Graph pattern matching is typically defined in terms of subgraph isomorphism, which makes it an np-complete problem. Moreover, it requires bijective functions, which are often too restrictive to characterize patterns in emerging applications. We propose a class of graph patterns, in which an edge denotes the connectivity in a data graph within a predefined number of hops. In addition, we define matching based on a notion of bounded simulation, an extension of graph simulation. We show that with this revision, graph pattern matching can be performed in cubic-time, by providing such an algorithm. We also develop algorithms for incrementally finding matches when data graphs are updated, with performance guarantees for dag patterns. We experimentally verify that these algorithms scale well, and that the revised notion of graph pattern matching allows us to identify communities commonly found in real-world networks.

Graph Pattern Matching.

This volume contains a selection of the papers presented at all Workshops and DoctoralConsortium of the 24th East-European Conference on Advances in Databases andInformation Systems (ADBIS 2020), the 24th International Conference on Theory andPractice of Digital Libraries (TPDL 2020), and the 16th Workshop on BusinessIntelligence and Big Data (EDA 2020), held during August 25–27, 2020, in Lyon,France.

ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium : International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, Lyon, France, August 25–27, 2020 : Proceedings

In this paper, we address the problem of efficiently finding homomorphic matches for hybrid patterns over large data graphs. Finding matches for patterns in data graphs is of fundamental importance for graph analytics. In hybrid patterns, each edge may correspond either to an edge or a path in the data graph, thus allowing for higher expressiveness and flexibility in query formulation. We introduce the concept of answer graph to compactly represent the query results and exploit computation sharing. We design a holistic bottom-up algorithm called GPM, which greatly reduces the number of intermediate results, leading to significant performance gains. GPM directly processes child constraints in the given query instead of resorting to a post-processing procedure. An extensive experimental evaluation using both real and synthetic datasets shows that our methods evaluate hybrid patterns up to several orders of magnitude faster than existing algorithms and exhibit much better scalability.

Efficiently Computing Homomorphic Matches of Hybrid Pattern Queries on Large Graphs

Prior algorithms on graph simulation for distributed graphs are not scalable enough as they exhibit heavy message passing. Moreover, they are dependent on the graph partitioning quality that can be a bottleneck due to the natural skew present in real-world data. As a result, their degree of parallelism becomes limited. In this paper, we propose an efficient parallel edge-centric approach for distributed graph pattern matching. We design a novel distributed data structure called ST that allows a fine-grain parallelism, and hence guarantees linear scalability. Based on ST, we develop a parallel graph simulation algorithm called PGSim. Furthermore, we propose PDSim, an edge-centric algorithm that efficiently evaluates dual simulation in parallel. PDSim combines ST and PGSim in a Split-and-Combine approach to accelerate the computation stages. We prove the effectiveness and efficiency of these propositions through theoretical guarantees and extensive experiments on massive graphs. The achieved results confirm that our approach outperforms existing algorithms by more than an order of magnitude.

/pdf/efficient-parallel-edge-centric-approach-for-relaxed-graph-vie8ziosii.pdf

Efficient parallel edge-centric approach for relaxed graph pattern matching

This paper focuses on a domain expert querying system over databases. It presents a solution designed for a French enterprise interested in offering a natural language interface for its clients. The approach, based on entity enrichment, aims at translating natural language queries into database queries. In this paper, the database is treated through a logical paradigm, suggesting the adaptability of our approach to different database models. The good precision of our method is shown through some preliminary experiments.

pdf/natural-language-querying-system-through-entity-enrichment-3f5xebu95y.pdf

Natural Language Querying System Through Entity Enrichment

Developing efficient and scalable techniques for pattern queries over large graphs is crucial for modern applications such as social networks, Web analysis, and bioinformatics. In this paper, we address the problem of efficiently finding the homomorphic matches for tree pattern queries with child and descendant edges (mixed pattern queries) over a large data graph. We propose a novel type of materialized views to accelerate the evaluation. Our materialized views are the sets of occurrence lists of the nodes of the pattern in the data graph. They are stored as compressed bitmaps on the inverted lists of the node labels in the data graph. Reachability information between occurrence list nodes is provided by a node reachability index. This technique not only minimizes the materialization space but also reduces CPU and I/O costs by translating view materialization processing into bitwise operations. We provide conditions for view usability using the concept of pattern node coverage. We design a holistic bottom-up algorithm which efficiently computes pattern query matches in the data graph using bitmap views. An extensive experimental evaluation shows that our method evaluates mixed patterns up to several orders of magnitude faster than existing algorithms.

Evaluating Mixed Patterns on Large Data Graphs Using Bitmap Views

Scientific impact of publications is often measured using citation networks. However, traditional measures typically rely on direct citations only. To fully leverage citation networks for assessing scientific impact, it is necessary to investigate also indirect scientific influence, which is captured by citation paths. Further, the analysis and exploration of citation networks requires the ability to efficiently evaluate expressive queries on them. In this paper, we propose to use hybrid query patterns to query citation networks. These allow for both edge-to-edge and edge-to-path mappings between the query pattern and the graph, thus being able to extract both direct and indirect relationships. To efficiently evaluate hybrid pattern queries on citation graphs, we employ a pattern matching algorithm which exploits graph simulation to prune nodes that do not appear in the final answer. Our experimental results on citation networks show that our method not only allows for more expressive queries but is also efficient and scalable.

Exploring Citation Networks with Hybrid Tree Pattern Queries

Graphs are a widely used data model in modern data-intensive applications. Graph pattern matching is a fundamental operation for the exploration and analysis of large data graphs. In this paper, we present a novel approach for efficiently finding homomorphic matches of graph pattern queries, where pattern edges denote reachability relationships between nodes in the data graph. We first propose the concept of query reachability graph to compactly encode all the possible homomorphisms from a query pattern to the data graph. Then, we design a graph traversal-based filtering method to prune nodes from the data graph which violate reachability conditions induced by the pattern edges. We use the pruned data graph to generate a refined query reachability graph which serves as a compact search space for the pattern query answer. Finally, we design a multiway join algorithm to enumerate answer tuples from the query reachability graph without generating an excessive number of redundant intermediate results (a drawback of previous approaches). We experimentally verify the efficiency of our approach and demonstrate that it outperforms by far existing approaches and a recent graph DBMS on evaluating reachability graph pattern queries.KeywordsGraph pattern matchingEdge-to-path homomorphismMulti-way join 

Efficient In-Memory Evaluation of Reachability Graph Pattern Queries on Data Graphs

We address the problem of evaluating graph pattern queries involving reachability (edge-to-path mapping) and direct (edge-to-edge mapping) relationships under homomorphisms on data graphs using materialized graph pattern views. We propose an original approach for view materialization which materializes views as summary graphs, an approach that records, in a compact way, all the homomorphisms of the view to the data graph. In this context, we characterize view usability in terms of query edge coverage and provide necessary and sufficient conditions for answering queries using views. We design algorithms for deciding whether a query can be answered using a set of views, for generating the summary graph of a query from the view materializations, and for producing a minimal view set capable of answering a query. Our experimental evaluation demonstrates that our approach outperforms, by several orders of magnitude, a state-of-the-art approach which does not use materialized views, and substantially improves upon its scalability. Queries using Compact Materialized Views. In Proceedings

Answering Graph Pattern Queries using Compact Materialized Views

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