Graph Query Processing Research Articles

Neural Similarity Search on Supergraph Containment

Supergraph search is a fundamental graph query processing problem. Supergraph search aims to find all data graphs contained in a given query graph based on the subgraph isomorphism. Existing algorithms construct the indices and adopt the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">filtering-and-verification</i> framework which is usually computationally expensive and can cause redundant computations. Recently, various learning-based methods have been proposed for a good trade-off between accuracy and efficiency for query processing tasks. However, to the best of our knowledge, there is no learning-based method proposed for the supergraph search task. In this paper, we propose the first learning-based method for similarity search on supergraph containment, named Neural Supergraph similarity Search ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSS</monospace> ). <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSS</monospace> first learns the representations for query and data graphs and then efficiently conducts the supergraph search on the representation space whose complexity is linear to the number of data graphs. The carefully designed Wasserstein discriminator and reconstruction network enable <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSS</monospace> to better capture the interrelation, structural and label information between and within the query and data graphs. Experiments demonstrate that the <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NSS</monospace> is up to 6 orders of magnitude faster than the state-of-the-art exact supergraph search algorithm in terms of query processing and more accurate compared to the other learning-based solutions.

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.

Compact structure for sparse undirected graphs based on a clique graph partition

Compressing real-world graphs has many benefits such as improving or enabling the visualization in small memory devices, graph query processing, community search, and mining algorithms. This work proposes a novel compact representation for real sparse and clustered undirected graphs. The approach lists all the maximal cliques by using a fast algorithm and defines a clique graph based on its maximal cliques. Further, the method defines a fast and effective heuristic for finding a clique graph partition that avoids the construction of the clique graph. Finally, this partition is used to define a compact representation of the input graph. The experimental evaluation shows that this approach is competitive with the state-of-the-art methods in terms of compression efficiency and access times for neighbor queries, and that it recovers all the maximal cliques faster than using the original graph. Moreover, the approach makes it possible to query maximal cliques, which is useful for community detection.

IDAR

Supergraph search is one of fundamental graph query processing problems in many application domains. Given a query graph and a set of data graphs, supergraph search is to find all the data graphs contained in the query graph as subgraphs. In existing algorithms, index construction or filtering approaches are computationally expensive, and search methods can cause redundant computations. In this paper, we introduce four new concepts to address these limitations: (1) DAG integration, (2) dynamic programming between integrated DAG and graph, (3) active-first search, and (4) relevance-size order, which together lead to a much faster and scalable algorithm for supergraph search. Extensive experiments with real datasets show that our approach outperforms state-of-the-art algorithms by up to orders of magnitude in terms of indexing time and query processing time.

Verifiable Subgraph Matching With Cryptographic Accumulators in Cloud Computing

Due to the rapid development of social networks, bio-informatics, internet-of-things etc. , subgraph matching query can be found in various applications. Meanwhile, the increasing popularity of storing graph data in the cloud drives demands for graph query processing on a remote cloud server. However, the query results in this scenario may not be guaranteed to be correct, especially when the cloud service provider (CSP) is malicious or compromised by some adversaries, for example, a CSP might omit some edges of the graph so that its search cost would be substantially reduced. Besides, various software bugs and unintended errors are also inevitable. All current generic verifiable computation (VC) schemes applied in this scenario are not only too impractical to be implemented but also need a lot of space to locally store their auxiliary data. To that end, we have put forth both public and designated verification schemes which focus on subgraph matching problems for outsourced graph data. They utilize a modified cryptographic primitive called accumulator to realize fast verification and low local storage overhead. In addition to the two main constructions, we have proposed an optimization to make the scheme more applicable, namely, supporting dynamic updates of the graph. At last, rigorous security proofs and efficiency analysis are given, which justify that our proposed schemes are secure and efficient, satisfying the requirements of general verifiable computation protocols.

Cost-Minimizing Online Algorithms for Geo-Distributed Data Analytics

Modern enterprises often manage geographically distributed datacenters around the globe. In such environment, datasets are naturally collected and stored in different data centers and were later queried for complex analytics. In this paper, we study the Wide-Area Data Analytics problem, which aims to efficiently control data movements and achieve low latency for overall queries processing, both constrained by limited and expensive network resources across datacenters. Previous papers focus on offline settings of single analytical queries and do not consider time in optimizing system performance, and therefore ignores the dynamics of data and task placement in terms of inter-DC bandwidth utilization. In this paper, we consider the online setting and formulate a cost-minimizing optimization problem over time for arbitrary Directed Acyclic Graph query processing. Considering dynamics of network resource usage, we developed two online algorithms, Online Switch Resist (OSR) and Most Fixed Horizon Control (MFHC) with good competitive ratios. We performed extensive simulations and comparative studies using the TPC-CH benchmark and verified the efficacy of proposed algorithms. The algorithm we proposed is better than the existing algorithm, and its performance approximates the theoretical optimal value.

