Abstract
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.
Highlights
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
Our contributions are described as follows: (1) We develop a flow graph index to pruning the noncandidates of query vertices on topological knowledge graph. e flow graph index is defined as a flow graph (FG), which is a directed multigraph, constructed from the initial data graph G0 and guided by a matching order of query graph
(5) We design an incremental subgraph matching algorithm based on the sequential subdivided flow graph. e consistency of subgraph matching is guaranteed by two verifications of selected candidates, relational verification and sequential verification. e relational and sequential verifications are used to verify the local isomorphism and the
Summary
Multigraph Characteristic of Knowledge Graph Intensifies the Complexity of Candidate Calculation. Knowledge graph is a directed labeled multigraph having multiple edges between a pair of vertices, each vertex represents an entity with attributes and each edge denotes an interentity relationship. Considering the model of knowledge multigraph, it is composed of attributed and topological structures. E attributed structure describes the attribute and type of entity, where attribute is taken as the label of edge coupled with a value and type is taken as the label of entity. E topological structure describes the relationship between a pair of entities and some relationships are coexistent, e.g., partnerships and couple relationship between persons. E multigraph characteristic of knowledge graph leads to a more dense adjacent structure than general graph, and it brings a new challenge to the research of KG-CSM problem. KG-CSM problem still contains the traditional challenge on general graph
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