Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 13362))
Included in the following conference series:
1522Accesses
Abstract
Similarity search on directed social networks (DSNs) could help users find theK nearest neighbors (KNNs). The graph index based similarity search does not have to compare query node against every node in DSNs, since the neighbor relationship of the nodes is captured by the edges. Nevertheless, the performance of similarity search is still unsatisfactory, such as not supporting the end-to-end search or taking unnecessary detours, etc. In this paper, we propose the method of Graph Index on Directed Social Network Embedding (GI-DSNE) to facilitate the approximate KNN search on DSNs. First, the DSNE is proposed to embed the DSN into a low-dimensional vector space to achieve the embeddings for efficient calculation of similarities on the search path. Then, the nearest neighbor descent algorithm is adopted to calculate the KNN graph. Subsequently, to construct the graph index efficiently, the direction guided strategy for edge selection, maximum out-degree of GI-DSNE and the depth-first-search tree for guaranteeing the connectivity of GI-DSNE are proposed. Experimental results show that our proposed method outperforms the state-of-the-art competitors on both execution time and precision.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 9723
- Price includes VAT (Japan)
- Softcover Book
- JPY 12154
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, G., Xu, C., Wang, J., Feng, J., Feng, J.: Nonnegative matrix factorization for link prediction in directed complex networks using PageRank and asymmetric link clustering information. Expert Syst. Appl.148, 113290 (2020)
Dong, W., Charikar, M., Li, K.: Efficient k-nearest neighbor graph construction for generic similarity measures. In: Proceedings of the 20th International Conference on World Wide Web, pp. 577–586 (2011)
Fu, C., Wang, C., Cai, D.: High dimensional similarity search with satellite system graph: efficiency, scalability, and unindexed query compatibility. IEEE Trans. Pattern Anal. Mach. Intell., 1 (2021)
Fu, C., Xiang, C., Wang, C., Cai, D.: Fast approximate nearest neighbor search with the navigating spreading-out graphs. PVLDB12(5), 461–474 (2019)
Harwood, B., Drummond, T.: FANNG: fast approximate nearest neighbour graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5713–5722 (2016)
Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. In: Advances in Neural Information Processing Systems, vol. 27, pp. 2177–2185 (2014)
Liu, X., Murata, T., Kim, K.S., Kotarasu, C., Zhuang, C.: A general view for network embedding as matrix factorization. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 375–383 (2019)
Malkov, Y., Ponomarenko, A., Logvinov, A., Krylov, V.: Approximate nearest neighbor algorithm based on navigable small world graphs. Inf. Syst.45, 61–68 (2014)
Malkov, Y.A., Yashunin, D.A.: Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE Trans. Pattern Anal. Mach. Intell.42(4), 824–836 (2020)
Naidan, B., Boytsov, L., Nyberg, E.: Permutation search methods are efficient, yet faster search is possible. PVLDB8(12), 1618–1629 (2015)
Paredes, R., Chávez, E.: Using thek-nearest neighbor graph for proximity searching in metric spaces. In: Consens, M., Navarro, G. (eds.) SPIRE 2005. LNCS, vol. 3772, pp. 127–138. Springer, Heidelberg (2005).https://doi.org/10.1007/11575832_14
Qi, Z., Yue, K., Duan, L., Wang, J., Qiao, S., Fu, X.: Matrix factorization based Bayesian network embedding for efficient probabilistic inferences. Expert Syst. Appl.169, 114294 (2021)
Shim, C., Kim, W., Heo, W., Yi, S., Chung, Y.D.: Nearest close friend search in geo-social networks. Inf. Sci.423, 235–256 (2018)
Shimomura, L.C., Oyamada, R.S., Vieira, M.R., Kaster, D.S.: A survey on graph-based methods for similarity searches in metric spaces. Inf. Syst.95, 101507 (2021)
Symeonidis, P.: Similarity search, recommendation and explainability over graphs in different domains: social media, news, and health industry. In: Brambilla, M., Chbeir, R., Frasincar, F., Manolescu, I. (eds.) ICWE 2021. LNCS, vol. 12706, pp. 537–541. Springer, Cham (2021).https://doi.org/10.1007/978-3-030-74296-6_46
Toussaint, G.T.: The relative neighbourhood graph of a finite planar set. Pattern Recogn.12(4), 261–268 (1980)
Wang, M., Xu, X., Yue, Q., Wang, Y.: A comprehensive survey and experimental comparison of graph-based approximate nearest neighbor search. PVLDB14(11), 1964–1978 (2021)
Zheng, B., et al.: Towards a distributed local-search approach for partitioning large-scale social networks. Inf. Sci.508, 200–213 (2020)
Acknowledgments
This paper was supported by the National Natural Science Foundation of China (U1802271, 62002311), Program of Key Lab of Intelligent Systems and Computing of Yunnan Province, Yunnan University, Science Foundation for Distinguished Young Scholars of Yunnan Province (2019FJ011), Major Project of Science and Technology of Yunnan Province (202002AD080002-1-B), Fundamental Research Project of Yunnan Province (202001BB050052).
Author information
Authors and Affiliations
School of Information Science and Engineering, Yunnan University, Kunming, China
Zhiwei Qi, Kun Yue & Liang Duan
Key Lab of Intelligent Systems and Computing of Yunnan Province, Yunnan University, Kunming, China
Zhiwei Qi, Kun Yue & Liang Duan
School of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, China
Zhihong Liang
- Zhiwei Qi
You can also search for this author inPubMed Google Scholar
- Kun Yue
You can also search for this author inPubMed Google Scholar
- Liang Duan
You can also search for this author inPubMed Google Scholar
- Zhihong Liang
You can also search for this author inPubMed Google Scholar
Corresponding author
Correspondence toKun Yue.
Editor information
Editors and Affiliations
Polytechnic University of Bari, Bari, Italy
Tommaso Di Noia
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea (Republic of)
In-Young Ko
Johannes Kepler University Linz, Linz, Austria
Markus Schedl
Polytechnic University of Bari, Bari, Italy
Carmelo Ardito
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Qi, Z., Yue, K., Duan, L., Liang, Z. (2022). Similarity Search with Graph Index on Directed Social Network Embedding. In: Di Noia, T., Ko, IY., Schedl, M., Ardito, C. (eds) Web Engineering. ICWE 2022. Lecture Notes in Computer Science, vol 13362. Springer, Cham. https://doi.org/10.1007/978-3-031-09917-5_6
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-031-09916-8
Online ISBN:978-3-031-09917-5
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative