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Betweenness centrality-based community adaptive network representation for link prediction

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Abstract

Link prediction is a fundamental problem in biological network analysis, personalized recommendation, network evolution modeling, etc. It aims at discovering links in the network that are unknown, missing, or will be formed in the future. Network representation learning-based link prediction approaches have drawn extensive attention, due to its high efficiency. The previous approaches use random or hyper parameters to select nodes from neighbors or communities when generating walk sequences. However, they do not fully consider the contribution of nodes to the embedded representation and hence impairing the affect the role of community structure in link prediction. To overcome this limitation and utilize community structure, we propose a betweenness centrality-based community adaptive network representation for link prediction method called CALP, which forms network representation by using betweenness centrality to measure the different contribution of community nodes and neighbor nodes for embedding and then applies it to link prediction. CALP first divides the network into communities. Then, it selects a node from the community nodes or neighbor nodes to join the walk sequence by the contribution of the node to embedding. Finally, it generates the corresponding network representation for link prediction. Experiments on realistic networks such as Cora, Citeseer, etc. show that the accuracy of CALP is much better than other approaches.

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References

  1. Martinez V, Berzal F, Cubero JC (2017) A survey of link prediction in complex networks. ACM Comput Surv 49(4):33. 10.1145/3012704

    Article  Google Scholar 

  2. Urena R, Chiclana F, Melancon G, Herrera-Viedma E (2019) A social network based approach for consensus achievement in multiperson decision making. Inf Fusion 47:72–87. https://doi.org/10.1016/j.inffus.2018.07.006

    Article  Google Scholar 

  3. Liu HW, Kou HZ, Yan C, Qi LY (2019) Link prediction in paper citation network to construct paper correlation graph. EURASIP J Wirel Commun Netw 2019(1):12. https://doi.org/10.1186/s13638-019-1561-7

    Article  Google Scholar 

  4. Liu F, Deng Y (2019) A fast algorithm for network forecasting time series. Ieee Access 102554-102560:7. https://doi.org/10.1109/access.2019.2926986

    Google Scholar 

  5. Cui P, Wang X, Pei J, Zhu W (2019) A survey on network embedding. IEEE Trans Knowl Data Eng 31(5):833–852. https://doi.org/10.1109/TKDE.2018.2849727

    Article  Google Scholar 

  6. Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. Paper presented at the Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York

  7. Cannistraci CV, Alanis-Lobato G, Ravasi T (2013) From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Scientific Reports 3:13. https://doi.org/10.1038/srep01613

    Article  Google Scholar 

  8. Keikha MM, Rahgozar M, Asadpour M (2018) Community aware random walk for network embedding. Knowledge-Based Systems 148:47–54. https://doi.org/10.1016/j.knosys.2018.02.028

    Article  Google Scholar 

  9. Liu F, Wang Z, Deng Y (2020) GMM: A generalized mechanics model for identifying the importance of nodes in complex networks. Knowl-Based Syst 193:17. https://doi.org/10.1016/j.knosys.2019.105464

    Google Scholar 

  10. Dettmers T, Minervini P, Stenetorp P, Riedel S, Aaai (2018) Convolutional 2D knowledge graph embeddings. Paper presented at the Thirty-Second Aaai Conference on Artificial Intelligence / Thirtieth Innovative Applications of Artificial Intelligence Conference / Eighth Aaai Symposium on Educational Advances in Artificial Intelligence. Palo Alto

  11. Ganea O-E, Becigneul G, Hofmann T (2018) Hyperbolic entailment cones for learning hierarchical embeddings

  12. Lu H, Halappanavar M, Kalyanaraman A (2015) Parallel heuristics for scalable community detection. Parallel Computing 47:19–37. https://doi.org/10.1016/j.parco.2015.03.003

    Article  MathSciNet  Google Scholar 

  13. Sarukkai RR (2000) Link prediction and path analysis using Markov chains1This work was done by the author prior to his employment at Yahoo Inc.1. Computer Networks 33(1):377–386. https://doi.org/10.1016/S1389-1286(00)00044-X

    Article  Google Scholar 

  14. Liben-Nowell D, Kleinberg J (2003) The link prediction problem for social networks. Paper presented at the Proceedings of the twelfth international conference on Information and knowledge management New Orleans, LA, USA

  15. Zhou K, Michalak TP, Waniek M, Rahwan T, Vorobeychik Y, Assoc Comp M (2019) Attacking Similarity-Based Link Prediction in Social Networks Aamas ’19: Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems. Assoc Computing Machinery, New York

    Google Scholar 

  16. Yang YL, Guo H, Tian T, Li HF (2015) Link prediction in brain networks based on a hierarchical random graph model. Tsinghua Sci Technol 20(3):306–315. https://doi.org/10.1109/tst.2015.7128943

    Article  MathSciNet  Google Scholar 

  17. Zhang XJ, Pang WB, Xia YX (2018) An intermediary probability model for link prediction. Physica A 512:902–912. https://doi.org/10.1016/j.physa.2018.08.068