Trade-offs Computing Minimum Hub Cover toward Optimized Labeled Graph Query Processing

As techniques for graph query processing mature, the need for optimization is increasingly becoming an imperative. Query processing aids such as indices are one of the key ingredients toward efficient query processing strategies via cost-based optimization. Due to the apparent absence of a common representation model, it is difficult to make a focused effort toward developing access structures, metrics to evaluate query costs, and choose alternatives. In this context, recent interests in covering-based graph matching appears to be a promising direction of research. In this paper, our goal is to formally introduce a new graph representation model, called Minimum Hub Cover, and demonstrate that this representation offers interesting strategic advantages, facilitates construction of candidate graphs from graph fragments, and helps leverage indices in novel ways for query optimization. However, similar to other covering problems, minimum hub cover is NP-hard, and thus is a natural candidate for optimization. We claim that computing the minimum hub cover leads to substantial cost reduction for graph query processing. We present a computational characterization of minimum hub cover based on integer programming to substantiate our claim and investigate its computational cost on various graph types.

Trade-offs Computing Minimum Hub Cover toward Optimized Labeled Graph Query Processing

Trade-offs Computing Minimum Hub Cover toward Optimized Labeled Graph Query Processing

Using Queries as Schema-Templates for Graph Databases

In contrast to heavy-handed ER-style data models in relational databases, knowledge graphs (or graph databases) capture entity semantics in terms of entity relationships and properties following a simple collect-as-you-go model. While this allows for a more flexible and dynamically adaptable knowledge representation, it comes at the price of more complex querying: with varying degrees of information sparsity, it will gradually become more difficult to figure out what an entity actually represents. Thus, matching the intended schema as specified by a query against actually occurring entity patterns in the graph database needs severe attention on a conceptual level. In this article, we analyze graph patterns as schema information from a graph pattern matching perspective. We argue that every query consists of a mixture of conceptual information (how entities are structured) together with evaluation information (further dependencies and constraints on data) and that this mixture is not always easy to divide. To arrive at truly schema-aware graph query processing, we propose several matching mechanisms, each mandating a specific semantic meaning of the graph pattern, and discuss their practical applicability.

Guest Editorial: Special Issue on Managing and Mining Massive Graphs

Many applications need to handle very large graphs, including theweb graph, social network graph, knowledge graphs, biology graphs, etc. We are facing challenges at all levels from system infrastructures to programmingmodels formanaging and analyzing large graphs. Unlike other types of big data, graphs are highly interconnected, which enables graph to represent complex data structures in a variety of applications, but at the same time, also makes graph query processing and graph analytics extremely difficult, especially when the graph is big. Much effort has been devoted to managing andminingmassive graphs. Thesework include graph systems, graphquery languages, graph access methods, basic operators such as graph reachability and shortest distance queries, and advanced analytics on graphs. There is also a big push from the application side. Linked data, social networks, customer relationship management, biological and chemical applications are all pushing for breakthroughs in this area. This special issue presents high quality research ideas related to the graph data management and mining area. The first paper of this issue: The G* Graph Database: Efficient Managing Large Distributed Dynamic Graphs by Alan G. Labouseur, Jeremy Birnbaum, PaulW. Olsen Jr., Sean R. Spillane, Jayadevan Vijayan, Jeong-Hyon Hwang, and Wook-Shin Han presents a graph database system G* to process complex queries over large graphs in parallel with the features of sharing computation across graphs.

Approximating the minimum hub cover problem on planar graphs

We study an approximation algorithm with a performance guarantee to solve a new $$\mathcal {NP}$$ -hard optimization problem on planar graphs. The problem, which is referred to as the minimum hub cover problem, has recently been introduced to the literature to improve query processing over large graph databases. Planar graphs also arise in various graph query processing applications, such as; biometric identification, image classification, object recognition, and so on. Our algorithm is based on a well-known graph decomposition technique that partitions the graph into a set of outerplanar graphs and provides an approximate solution with a proven performance ratio. We conduct a comprehensive computational experiment to investigate the empirical performance of the algorithm. Computational results demonstrate that the empirical performance of the algorithm surpasses its guaranteed performance. We also apply the same decomposition approach to develop a decomposition-based heuristic, which is much more efficient than the approximation algorithm in terms of computation time. Computational results also indicate that the efficacy of the decomposition-based heuristic in terms of solution quality is comparable to that of the approximation algorithm.

