Abstract
As an effective approach to solve graph mining problems, network embedding aims to learn low-dimensional latent representation of nodes in a network. We develop a representation learning method called GNE for generic heterogeneous information networks to learn the vertex representations for generic HINs. Greatly different from previous works, our model consists two components. First, GNE assigns the probability of each random walk step according to vertex centrality, weight of relations and structural similarity for neighbors on premise of performing a biased self-adaptive random walk generator. Second, to learn more desirable representations for generic HINs, we then design an advanced joint optimization framework by accounting for both the explicit (1st-order) relations and implicit (higher-order) relations.
C. Kong and B. Chen—Contributed equally to this work.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China Youth Fund under Grant No. 61902001 and Initial Scientific Research Fund of Introduced Talents in Anhui Polytechnic University under Grant No. 2017YQQ015.
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Kong, C., Chen, B., Li, S., Chen, Y., Chen, J., Zhang, L. (2020). GNE: Generic Heterogeneous Information Network Embedding. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_11
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DOI: https://doi.org/10.1007/978-3-030-60029-7_11
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