WeChat Mini Program
Old Version Features

Fast and Exact Subgraph Isomorphism Querying: Using Embedding and Searching Techniques

International Conference on Algorithms, Computing and Artificial Intelligence(2022)

Cited 0|Views14
Abstract
Subgraph isomorphism querying finds if there is any instance of a given pattern within the data graph, and has a wide applications in all fields related to graphs, such as databases, social network analysis, chemistry and biology. It is well-known that this task can be done by brute-force, but the complexity increases exponentially with the graph size and is proven an NP-hard problem. Many works have been done on accelerating subgraph isomorphism with the sacrifice of accuracy, most of which still suffer from the inherent difficulty, especially when the graph size get large. In this paper we propose a novel method, named SubQHS (Subgraph Querying based on Heuristic Search), which can quickly find an existing pattern in the data graph using heuristic. We also propose its variant SubQHS-T, which is guaranteed to terminate in polynomial time to get an approximate result.
More
Translated text
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined