A Deep Learning Based Dynamic Social Network Alignment Method

WANG Fei-yang; JI Peng-xin; SUN Li; WEI Qian; LI Gen; ZHANG Zhong-bao

doi:10.12263/DZXB.20201436

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A Deep Learning Based Dynamic Social Network Alignment Method

PAPERS | 更新时间：2025-12-08

- A Deep Learning Based Dynamic Social Network Alignment Method
- ACTA ELECTRONICA SINICA Vol. 50, Issue 8, Pages: 1925-1936(2022)
- 作者机构：
  
  北京邮电大学计算机学院，北京 100876
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20201436
  CLC： TP311;
- Received：15 December 2020，
  
  Revised：2021-04-11，
  
  Published：25 August 2022
- 稿件说明：
移动端阅览
王飞扬,冀鹏欣,孙笠等.一种基于深度学习的动态社交网络用户对齐方法[J].电子学报,2022,50(08):1925-1936.

WANG Fei-yang,JI Peng-xin,SUN Li,et al.A Deep Learning Based Dynamic Social Network Alignment Method[J].ACTA ELECTRONICA SINICA,2022,50(08):1925-1936.
王飞扬,冀鹏欣,孙笠等.一种基于深度学习的动态社交网络用户对齐方法[J].电子学报,2022,50(08):1925-1936. DOI： 10.12263/DZXB.20201436.

WANG Fei-yang,JI Peng-xin,SUN Li,et al.A Deep Learning Based Dynamic Social Network Alignment Method[J].ACTA ELECTRONICA SINICA,2022,50(08):1925-1936. DOI： 10.12263/DZXB.20201436.

摘要

社交网络对齐旨在从不同的社交网络中识别出属于同一自然人的社交账户.现有的相关研究大多着眼于静态社交网络的对齐上，然而，社交网络是动态发展的.本文观察到，这种动态性可以揭示出更多的决定性模式，从而更有利于社交网络的对齐，这种现象促使本文在动态场景中重新思考这个问题.于是，本文利用社交网络的动态性，设计一个深度学习架构来解决动态社交网络的对齐问题，其称为DeepDSA（Deep learning based Dynamic Social network Alignment method）.首先设计一个深度序列模型来分别捕捉社交网络结构和属性的动态性；其次，对于每一个社交网络，通过保持相同用户结构和属性之间的相关性来融合二元动态，得到原始的综合嵌入表示；最后，以半监督的方式进行空间变换学习，并将每个网络的原始嵌入投影到一个目标子空间中，在该子空间中自然人是唯一表示的.本文在真实世界的数据集上进行大量的实验，证明DeepDSA方法相较于目前的主流算法提升了10%的对齐效果.

Abstract

Social network alignment aims to identify social accounts belonging to the same natural person from different social networks. Most of the existing related researches focus on the alignment of static social networks. However

social networks are dynamically evolving. We observe that dynamics can reveal more discriminative patterns and thus can benefit social network alignment. This phenomenon motivates us to rethink this issue in dynamic scenarios. Therefore

we propose to leverage the dynamics of social networks and design a deep learning architecture to address the dynamic social network alignment problem

termed as DeepDSA. Specifically

we first design a deep sequence model to capture the dynamics of social network structure and attributes respectively. For each social network

we merged binary dynamics by maintaining the correlation between structure and attributes of the same user to obtain the original comprehensive embeddings. We finally perform spatial transformation learning in a semi-supervised manner

and project the original embedding of each network into a target subspace in which a natural person is uniquely represented. We conduct extensive experiments on real-world datasets and demonstrate the proposed DeepDSA achieves 10% improvement of precision against the current mainstream algorithm.

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references

CAO X Z , YU Y . BASS: A Bootstrapping approach for aligning heterogenous social networks [C]// Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Riva del Garda : Springer , 2016 : 459 - 475 .

ZHOU J Y , FAN J X . TransLink: User identity linkage across heterogeneous social networks via translating embeddings [C]// IEEE INFOCOM 2019-IEEE Conference on Computer Communications . Paris : IEEE , 2019 : 2116 - 2124 .

ZHOU F , LIU L , ZHANG K P , et al . DeepLink: A deep learning approach for user identity linkage [C]// IEEE INFOCOM 2018-IEEE Conference on Computer Communications . Honolulu : IEEE , 2018 : 1313 - 1321 .

LI C Z , WANG S Z , YU P S , et al . Distribution distance minimization for unsupervised user identity linkage [C]// CIKM'18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management . Torino : ACM , 2018 : 447 - 456 .

LIU L , CHEUNG W K , LI X , et al . Aligning users across social networks using network embedding [C]// IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence . New York : AAAI Press , 2016 . 1774 - 1780 .

MAN T , SHEN H , LIU S , et al . Predict anchor links across social networks via an embedding approach [C]// IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence . New York : AAAI Press , 2016 . 1823 - 1829 .

SU S , SUN L , ZHANG Z B , et al . MASTER: Across multiple social networks, integrate attribute and structure embedding for reconciliation [C]// IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence . Stockholm : AAAI Press , 2018 : 3863 - 3869 .

