Abstract
Heterogeneous Information Network (HIN) has been widely adopted in various tasks due to its excellence in modeling complex network data. To handle the additional attributes of nodes in HIN, the Attributed Heterogeneous Information Network (AHIN) was brought forward. Recently, clustering on HIN becomes a hot topic, since it is useful in many applications. Although existing semi-supervised clustering methods in HIN have achieved performance improvements to some extent, these models seldom consider the correlations among attributes which typically exist in real applications. To tackle this issue, we propose a novel model SCAN for semi-supervised clustering in AHIN. Our model captures the coupling relations between mixed types of node attributes and therefore obtains better attribute similarity. Moreover, we propose a flexible constraint method to leverage supervised information and network information for flexible adaption of different datasets and clustering objectives. Extensive experiments have shown that our model outperforms state-of-the-art algorithms.
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Acknowledgement
This work is supported by the National Key Research and Development Program of China (2017YFB0803304), the National Natural Science Foundation of China (No. 61772082, 61806020, 61702296) the Beijing Municipal Natural Science Foundation (4182043), the CCF-Tencent Open Fund and the Fundamental Research Funds for the Central Universities.
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Zhao, J., Xiao, D., Hu, L., Shi, C. (2019). Coupled Semi-supervised Clustering: Exploring Attribute Correlations in Heterogeneous Information Networks. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11641. Springer, Cham. https://doi.org/10.1007/978-3-030-26072-9_7
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