A paper presented at IEEE BigData2022

A paper presented at IEEE BigData2022

Created
November 15, 2022
Tags
PaperCross-modal
Updated
November 15, 2022

We are excited to announce that our paper “Fast binary network hashing via graph clustering” has been accepted to IEEE BigData 2022 as a poster paper.

Yasuhiro Fujiwara, Masahiro Nakano, Atsutoshi Kumagai, Yasutoshi Ida, Akisato Kimura, Naonori Ueda, “Fast binary network hashing via graph clustering,” IEEE International Conference on Big Data, 2022.

Network hashing converts each node of a graph into a compact binary code, and it is a useful graph analytics tool since it can reduce memory cost.

INH-MF [Lian+ KDD2018] is a network hashing approach to factorize the high-order proximity matrix representing similarities between nodes. However, it ignores clusters in the graph where densely connected nodes share common inherent properties. Besides, it cuts small nonzero elements from the proximity matrix. Therefore, it fails to effectively extract insights from the graph. Moreover, it incurs high memory and computational costs since the proximity matrix is large and dense.

We propose Graph Clustering-based Network Hashing, an efficient network hashing approach. To compute the proximities effectively, it uses the structural relationships between nodes and clusters obtained from a graph clustering approach. Moreover, it can efficiently compute hash codes from eigenvectors of the matrix corresponding to the graph Laplacian by exploiting its low-rank property. Experiments show that it can more efficiently and effectively compute hash codes than previous approaches.

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The paper is available at IEEE Xplore.