Abstract
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.
Highlights
A graph is a semi-structured data represented by vertices and edges, which is usually represented as G (V, E), where V represents the set of vertices and E the set of edges between vertices
We propose a distributed subgraph matching algorithm based on Partition Replica to process the subgraph matching of large-scale data graph
PR-Match algorithm, we design a large-scale data graph partition and storage scheme based on the theory of equilibrium separation of large graphs, develop a high efficient vertex code index to process fast updating and maintenance on dynamic graphs, and establish the heuristic rules based on the prediction overhead to determine the merging sequence of subquery matching results
Summary
A graph is a semi-structured data represented by vertices and edges, which is usually represented as G (V, E), where V represents the set of vertices and E the set of edges between vertices. Existing distributed subgraph matching mainly uses an RDF graph engine and map-reduce computing framework, which can hardly achieve satisfying efficiency. To solve these problems, we propose a distributed subgraph matching algorithm based on Partition Replica (noted as PR-Match) to process the subgraph matching of large-scale data graph. PR-Match algorithm, we design a large-scale data graph partition and storage scheme based on the theory of equilibrium separation of large graphs, develop a high efficient vertex code index to process fast updating and maintenance on dynamic graphs, and establish the heuristic rules based on the prediction overhead to determine the merging sequence of subquery matching results.
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