A generic decentralized trust management framework

SummaryThis paper describes TRUSTPACK, a decentralized trust management framework that provides trust management as a generic service. TRUSTPACK is unique in that it does not provide a central service. Instead, it is run by many autonomous services. This design enables TRUSTPACK to alleviate privacy concerns, as well as potentially provide better personalization and scalability when compared with current centralized solutions. A major component of TRUSTPACK is a generic decentralized graph query processing framework called GRAPHPACK, which was also developed as part of this work. GRAPHPACK consists of a decentralized graph processing language as well as an execution engine, as elaborated in this paper. The paper also presents several examples and a case study showing how TRUSTPACK can be used to handle various trust management scenarios, as well as its incorporation in an existing third party P2P file sharing application. Prototypes of TRUSTPACK and GRAPHPACK are available as open source projects at http://code.google.com/p/trustpack/ and http://code.google.com/p/graphpack/, respectively. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Horton+

Horton+ is a graph query processing system that executes declarative reachability queries on a partitioned attributed multi-graph. It employs a query language, query optimizer, and a distributed execution engine. The query language expresses declarative reachability queries, and supports closures and predicates on node and edge attributes to match graph paths. We introduce three algebraic operators, select, traverse, and join, and a query is compiled into an execution plan containing these operators. As reachability queries access the graph elements in a random access pattern, the graph is therefore maintained in the main memory of a cluster of servers to reduce query execution time. We develop a distributed execution engine that processes a query plan in parallel on the graph servers. Since the query language is declarative, we build a query optimizer that uses graph statistics to estimate predicate selectivity. We experimentally evaluate the system performance on a cluster of 16 graph servers using synthetic graphs as well as a real graph from an application that uses reachability queries. The evaluation shows (1) the efficiency of the optimizer in reducing query execution time, (2) system scalability with the size of the graph and with the number of servers, and (3) the convenience of using declarative queries.

QUBLE: towards blending interactive visual subgraph search queries on large networks

In a previous paper, we laid out the vision of a novel graph query processing paradigm where instead of processing a visual query graph after its construction, it interleaves visual query formulation and processing by exploiting the latency offered by the gui to filter irrelevant matches and prefetch partial query results [8]. Our recent attempts at implementing this vision [8, 9] show significant improvement in system response time (srt) for subgraph queries. However, these efforts are designed specifically for graph databases containing a large collection of small or medium-sized graphs. In this paper, we propose a novel algorithm called quble (QUery Blender for Large nEtworks) to realize this visual subgraph querying paradigm on very large networks (e.g., protein interaction networks, social networks). First, it decomposes a large network into a set of graphlets and supergraphlets using a minimum cut-based graph partitioning technique. Next, it mines approximate frequent and small infrequent fragments (sifs) from them and identifies their occurrences in these graphlets and supergraphlets. Then, the indexing framework of [9] is enhanced so that the mined fragments can be exploited to index graphlets for efficient blending of visual subgraph query formulation and query processing. Extensive experiments on large networks demonstrate effectiveness of quble.

Fast-On*: An Extended Algorithm for Graph Isomorphism Problem and Graph Query Processing

Fast-On*: An Extended Algorithm for Graph Isomorphism Problem and Graph Query Processing

Fast graph query processing with a low-cost index

This paper studies the problem of processing supergraph queries, that is, given a database containing a set of graphs, find all the graphs in the database of which the query graph is a supergraph. Existing works usually construct an index and performs a filtering-and-verification process, which still requires many subgraph isomorphism testings. There are also significant overheads in both index construction and maintenance. In this paper, we design a graph querying system that achieves both fast indexing and efficient query processing. The index is constructed by a simple but fast method of extracting the commonality among the graphs, which does not involve any costly operation such as graph mining. Our query processing has two key techniques, direct inclusion and filtering. Direct inclusion allows partial query answers to be included directly without candidate verification. Our filtering technique further reduces the candidate set by operating on a much smaller projected database. Experimental results show that our method is significantly more efficient than the existing works in both indexing and query processing, and our index has a low maintenance cost.

On graph query optimization in large networks

The dramatic proliferation of sophisticated networks has resulted in a growing need for supporting effective querying and mining methods over such large-scale graph-structured data. At the core of many advanced network operations lies a common and critical graph query primitive: how to search graph structures efficiently within a large network? Unfortunately, the graph query is hard due to the NP-complete nature of subgraph isomorphism. It becomes even challenging when the network examined is large and diverse. In this paper, we present a high performance graph indexing mechanism, SPath, to address the graph query problem on large networks. SPath leverages decomposed shortest paths around vertex neighborhood as basic indexing units, which prove to be both effective in graph search space pruning and highly scalable in index construction and deployment. Via SPath, a graph query is processed and optimized beyond the traditional vertex-at-a-time fashion to a more efficient path-at-a-time way: the query is first decomposed to a set of shortest paths, among which a subset of candidates with good selectivity is picked by a query plan optimizer; Candidate paths are further joined together to help recover the query graph to finalize the graph query processing. We evaluate SPath with the state-of-the-art GraphQL on both real and synthetic data sets. Our experimental studies demonstrate the effectiveness and scalability of SPath, which proves to be a more practical and efficient indexing method in addressing graph queries on large networks.