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【www.7610.com】Attributed Network Embedding via Subspace Discovery

www.7610.com:2020-01-13    编辑:     来源:     点击:

报告时间:2020年1月15日 9:00


报告题目:Attributed Network Embedding via Subspace Discovery


报告摘要: Network embedding aims to learn a latent, low-dimensional vector representations of network nodes, effective in supporting various network analytic tasks. While prior arts on network embedding focus primarily on preserving network topology structure to learn node representations, recently proposed attributed network embedding algorithms attempt to integrate rich node content information with network topological structure for enhancing the quality of network embedding. In reality, networks often have sparse content, incomplete node attributes, as well as the discrepancy between node attribute feature space and network structure space, which severely deteriorates the performance of existing methods. In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space. The resultant latent subspace can respect network structure in a more consistent way towards learning high-quality node representations. We formulate an optimization problem which is solved by an efficient stochastic gradient descent algorithm, with linear time complexity to the number of nodes. We investigate a series of linear and non-linear transformations performed on node attributes and empirically validate their effectiveness on various types of networks. Another advantage of attri2vec is its ability to solve out-of-sample problems, where embeddings of new coming nodes can be inferred from their node attributes through the learned mapping function. Experiments on various types of networks confirm that attri2vec is superior to state-of-the-art baselines for node classification, node clustering, as well as out-of-sample link prediction tasks.

报告人概况: Dr Daokun Zhang is a Postdoctoral Research Associate at the Discipline of Business Analytics at the University of Sydney Business School. He is working on the research of machine learning on graphs and its application.Daokun Zhang received the PhD degree from the Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney in September 2019. As the first author, he has published several papers in top conferences/journals, such as ACM International Conference on Information and Knowledge Management, IEEE International Conference on Data Mining, International Joint Conference on Artificial Intelligence, IEEE Transactions on Big Data, Data Mining and Knowledge Discovery journal. He is also a program committee member of the IEEE International Conference on Data Mining (2019 and 2020), and International Joint Conference on Artificial Intelligence (2020).


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