    Article  Google Scholar 

  18. Yao L, Wang L, Pan L, Yao K (2016) Link Prediction Based on Common-Neighbors for Dynamic Social Network. Procedia Computer Science 83:82–89. https://doi.org/10.1016/j.procs.2016.04.102

    Article  Google Scholar 

  19. Hesamipour S, Balafar MA (2019) A new method for detecting communities and their centers using the Adamic/Adar Index and game theory. Physica A: Statistical Mechanics and its Applications 535:122354. https://doi.org/10.1016/j.physa.2019.122354

    Article  Google Scholar 

  20. Hebert-Dufresne L, Allard A, Marceau V, Noel PA, Dube LJ (2011) Structural preferential attachment: network organization beyond the link. Phys Rev Lett 107(15):5. https://doi.org/10.1103/PhysRevLett.107.158702

    Article  Google Scholar 

  21. Yao YB, Zhang RS, Yang F, Yuan YN, Hu RJ, Zhao ZL (2017) Link prediction based on local weighted paths for complex networks. Int J Mod Phys C 28(4):23. https://doi.org/10.1142/s012918311750053x

    Article  Google Scholar 

  22. Liao H, Zeng A, Zhang Y-C (2015) Predicting missing links via correlation between nodes. Physica A: Statistical Mechanics and its Applications 436:216–223. https://doi.org/10.1016/j.physa.2015.05.009

    Article  Google Scholar 

  23. Kumar A, Singh SS, Singh K, Biswas B (2020) Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications 553:124289. https://doi.org/10.1016/j.physa.2020.124289

    Article  MathSciNet  Google Scholar 

  24. Xie Y, Gong MG, Qin AK, Tang ZD, Fan XL (2019) TPNE: Topology Preserving network embedding. Inf Sci 504:20–31. https://doi.org/10.1016/j.ins.2019.07.035

    Article  MathSciNet  Google Scholar 

  25. Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowl-Based Syst 151:78–94. https://doi.org/10.1016/j.knosys.2018.03.022

    Article  Google Scholar 

  26. Tang J, Qu M, Wang MZ, Zhang M, Yan J, Mei QZ, Acm (2015) LINE: Large-scale Information Network Embedding Proceedings Of the 24th International Conference on World Wide Web. Assoc Computing Machinery, New York. https://doi.org/10.1145/2736277.2741093

    Google Scholar 

  27. Grover A, Leskovec J, Assoc Comp M (2016) Node2vec: Scalable Feature Learning for Networks. kdd’16: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. Assoc Computing Machinery, New York. https://doi.org/10.1145/2939672.2939754

    Google Scholar 

  28. Wang DX, Cui P, Zhu WW, Assoc Comp M (2016) Structural Deep Network Embedding kdd’16: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. Assoc Computing Machinery, New York. https://doi.org/10.1145/2939672.2939753

    Google Scholar 

  29. Alanis-Lobato G, Mier P, Andrade-Navarro MA (2016) Efficient embedding of complex networks to hyperbolic space via their Laplacian. Scientific Reports 6:10. https://doi.org/10.1038/srep30108

    Article  Google Scholar 

  30. De A, Bhattacharya S, Sarkar S, Ganguly N, Chakrabarti S (2016) Discriminative Link Prediction using Local, Community, and Global Signals. Ieee Transactions on Knowledge and Data Engineering 28(8):2057–2070. https://doi.org/10.1109/tkde.2016.2553665

    Article  Google Scholar 

  31. Yu W, Liu XY, Ouyang B (2020) Link prediction based on network embedding and similarity transferring methods. Mod Phys Lett B 34(16):13–35. https://doi.org/10.1142/s0217984920501699

    Article  MathSciNet  Google Scholar 

  32. Spring N, Mahajan R, Wetherall D (2002) Measuring ISP topologies with rocketfuel. ACM SIGCOMM Comp Commun Rev 32(4):133–145. https://doi.org/10.1145/964725.633039

    Article  Google Scholar 

  33. Adamic LA, Glance N (2005) The political blogosphere and the 2004. U.s. election: divided they blog Paper presented at the Proceedings of the 3rd international workshop on Link discovery. Chicago,Illinois

  34. von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P (2002) Comparative assessment of large-scale datasets of protein-protein interactions. Nature 417(6887):399–403. https://doi.org/10.1038/nature750

    Article  Google Scholar 

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Funding

This work was supported in part by the National Nature Science Foundation of China under Grant 61702060 and the Natural Science Foundation of Chongqing under Grant cstc2020jcyj-msxmX0137.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Mingqiang Zhou, Haijiang Jin, Quanwang Wu, Hong Xie & Qizhi Han

  2. Chongqing Key Laboratory of Software Theory and Technology, Chongqing, 400044, China

    Haijiang Jin & Quanwang Wu

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  1. Mingqiang Zhou
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Correspondence to Mingqiang Zhou.

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Zhou, M., Jin, H., Wu, Q. et al. Betweenness centrality-based community adaptive network representation for link prediction. Appl Intell 52, 3545–3558 (2022). https://doi.org/10.1007/s10489-021-02633-7

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