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Integrating Multi-Network Topology via Deep Semi-supervised Node Embedding

Published: 03 November 2019 Publication History

Abstract

Node Embedding, which uses low-dimensional non-linear feature vectors to represent nodes in the network, has shown a great promise, not only because it is easy-to-use for downstream tasks, but also because it has achieved great success on many network analysis tasks. One of the challenges has been how to develop a node embedding method for integrating topological information from multiple networks. To address this critical problem, we propose a novel node embedding, called DeepMNE, for multi-network integration using a deep semi-supervised autoencoder. The key point of DeepMNE is that it captures complex topological structures of multiple networks and utilizes correlation among multiple networks as constraints. We evaluate DeepMNE in node classification task and link prediction task on four real-world datasets. The experimental results demonstrate that DeepMNE shows superior performance over seven state-of-the-art single-network and multi-network embedding algorithms.

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Cited By

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  • (2023)Semi-supervised and un-supervised clusteringInformation Systems10.1016/j.is.2023.102178114:COnline publication date: 1-Mar-2023
  • (2021)Modeling Dynamic Heterogeneous Network for Link Prediction Using Hierarchical Attention with Temporal RNNMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-67658-2_17(282-298)Online publication date: 25-Feb-2021

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cover image ACM Conferences
CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management
November 2019
3373 pages
ISBN:9781450369763
DOI:10.1145/3357384
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 03 November 2019

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Author Tags

  1. multi-network representation learning
  2. network constraints
  3. node embedding
  4. semi-supervised autoencoder

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  • Short-paper

Funding Sources

  • Fundamental Research Funds for the Central Universities
  • National Natural Science Foundation of China

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CIKM '19
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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

View all
  • (2023)Semi-supervised and un-supervised clusteringInformation Systems10.1016/j.is.2023.102178114:COnline publication date: 1-Mar-2023
  • (2021)Modeling Dynamic Heterogeneous Network for Link Prediction Using Hierarchical Attention with Temporal RNNMachine Learning and Knowledge Discovery in Databases10.1007/978-3-030-67658-2_17(282-298)Online publication date: 25-Feb-2021

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