Attention-Based Graph Summarization for Large-Scale Information Retrieval

被引:1
作者
Shabani, Nasrin [1 ]
Beheshti, Amin [1 ]
Jolfaei, Alireza [2 ]
Wu, Jia [1 ]
Haghighi, Venus [1 ]
Najafabadi, Maryam Khanian [3 ]
Foo, Jin [1 ]
机构
[1] Macquarie Univ, Sch Comp, Macquarie Pk, NSW 2109, Australia
[2] Flinders Univ S Australia, Coll Sci & Engn, Adelaide, SA 5042, Australia
[3] Univ Sydney, Sch Comp Sci, Sydney, NSW 2050, Australia
基金
澳大利亚研究理事会;
关键词
Task analysis; Scalability; Information retrieval; Knowledge graphs; Navigation; Visualization; User experience; Attention mechanism; graph summarization; information retrieval; variational graph autoencoders; MODEL;
D O I
10.1109/TCE.2024.3411993
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Efficiently processing large-scale graphs for information retrieval tasks presents a formidable hurdle, demanding innovative solutions for enhancing user experiences. This paper introduces a framework that merges attention-based graph summarization with state-of-the-art graph sampling methods tailored explicitly for large-scale graph processing and information retrieval applications, all aimed at enriching user experiences. Our approach distinguishes itself through its adeptness in efficiently handling vast graph datasets, leveraging robust sampling techniques and attention mechanisms to enhance feature extraction. Central to our methodology is the utilization of graph summarization techniques, which focus on distilling pertinent information, thereby enhancing both the accuracy and computational efficiency of information retrieval and recommendation tasks. Through practical demonstrations, notably within academic databases, our framework showcases its effectiveness in real-world scenarios, offering a significant advancement in the realm of personal technology data management and information retrieval systems.
引用
收藏
页码:6224 / 6235
页数:12
相关论文
共 33 条
[1]  
Bojchevski A, 2018, PR MACH LEARN RES, V80
[2]   Graph clustering using k-Neighbourhood Attribute Structural similarity [J].
Boobalan, M. Parimala ;
Lopez, Daphne ;
Gao, X. Z. .
APPLIED SOFT COMPUTING, 2016, 47 :216-223
[3]  
Brody S, 2022, Arxiv, DOI [arXiv:2105.14491, DOI 10.48550/ARXIV.2105.14491]
[4]   Summarizing semantic graphs: a survey [J].
Cebiric, Sejla ;
Goasdoue, Francois ;
Kondylakis, Haridimos ;
Kotzinos, Dimitris ;
Manolescu, Ioana ;
Troullinou, Georgia ;
Zneika, Mussab .
VLDB JOURNAL, 2019, 28 (03) :295-327
[5]   Deep Self-Supervised Graph Attention Convolution Autoencoder for Networks Clustering [J].
Chen, Chao ;
Lu, Hu ;
Hong, Haotian ;
Wang, Hai ;
Wan, Shaohua .
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2023, 69 (04) :974-983
[6]   Approximate Querying on Property Graphs [J].
Dumbrava, Stefania ;
Bonifati, Angela ;
Diaz, Amaia Nazabal Ruiz ;
Vuillemot, Romain .
SCALABLE UNCERTAINTY MANAGEMENT, SUM 2019, 2019, 11940 :250-265
[7]   Deep Graph Generators: A Survey [J].
Faez, Faezeh ;
Ommi, Yassaman ;
Baghshah, Mahdieh Soleymani ;
Rabiee, Hamid R. .
IEEE ACCESS, 2021, 9 :106675-106702
[8]   Graph based model for information retrieval using a stochastic local search [J].
Farhi, Sidali Hocine ;
Boughaci, Dalila .
PATTERN RECOGNITION LETTERS, 2018, 105 :234-239
[9]  
Gibson D., 2005, P 31 INT C VER LARG, P721
[10]   node2vec: Scalable Feature Learning for Networks [J].
Grover, Aditya ;
Leskovec, Jure .
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2016, :855-864