TAN S L , GUAN Z Y , CAI D , et al . Mapping users across networks by manifold alignment on hypergraph [C]// AAAI'14: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence . Québec City : AAAI Press , 2014 : 159 - 165 .

WANG Y Q , SHEN H W , GAO J H , et al . Learning binary hash codes for fast anchor link retrieval across networks [C]// WWW'19: The World Wide Web Conference. San Francisco: ACM , 2019 : 3335 - 3341 .

SHU K , WANG S H , TANG J L , et al . User identity linkage across online social networks [J]. ACM SIGKDD Explorations Newsletter , 2017 , 18 ( 2 ): 5 - 17 .

ZHANG S , TONG H H , MACIEJEWSKI R , et al . Multilevel network alignment [C]// WWW'19: The World Wide Web Conference. San Francisco: ACM , 2019 : 2344 - 2354 .

XIE W , MU X , LEE R K W , et al . Unsupervised user identity linkage via factoid embedding [C]// 2018 IEEE International Conference on Data Mining . Singapore : IEEE , 2018 : 1338 - 1343 .

ZHOU F , WEN Z J , TRAJCEVSKI G , et al . Disentangled network alignment with matching explainability [C]// IEEE INFOCOM 2019-IEEE Conference on Computer Communications . Paris : IEEE , 2019 : 1360 - 1368 .

ZAFARANI R , LIU H . Connecting corresponding identities across communities [C]// AAAI International Conference on Web and Social Media . Palo Alto : AAAI Press , 2009 . 354 - 357 .

ZHANG J , CHEN B , WANG X M , et al . MEgo2Vec: Embedding matched ego networks for user alignment across social networks [C]// CIKM'18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management . Torino : ACM , 2018 : 327 - 336 .

ZHANG Y T , TANG J , YANG Z L , et al . COSNET: Connecting heterogeneous social networks with local and global consistency [C]// Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . Sydney : ACM , 2015 : 1485 - 1494 .

ZHONG Z , CAO Y , GUO M , et al . Colink: An unsupervised framework for user identity linkage [C]// AAAI Conference on Artificial Intelligence . Palo Alto : AAAI Press , 2018 . 5714 - 5721 .

ZAFARANI R , LIU H . Connecting users across social media sites: A behavioral-modeling approach [C]// Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . Chicago : ACM , 2013 : 41 - 49 .

SUN L , ZHANG Z B , JI P X , et al . DNA: Dynamic social network alignment [C]// 2019 IEEE International Conference on Big Data(Big Data) . Los Angeles : IEEE , 2019 : 1224 - 1231 .

WANG Y Q , SHEN H W , LIU S H , et al . Cascade dynamics modeling with attention-based recurrent neural network [C]// IJCAI'17: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence . Melbourne : AAAI Press , 2017 : 2985 - 2991 .

MU X , ZHU F D , LIM E P , et al . User identity linkage by latent user space modelling [C]// KDD'16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . San Francisco : ACM , 2016 : 1775 - 1784 .

GREFF K , SRIVASTAVA R K , KOUTNIK J , et al . LSTM: A search space odyssey [J]. IEEE Transactions on Neural Networks and Learning Systems , 2017 , 28 ( 10 ): 2222 - 2232 .

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 , 2014 : 701 - 710 .

YANG C , LIU Z , ZHAO D , et al . Network representation learning with rich text information [C]// International Joint Conference on Artificial Intelligence . Buenos Aires : AAAI Press , 2015 . 2111 - 2117 .

LE Q , MIKOLOV T . Distributed representations of sentences and documents [C]// International Conference on Machine Learning . Beijing : JMLR.org , 2014 : 1188 - 1196 .

UTZ S , TANIS M , VERMEULEN I . It is all about being popular: The effects of need for popularity on social network site use [J]. Cyberpsychology, Behavior, and Social Networking , 2012 , 15 ( 1 ): 37 - 42 .

ZUO Y , LIU G N , LIN H , et al . Embedding temporal network via neighborhood formation [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . London : ACM , 2018 : 2857 - 2866 .

LIANG S S , ZHANG X L , REN Z C , et al . Dynamic embeddings for user profiling in twitter [C]// Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . London : ACM , 2018 : 1764 - 1773 .

VASWANI A , SHAZEER N , PARMAR N , et al . Attention is all you need [C]// Advances in Neural Information Processing Systems . Long Beach : Curran Associates Inc. , 2017 : 5998 - 6008 .

GAO H C , HUANG H . Deep attributed network embedding [C]// IJCAI'18: Proceedings of the 27th International Joint Conference on Artificial Intelligence . Stockholm : AAAI Press , 2018 : 3364 - 3370 .

KONG X N , ZHANG J W , YU P S . Inferring anchor links across multiple heterogeneous social networks [C]// CIKM'13: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management . San Francisco : ACM , 2013 : 179 - 188 .

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Related Institution

Department of Computer Science， Beijing University of Posts and Telecommunications

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