Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching

• URL: http://arxiv.org/abs/2201.11251v1
• Date: Tue, 25 Jan 2022 00:10:03 GMT
• Title: Reinforcement Learning Based Query Vertex Ordering Model for Subgraph Matching
• Authors: Hanchen Wang, Ying Zhang, Lu Qin, Wei Wang, Wenjie Zhang, Xuemin Lin
• Abstract summary: Subgraph matching algorithms enumerate all is embeddings of a query graph in a data graph G. matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms.
• Score: 58.39970828272366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Subgraph matching is a fundamental problem in various fields that use graph structured data. Subgraph matching algorithms enumerate all isomorphic embeddings of a query graph q in a data graph G. An important branch of matching algorithms exploit the backtracking search approach which recursively extends intermediate results following a matching order of query vertices. It has been shown that the matching order plays a critical role in time efficiency of these backtracking based subgraph matching algorithms. In recent years, many advanced techniques for query vertex ordering (i.e., matching order generation) have been proposed to reduce the unpromising intermediate results according to the preset heuristic rules. In this paper, for the first time we apply the Reinforcement Learning (RL) and Graph Neural Networks (GNNs) techniques to generate the high-quality matching order for subgraph matching algorithms. Instead of using the fixed heuristics to generate the matching order, our model could capture and make full use of the graph information, and thus determine the query vertex order with the adaptive learning-based rule that could significantly reduces the number of redundant enumerations. With the help of the reinforcement learning framework, our model is able to consider the long-term benefits rather than only consider the local information at current ordering step.Extensive experiments on six real-life data graphs demonstrate that our proposed matching order generation technique could reduce up to two orders of magnitude of query processing time compared to the state-of-the-art algorithms.

Related papers

• Ensemble Quadratic Assignment Network for Graph Matching [52.20001802006391]
  Graph matching is a commonly used technique in computer vision and pattern recognition. Recent data-driven approaches have improved the graph matching accuracy remarkably. We propose a graph neural network (GNN) based approach to combine the advantages of data-driven and traditional methods.
  arXiv  Detail & Related papers  (2024-03-11T06:34:05Z)
• ProvG-Searcher: A Graph Representation Learning Approach for Efficient Provenance Graph Search [13.627536649679577]
  We present ProvG-Searcher, a novel approach for detecting known APT behaviors within system security logs. We formulate the task of searching provenance graphs as a subgraph matching problem and employ a graph representation learning method. Experimental results on standard datasets demonstrate that ProvG-Searcher achieves superior performance, with an accuracy exceeding 99% in detecting query behaviors.
  arXiv  Detail & Related papers  (2023-09-07T11:29:01Z)
• Subgraph Matching via Query-Conditioned Subgraph Matching Neural Networks and Bi-Level Tree Search [33.9052190473029]
  Subgraph Matching is a core operation in graph database search, biomedical analysis, social group finding, etc. In this paper, we propose a novel encoder-decoder neural network architecture to dynamically compute the matching information between the query and the target graphs. Experiments on five large real-world target graphs show that N-BLS can significantly improve the subgraph matching performance.
  arXiv  Detail & Related papers  (2022-07-21T04:47:21Z)
• Sublinear Algorithms for Hierarchical Clustering [14.124026862687941]
  We study hierarchical clustering for massive graphs under three well-studied models of sublinear computation. We design sublinear algorithms for hierarchical clustering in all three models.
  arXiv  Detail & Related papers  (2022-06-15T16:25:27Z)
• Deep Probabilistic Graph Matching [72.6690550634166]
  We propose a deep learning-based graph matching framework that works for the original QAP without compromising on the matching constraints. The proposed method is evaluated on three popularly tested benchmarks (Pascal VOC, Willow Object and SPair-71k) and it outperforms all previous state-of-the-arts on all benchmarks.
  arXiv  Detail & Related papers  (2022-01-05T13:37:27Z)
• Effective and efficient structure learning with pruning and model averaging strategies [9.023722579074734]
  This paper describes an approximate BN structure learning algorithm that combines two novel strategies with hill-climbing search. The algorithm starts by pruning the search space graphs, where the pruning strategy can be viewed as an aggressive version of the pruning strategies. It then performs model averaging in the hill-climbing search process and moves to the neighbouring graph that maximises the objective function.
  arXiv  Detail & Related papers  (2021-12-01T10:35:34Z)
• Joint Graph Learning and Matching for Semantic Feature Correspondence [69.71998282148762]
  We propose a joint emphgraph learning and matching network, named GLAM, to explore reliable graph structures for boosting graph matching. The proposed method is evaluated on three popular visual matching benchmarks (Pascal VOC, Willow Object and SPair-71k) It outperforms previous state-of-the-art graph matching methods by significant margins on all benchmarks.
  arXiv  Detail & Related papers  (2021-09-01T08:24:02Z)
• Second-Order Pooling for Graph Neural Networks [62.13156203025818]
  We propose to use second-order pooling as graph pooling, which naturally solves the above challenges. We show that direct use of second-order pooling with graph neural networks leads to practical problems. We propose two novel global graph pooling methods based on second-order pooling; namely, bilinear mapping and attentional second-order pooling.
  arXiv  Detail & Related papers  (2020-07-20T20:52:36Z)
• Graph Ordering: Towards the Optimal by Learning [69.72656588714155]
  Graph representation learning has achieved a remarkable success in many graph-based applications, such as node classification, prediction, and community detection. However, for some kind of graph applications, such as graph compression and edge partition, it is very hard to reduce them to some graph representation learning tasks. In this paper, we propose to attack the graph ordering problem behind such applications by a novel learning approach.
  arXiv  Detail & Related papers  (2020-01-18T09:14:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.