基于高阶相似性的属性网络表示学习

邬少清; 董一鸿; 王雄; 曹燕; 辛宇

doi:10.11959/j.issn.1000-0801.2020309

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基于高阶相似性的属性网络表示学习

研究与开发 | 更新时间：2024-06-05

- 基于高阶相似性的属性网络表示学习
- Learning attribute network algorithm based on high-order similarity
- 电信科学 2020年36卷第12期页码：20-32
- 作者机构：
- 作者简介：
  
  [ "邬少清（1995- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为大数据、数据挖掘" ]
  [ "董一鸿（1969- ），男，博士，宁波大学信息科学与工程学院教授、硕士生导师，主要研究方向为大数据、数据挖掘、人工智能" ]
  [ "王雄（1994- ），男，宁波大学信息科学与工程学院硕士生，主要研究方向为数据挖掘" ]
  [ "曹燕（1993- ），女，宁波大学信息科学与工程学院硕士生，主要研究方向为大数据、数据挖掘" ]
  [ "辛宇（1987- ），男，宁波大学信息科学与工程学院博士生，主要研究方向为数据库、知识工程" ]
- 基金信息：
  
  浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LY20F020009);浙江省自然科学基金资助项目;Zhejiang Provincial Natural Science Foundation of China(LZ20F020001);国家自然科学基金资助项目;The National Natural Science Foundation of China(61602133);宁波市自然科学基金资助项目;Ningbo Natural Science Foundation(202003N4086);宁波市自然科学基金资助项目;Ningbo Natural Science Foundation(2019A610093)
- DOI：10.11959/j.issn.1000-0801.2020309
  中图分类号： TP311
- 网络出版日期：2020-12，
  
  纸质出版日期：2020-12-20
- 稿件说明：
移动端阅览
邬少清, 董一鸿, 王雄, 等. 基于高阶相似性的属性网络表示学习[J]. 电信科学, 2020,36(12):20-32.

Shaoqing WU, Yihong DONG, Xiong WANG, et al. Learning attribute network algorithm based on high-order similarity[J]. Telecommunications science, 2020, 36(12): 20-32.
邬少清, 董一鸿, 王雄, 等. 基于高阶相似性的属性网络表示学习[J]. 电信科学, 2020,36(12):20-32. DOI： 10.11959/j.issn.1000-0801.2020309.

Shaoqing WU, Yihong DONG, Xiong WANG, et al. Learning attribute network algorithm based on high-order similarity[J]. Telecommunications science, 2020, 36(12): 20-32. DOI： 10.11959/j.issn.1000-0801.2020309.

摘要

现有的网络表示学习方法缺少对网络中隐含的深层次信息进行挖掘和利用。对网络中的潜在信息做进一步挖掘，提出了潜在的模式结构相似性，定义了网络结构间的相似度分数，用以衡量各个结构之间的相似性，使节点可以跨越不相干的顶点，获取全局结构上的高阶相似性。利用深度学习，融合多个信息源共同参与训练，弥补随机游走带来的不足，使得多个信息源信息之间紧密结合、互相补充，以达到最优的效果。实验选取Lap、DeepWalk、TADW、SDNE、CANE作为对比方法，将3个真实世界网络作为数据集来验证模型的有效性，进行节点分类和链路重构的实验。在节点分类中针对不同数据集和训练比例，性能平均提升1.7个百分点；链路重构实验中，仅需一半维度便实现了更好的性能，最后讨论了不同网络深度下模型的性能提升，通过增加模型的深度，节点分类的平均性能增加了1.1个百分点。

Abstract

Due to the lack of deep-level information mining and utilization in the existing network representation learning methods

the potential pattern structure similarity was proposed by further exploring the potential information in the network.The similarity score between network structures was defined to measure the similarity between various structures so that nodes could cross irrelevant vertices to obtain high-order similarities on the global structure.In order to achieve the best effect

deep learning was used to fuse multiple information sources to participate in training together to make up for the deficiency of random walks.In the experiment

Lap

DeepWalk

TADW

SDNE and CANE were selected as comparison methods

and three real-world networks were used as data sets to verify the validity of the model

and experiments of node classification and link reconstruction are carried out.In the node classification

the average performance is improved by 1.7 percentage points for different datasets and training proportions.In the link reconstruction experiment

only half the dimension is needed to achieve better performance.Finally

the performance improvement of the model under different network depths was discussed.By increasing the depth of the model

the average performance of node classification increased by 1.1 percentage points.

关键词

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references

LANDE D , FU M , GUO W , et al . Link prediction of scientific collaboration networks based on information retrieval [J ] . World Wide Web , 2020 ( 23 ): 2239 - 2257 .

FRANCOIS M , DONOVAN P , FONTAINE F . Modulating transcription factor activity:interfering with protein-protein interaction networks [C ] // Proceedings of Seminars in Cell ＆ Developmental Biology . Amsterdam:Academic Press , 2020 : 12 - 19 .

李晋 , 杨子龙 . 微博转发网络中的节点特征和传播模型 [J ] . 电信科学 , 2016 , 32 ( 1 ): 40 - 45 .

