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Deep Network Embedding with Aggregated Proximity Preserving

Published: 31 July 2017 Publication History

Abstract

Network embedding is an effective method to learn a low-dimensional feature vector representation for each node of a given network. In this paper, we propose a deep network embedding model with aggregated proximity preserving (DNE-APP). Firstly, an overall network proximity matrix is generated to capture both local and global network structural information, by aggregating different k-th order network proximities between different nodes. Then, a semi-supervised stacked auto-encoder is employed to learn the hidden representations which can best preserve the aggregated proximity in the original network, and also map the node pairs with higher proximity closer to each other in the embedding space. With the hidden representations learned by DNE-APP, we apply vector-based machine learning techniques to conduct node classification and link label prediction tasks on the real-world datasets. Experimental results demonstrate the superiority of our proposed DNE-APP model over the state-of-the-art network embedding algorithms.

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  1. Deep Network Embedding with Aggregated Proximity Preserving

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      Published In

      cover image ACM Conferences
      ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017
      July 2017
      698 pages
      ISBN:9781450349932
      DOI:10.1145/3110025
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      Publication History

      Published: 31 July 2017

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      Author Tags

      1. Network embedding
      2. graph representation
      3. network proximity
      4. semi-supervised
      5. stacked auto-encoder

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      • Short-paper
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      • Refereed limited

      Funding Sources

      • PolyU project
      • Hong Kong PhD Fellowship Scheme

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      ASONAM '17
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      Overall Acceptance Rate 116 of 549 submissions, 21%

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      • (2023)Learning asymmetric embedding for attributed networks via convolutional neural networkExpert Systems with Applications10.1016/j.eswa.2023.119659219(119659)Online publication date: Jun-2023
      • (2023)Label-Aware Hierarchical Contrastive Domain Adaptation for Cross-Network Node ClassificationAdvanced Data Mining and Applications10.1007/978-3-031-46671-7_13(183-198)Online publication date: 5-Nov-2023
      • (2022)LARW: Network Representation Learning Algorithm Based On Long Anonymous Random WalksProceedings of the 4th International Conference on Advanced Information Science and System10.1145/3573834.3574491(1-4)Online publication date: 25-Nov-2022
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      • (2021)Learning Graph Neural Networks with Positive and Unlabeled NodesACM Transactions on Knowledge Discovery from Data10.1145/345031615:6(1-25)Online publication date: 28-Jun-2021
      • (2021)Network Together: Node Classification via Cross-Network Deep Network EmbeddingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.299548332:5(1935-1948)Online publication date: May-2021
      • (2020)Unsupervised Domain Adaptive Graph Convolutional NetworksProceedings of The Web Conference 202010.1145/3366423.3380219(1457-1467)Online publication date: 20-Apr-2020
      • (2020)Deep Network Embedding for Graph Representation Learning in Signed NetworksIEEE Transactions on Cybernetics10.1109/TCYB.2018.287150350:4(1556-1568)Online publication date: Apr-2020
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