A network representation method based on edge information extraction

In recent years, network representation learning has attracted extensive attention in the academic field due to its significant application potential. However, most of the methods cannot explore edge information in the network deeply, resulting in poor performance at downstream tasks such as classification, clustering and link prediction. In order to solve this problem, we propose a novel way to extract network information. First, the original network is transformed into an edge network with structure and edge information. Then, edge representation vectors can be obtained directly by using an existing network representation model with edge network as its input. Node representation vectors can also be obtained by utilizing the relationships between edges and nodes. Compared with the structure of original network, the edge network is denser, which can help solving the problems caused by sparseness. Extensive experiments on several real-world networks demonstrate that edge network outperforms original network in various graph mining tasks, i.e., node classification and node clustering.

[1]  Le Wu,et al.  Deep Attributed Network Embedding by Preserving Structure and Attribute Information , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[2]  Zhiguo Gong,et al.  Identifying influential user communities on the social network , 2015, Enterp. Inf. Syst..

[3]  Longbing Cao,et al.  Perceiving the Next Choice with Comprehensive Transaction Embeddings for Online Recommendation , 2017, ECML/PKDD.

[4]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[5]  Laurens van der Maaten,et al.  Accelerating t-SNE using tree-based algorithms , 2014, J. Mach. Learn. Res..

[6]  Zhiyuan Liu,et al.  Max-Margin DeepWalk: Discriminative Learning of Network Representation , 2016, IJCAI.

[7]  Jiawei Han,et al.  Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks , 2018, KDD.

[8]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[9]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[10]  Jiawei Han,et al.  An Attention-based Collaboration Framework for Multi-View Network Representation Learning , 2017, CIKM.

[11]  Chong Wu,et al.  Information loss method to measure node similarity in networks , 2014 .

[12]  Zhiyuan Liu,et al.  CANE: Context-Aware Network Embedding for Relation Modeling , 2017, ACL.

[13]  Xiangnan He,et al.  Attributed Social Network Embedding , 2017, IEEE Transactions on Knowledge and Data Engineering.

[14]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[15]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[16]  Deli Zhao,et al.  Network Representation Learning with Rich Text Information , 2015, IJCAI.

[17]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[18]  Ngoc Thanh Nguyen,et al.  Research collaboration model in academic social networks , 2018, Enterp. Inf. Syst..

[19]  Jian Pei,et al.  Asymmetric Transitivity Preserving Graph Embedding , 2016, KDD.

[20]  Jian Pei,et al.  Community Preserving Network Embedding , 2017, AAAI.

[21]  Vasant Honavar,et al.  MEGAN: A Generative Adversarial Network for Multi-View Network Embedding , 2019, IJCAI.

[22]  Wendy Miller,et al.  Using social network analysis to identify stakeholders’ influence on energy efficiency of housing , 2017 .

[23]  Chengqi Zhang,et al.  Binarized attributed network embedding , 2018, 2018 IEEE International Conference on Data Mining (ICDM).

[24]  Feiping Nie,et al.  Cauchy Graph Embedding , 2011, ICML.

[25]  Ling Luo,et al.  Caching Placement with Recommendation Systems for Cache-Enabled Mobile Social Networks , 2017, IEEE Communications Letters.

[26]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[27]  Gerard Salton,et al.  Automatic text analysis. , 1970 .

[28]  Qiongkai Xu,et al.  GraRep: Learning Graph Representations with Global Structural Information , 2015, CIKM.

[29]  Wenwu Zhu,et al.  Structural Deep Network Embedding , 2016, KDD.

[30]  M. Newman,et al.  Vertex similarity in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Chuan Zhou,et al.  Low-Bit Quantization for Attributed Network Representation Learning , 2019, IJCAI.

[32]  Zhiyuan Liu,et al.  TransNet: Translation-Based Network Representation Learning for Social Relation Extraction , 2017, IJCAI.