Path Index-Enhanced Incremental Subgraph Matching Algorithm for Dynamic Graph

2021 ◽  
pp. 1879-1887
Author(s):  
Yunhao Sun ◽  
Guanyu Li ◽  
Bo Ning ◽  
Bing Han
Keyword(s):  
Dynamic Graph ◽  
Matching Algorithm ◽  
Subgraph Matching ◽  
Path Index

scholarly journals Optimized Distributed Subgraph Matching Algorithm Based on Partition Replication

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 184
Author(s):  
Ling Yuan ◽  
Jiali Bin ◽  
Peng Pan
Keyword(s):  
Large Scale ◽  
High Efficiency ◽  
Search Space ◽  
Dynamic Graph ◽  
Matching Algorithm ◽  
Query Graph ◽  
Subgraph Matching ◽  
Match Algorithm ◽  
Large Scale Data ◽  
Scale Data

At present, with the explosive growth of data scale, subgraph matching for massive graph data is difficult to satisfy with efficiency. Meanwhile, the graph index used in existing subgraph matching algorithm is difficult to update and maintain when facing dynamic graphs. We propose a distributed subgraph matching algorithm based on Partition Replica (noted as PR-Match) to process the partition and storage of large-scale data graphs. The PR-Match algorithm first splits the query graph into sub-queries, then assigns the sub-query to each node for sub-graph matching, and finally merges the matching results. In the PR-Match algorithm, we propose a heuristic rule based on prediction cost to select the optimal merging plan, which greatly reduces the cost of merging. In order to accelerate the matching speed of the sub-query graph, a vertex code based on the vertex neighbor label signature is proposed, which greatly reduces the search space for the subquery. As the vertex code is based on the increment, the problem that the feature-based graph index is difficult to maintain in the face of the dynamic graph is solved. An abundance of experiments on real and synthetic datasets demonstrate the high efficiency and strong scalability of the PR-Match algorithm when handling large-scale data graphs.

scholarly journals Subgraph-Indexed Sequential Subdivision for Continuous Subgraph Matching on Dynamic Knowledge Graph

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Yunhao Sun ◽  
Guanyu Li ◽  
Mengmeng Guan ◽  
Bo Ning
Keyword(s):  
Empirical Studies ◽  
Search Space ◽  
Knowledge Graph ◽  
Dynamic Graph ◽  
Matching Problem ◽  
Flow Graph ◽  
Query Graph ◽  
Subgraph Matching ◽  
Wide Range ◽  
Multiple Edges

Continuous subgraph matching problem on dynamic graph has become a popular research topic in the field of graph analysis, which has a wide range of applications including information retrieval and community detection. Specifically, given a query graph q , an initial graph G 0 , and a graph update stream △ G i , the problem of continuous subgraph matching is to sequentially conduct all possible isomorphic subgraphs covering △ G i of q on G i (= G 0   ⊕   △ G i ). Since knowledge graph is a directed labeled multigraph having multiple edges between a pair of vertices, it brings new challenges for the problem focusing on dynamic knowledge graph. One challenge is that the multigraph characteristic of knowledge graph intensifies the complexity of candidate calculation, which is the combination of complex topological and attributed structures. Another challenge is that the isomorphic subgraphs covering a given region are conducted on a huge search space of seed candidates, which causes a lot of time consumption for searching the unpromising candidates. To address these challenges, a method of subgraph-indexed sequential subdivision is proposed to accelerating the continuous subgraph matching on dynamic knowledge graph. Firstly, a flow graph index is proposed to arrange the search space of seed candidates in topological knowledge graph and an adjacent index is designed to accelerate the identification of candidate activation states in attributed knowledge graph. Secondly, the sequential subdivision of flow graph index and the transition state model are employed to incrementally conduct subgraph matching and maintain the regional influence of changed candidates, respectively. Finally, extensive empirical studies on real and synthetic graphs demonstrate that our techniques outperform the state-of-the-art algorithms.

scholarly journals The Index-Based Subgraph Matching Algorithm (ISMA): Fast Subgraph Enumeration in Large Networks Using Optimized Search Trees

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e61183 ◽  
Author(s):  
Sofie Demeyer ◽  
Tom Michoel ◽  
Jan Fostier ◽  
Pieter Audenaert ◽  
Mario Pickavet ◽  
...  
Keyword(s):  
Search Trees ◽  
Large Networks ◽  
Matching Algorithm ◽  
Subgraph Matching

Detection of Design Patterns in Software Design Model Using Graph

2013 ◽  
Vol 411-414 ◽  
pp. 559-562 ◽  
Author(s):  
Chalida Liamwiset ◽  
Vatanawood Wiwat
Keyword(s):  
Software Design ◽  
Design Patterns ◽  
Graph Matching ◽  
Design Model ◽  
Functional Requirements ◽  
Matching Algorithm ◽  
Subgraph Matching ◽  
Matching Technique ◽  
Uml Class Diagram ◽  
Local Properties

Detection of design patterns in software design phase possibly ensures the non-functional requirements, regarding performance features, before investing the implementation. We formalize the structural UML class diagram using graph. By applying graph matching technique, we propose an alternative of subgraph matching algorithm to extract the local properties of the UML class diagrams and perform the detecting of subgraph of possible design patterns found in the target software design model.

