Abstract
Similarity search on directed social networks (DSNs) could help users find the K 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.
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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).
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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
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