Lun Du

Lun Du

Researcher at Microsoft Research

Posts by Collection

portfolio

publications

Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding.

Published in IJCAI, 2018

Network embedding, as an approach to learn low dimensional representations of vertices, has been proved extremely useful in many applications. Lots of state-of-the-art network embedding methods based on Skip-gram framework are efficient and effective. However, these methods mainly focus on the static network embedding and cannot naturally generalize to the dynamic environment. In this paper, we propose a stable dynamic embedding framework with high efficiency. It is an extension for the Skip-gram based network embedding methods, which can keep the optimality of the objective in the Skip-gram based methods in theory. Our model can not only generalize to the new vertex representation, but also update the most affected original vertex representations during the evolvement of the network. Multi-class classification on three real-world networks demonstrates that, our model can update the vertex representations efficiently and achieve the performance of retraining simultaneously. Besides, the visualization experimental result illustrates that, our model is capable of avoiding the embedding space drifting.

Recommended citation: L Du, Y Wang, G Song, Z Lu, J Wang. (2018). "Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding. " IJCAI: 2068-2092. https://www.ijcai.org/Proceedings/2018/288

Galaxy Network Embedding: A Hierarchical Community Structure Preserving Approach.

Published in IJCAI, 2018

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification and network visualization. The source code of GNE is available online.

Recommended citation: Du, L., Lu, Z., Wang, Y., Song, G., Wang, Y., & Chen, W. (2018). " Galaxy Network Embedding: A Hierarchical Community Structure Preserving Approach. " IJCAI: 2068-2092. https://www.ijcai.org/Proceedings/2018/287

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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