PBSM: An Efficient Top-K Subgraph Matching Algorithm

2018 ◽  
Vol 32 (06) ◽  
pp. 1850020
Author(s):  
Wei Chen ◽  
Jia Liu ◽  
Ziyang Chen ◽  
Xian Tang ◽  
Kaiyu Li
Keyword(s):  
Large Graphs ◽  
Research Issues ◽  
Matching Algorithm ◽  
Query Graph ◽  
Subgraph Matching ◽  
Minimum Number ◽  
Overall Performance ◽  
Data Graph ◽  
Memory Cost ◽  
Graph Data Management

Top-K subgraph matching is one of the hot research issues in graph data management, which is to find, from the data graph, K subgraphs isomorphic to the query graph with the largest sum of weights. The existing methods of Top-K subgraph matching on large graphs usually use the filter-and-verify strategy. However, they all suffer from inefficiency in both stages. In the filtering stage, there exists repeated enumeration of vertices and the excessive memory cost of the filtering. In the verification stage, there exists redundant verification. Regarding to the above problems, we propose to use the preprocessing of the graph compression based on equivalent vertices to reduce the enumeration. In the filtering stage, we propose to reduce the memory cost by only considering the direct neighbors. In the verification stage, we take the vertex with the minimum number of candidate vertices in the query graph as the start vertex of the matching order, and use the idea of Ranking While Matching (RWM) to terminate the execution of the algorithm as early as possible by estimating the upper bound of the weights, so as to reduce redundant verification and improve the overall performance. Finally, the experimental results show that our method is much more efficient than existing methods in compression and the processing time.

A subgraph matching algorithm based on subgraph index for knowledge graph

2021 ◽  
Vol 16 (3) ◽  
Author(s):  
Yunhao Sun ◽  
Guanyu Li ◽  
Jingjing Du ◽  
Bo Ning ◽  
Heng Chen
Keyword(s):  
Knowledge Graph ◽  
Matching Algorithm ◽  
Subgraph Matching

scholarly journals The Index-Based Subgraph Matching Algorithm with General Symmetries (ISMAGS): Exploiting Symmetry for Faster Subgraph Enumeration

PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e97896 ◽  
Author(s):  
Maarten Houbraken ◽  
Sofie Demeyer ◽  
Tom Michoel ◽  
Pieter Audenaert ◽  
Didier Colle ◽  
...  
Keyword(s):  
Matching Algorithm ◽  
Subgraph Matching

scholarly journals Symmetric continuous subgraph matching with bidirectional dynamic programming

2021 ◽  
Vol 14 (8) ◽  
pp. 1298-1310
Author(s):  
Seunghwan Min ◽  
Sung Gwan Park ◽  
Kunsoo Park ◽  
Dora Giammarresi ◽  
Giuseppe F. Italiano ◽  
...  
Keyword(s):  
Dynamic Programming ◽  
Cyber Security ◽  
Spanning Tree ◽  
State Of The Art ◽  
The State ◽  
Dynamic Graph ◽  
Matching Problem ◽  
Edge Deletion ◽  
Query Graph ◽  
Subgraph Matching

In many real datasets such as social media streams and cyber data sources, graphs change over time through a graph update stream of edge insertions and deletions. Detecting critical patterns in such dynamic graphs plays an important role in various application domains such as fraud detection, cyber security, and recommendation systems for social networks. Given a dynamic data graph and a query graph, the continuous subgraph matching problem is to find all positive matches for each edge insertion and all negative matches for each edge deletion. The state-of-the-art algorithm TurboFlux uses a spanning tree of a query graph for filtering. However, using the spanning tree may have a low pruning power because it does not take into account all edges of the query graph. In this paper, we present a symmetric and much faster algorithm SymBi which maintains an auxiliary data structure based on a directed acyclic graph instead of a spanning tree, which maintains the intermediate results of bidirectional dynamic programming between the query graph and the dynamic graph. Extensive experiments with real and synthetic datasets show that SymBi outperforms the state-of-the-art algorithm by up to three orders of magnitude in terms of the elapsed time.

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