ) = Q ( G )⊕ ΔO . It is called bounded if its cost can be expressed as a polynomial function in the sizes of Q , ΔG and ΔO , which reduces the computations on possibly big G to small ΔG and ΔO . No matter how desirable, however, our first results are negative: For common graph queries such as traversal, connectivity, keyword search, pattern matching, and maximum cardinality matching, their incremental problems are unbounded. In light of the negative results, we propose two characterizations for the effectiveness of incremental graph computation: (a) localizable , if its cost is decided by small neighbors of nodes in ΔG instead of the entire G ; and (b) bounded relative to a batch graph algorithm \( {\mathcal {T}} \) , if the cost is determined by the sizes of ΔG and changes to the affected area that is necessarily checked by any algorithms that incrementalize \( {\mathcal {T}} \) . We show that the incremental computations above are either localizable or relatively bounded by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize existing batch graph algorithms by minimizing unnecessary recomputation. Using real-life and synthetic data, we experimentally verify the effectiveness of our incremental algorithms.