Abstract
Community detection in complex networks can find the community structure one of the most important properties of complex networks. Nodes in the same community have more dense connections than those in different communities, which can be utilized to analyze the function of complex networks. In addition, heterogeneous networks are ubiquitous in the real world. For example, academic networks have different types of nodes such as authors, papers, and conferences. Network representation learning is an important method to discover the complex nonlinear relationships between nodes in the network, which is of great help to community detection. Attention network is a typical network representation learning method, and it will pay attention to the important part in the network for the specific task. However, most existing heterogeneous NRL algorithms use metapaths to capture heterogeneous information, which requires prior knowledge to set metapaths in advance. This paper proposes a novel random-walk-based heterogeneous attention network (RHAN) for community detection on heterogeneous networks. Random walk is used to generate the neighbor nodes set of nodes, and heterogeneous information is considered by the intra-type attention and the inter-type attention, which is no need for metapaths. The experimental results on four widely used heterogeneous networks verify the effectiveness of RHAN.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grant No. 61672159, No. 61672158, No. 61300104 and No. 62002063, the RGC Theme-based Research Scheme [T41-603/20-R], the Fujian Collaborative Innovation Center for Big Data Applications in Governments, the Fujian Industry-Academy Cooperation Project under Grant No. 2017H6008 and No. 2018H6010, the Natural Science Foundation of Fujian Province under Grant No. 2018J07005, No. 2019J01835, No. 2020J05112 and No. 2020J01494, the Fujian Provincial Department of Education under Grant No. JAT190026, the Fuzhou University under Grant 510872/GXRC-20016 and Haixi Government Big Data Application Cooperative Innovation Center.
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Zhang, P., Guo, K., Wu, L. (2022). A Random-Walk-Based Heterogeneous Attention Network for Community Detection. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1492. Springer, Singapore. https://doi.org/10.1007/978-981-19-4549-6_15
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