Personalized Query Techniques in Graphs: A Survey
Information sciences（2022）
Hunan Univ
Abstract
Graph is a famous data structure that has prevalent applications in the real world, including social networks, biological networks, and computer networks. In these applications, graph management operators are powerful tools for mining important information hidden in large-scale graphs. As important graph data management operators, personalized graph queries are playing an increasingly significant role in providing users with effective decision support. In particular, the purpose of personalized graph queries is to compute personalized results which can meet the preferences of different users from the three aspects of specified query vertices, structures, and attributes. In this paper, we conduct a survey to offer a comprehensive view of the current personalized graph queries which need users to specify query vertices over simple and attributed graphs, respectively. These queries fall into three categories, including point-related, path-related, and subgraph-related graph queries, whose query results have distinct structures. We analyze existing approaches to personalized graph queries and highlight current challenges. In addition, we also offer guidelines for future graph queries.
MoreTranslated text
Key words
Community search,Graph search,Keyword query,Reachability query,Similarity query,Shortest path query
求助PDF
上传PDF
View via Publisher
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