LI J , YANG Z L . Node characteristic and propagation model in microblog forwarding network [J ] . Telecommunications Science , 2016 , 32 ( 1 ): 40 - 45 .

TSOUMAKAS G , KATAKIS I . Multi-label classification:an overview [J ] . International Journal of Data Warehousing and Mining (IJDWM) , 2007 , 3 ( 3 ): 1 - 13 .

周晶 , 孙喜民 , 于晓昆 , 等 . 知识图谱与数据应用——智能推荐 [J ] . 电信科学 , 2019 , 35 ( 8 ): 165 - 172 .

ZHOU J , SUN X M , YU X K , et al . Knowledge graph and data application-intelligent recommendation [J ] . Telecommunications Science , 2019 , 35 ( 8 ): 165 - 172 .

LIBEN-NOWELL D , KLEINBERG J . The link-prediction problem for social networks [J ] . Journal of the American Society for Information Science and Technology , 2007 , 58 ( 7 ): 1019 - 1031 .

PEROZZI B , AL-RFOU R , SKIENA S . Deepwalk:online learning of social representations [C ] // Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2014 : 701 - 710 .

TANG J , QU M , WANG M , et al . Line:Large-scale information network embedding [C ] // Proceedings of the 24th International Conference on World Wide Web.[S.l.:s.n . ] , 2015 : 1067 - 1077 .

CAO S , LU W , XU Q . Grarep:learning graph representations with global structural information [C ] // Proceedings of the 24th ACM International on Conference on Information and Knowledge Management . New York:ACM Press , 2015 : 891 - 900 .

GROVER A , LESKOVEC J . node2vec:scalable feature learning for networks [C ] // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2016 : 855 - 864 .

MIKOLOV T , CHEN K , CORRADO G , et al . Efficient estimation of word representations in vector space [J ] . arXiv:1301.3781 , 2013

NEWMAN M E J . Modularity and community structure in networks [J ] . Proceedings of the National Academy of Sciences , 2006 , 103 ( 23 ): 8577 - 8582 .

WANG D , CUI P , ZHU W . Structural deep network embedding [C ] // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2016 : 1225 - 1234 .

LI Y , WANG Y , ZHANG T , et al . Learning network embedding with community structural information [C ] // Proceedings of IJCAI . San Francisco:Morgan Kaufman , 2019 : 2937 - 2943 .

YANG C , LIU Z , ZHAO D , et al . Network representation learning with rich text information [C ] // Proceedings of Twenty-Fourth International Joint Conference on Artificial Intelligence . San Francisco:Morgan Kaufman , 2015 .

HUANG X , LI J , HU X . Accelerated attributed network embedding [C ] // Proceedings of the 2017 SIAM International Conference on Data Mining . Philadelphia:SIAM , 2017 : 633 - 641 .

TU C , LIU H , LIU Z , et al . Cane:context-aware network embedding for relation modeling [C ] // Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics . Stroudsburg:ACL , 2017 : 1722 - 1731 .

HUANG X , SONG Q , LI J , et al . Exploring expert cognition for attributed network embedding [C ] // Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . New York:ACM Press , 2018 : 270 - 278 .

HOU C , HE S , TANG K . RoSANE:robust and scalable attributed network embedding for sparse networks [J ] . Neurocomputing , 2020 ( 409 ): 231 - 243 .

JIN D , LI B , JIAO P , et al . Network-specific variational auto-encoder for embedding in attribute networks [C ] // Proceedings of IJCAI . San Francisco:Morgan Kaufman , 2019 : 2663 - 2669 .

HE Z , LIU J , LI N , et al . Learning network-to-network model for content-rich network embedding [C ] // Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining . New York:ACM Press , 2019 : 1037 - 1045 .

LE Q , MIKOLOV T . Distributed representations of sentences and documents [C ] // Proceedings of International Conference on Machine Learning . New York:ACM Press , 2014 : 1188 - 1196 .

MCCALLUM A K , NIGAM K , RENNIE J , et al . Automating the construction of internet portals with machine learning [J ] . Information Retrieval , 2000 , 3 ( 2 ): 127 - 163 .

TANG J , ZHANG J , YAO L , et al . Arnetminer:extraction and mining of academic social networks [C ] // Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2008 : 990 - 998 .

TRAUD A L , MUCHA P J , PORTER M A . Social structure of facebook networks [J ] . Physica A:Statistical Mechanics and its Applications , 2012 , 391 ( 16 ): 4165 - 4180 .

BELKIN M , NIYOGI P . Laplacian eigenmaps and spectral techniques for embedding and clustering [C ] // Proceedings of Advances in Neural Information Processing Systems . Cambridge:MIT Press , 2002 : 585 - 591 .

PEDREGOSA F , VAROQUAUX G , GRAMFORT A , et al . Scikit-learn:machine learning in Python [J ] . Journal of Machine Learning Research , 2011 , 12 ( 8 ): 2825 - 2830 .

SEN P , NAMATA G , BILGIC M , et al . Collective classification in network data [J ] . AI Magazine , 2008 , 29 ( 3 ):93.

HANLEY J A , MCNEIL B J . The meaning and use of the area under a receiver operating characteristic (ROC) curve [J ] . Radiology , 1982 , 143 ( 1 ): 29 - 36 .

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