| [1] |
Srinivas, Virinchi and Mitra, Pabitra. Link prediction in social networks: role of power law distribution. Springer. 2016. [DOI ] |
| [2] |
Dai, Caiyan and Chen, Ling and Li, Bin. Link prediction based on sampling in complex networks. Applied Intelligence. 47(1): 1--12, Springer. 2017. [DOI ] |
| [3] |
Fortunato, Santo. Community detection in graphs. Physics reports. 486(3-5): 75--174, Elsevier. 2010. [DOI ] |
| [4] |
Kim, Kibae and Altmann, J{\"o}rn. Effect of homophily on network formation. Communications in Nonlinear Science and Numerical Simulation. 44: 482--494, Elsevier. 2017. [DOI ] |
| [5] |
Bramoull{\'e}, Yann and Currarini, Sergio and Jackson, Matthew O and Pin, Paolo and Rogers, Brian W. Homophily and long-run integration in social networks. Journal of Economic Theory. 147(5): 1754--1786, Elsevier. 2012. [DOI ] |
| [6] |
Mart{\'\i}nez, V{\'\i}ctor and Berzal, Fernando and Cubero, Juan-Carlos. A survey of link prediction in complex networks. ACM computing surveys (CSUR). 49(4): 1--33, ACM New York, NY, USA. 2016. [DOI ] |
| [7] |
Yuliansyah, Herman and Othman, Zulaiha Ali and Bakar, Azuraliza Abu. Taxonomy of link prediction for social network analysis: A review. IEEE Access. 8: 183470--183487, IEEE. 2020. [DOI ] |
| [8] |
Kumar, Ajay and Singh, Shashank Sheshar and Singh, Kuldeep and Biswas, Bhaskar. Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications. 553: 124289, Elsevier. 2020. [DOI ] |
| [9] |
Samad, Abdul and Qadir, Mamoona and Nawaz, Ishrat and Islam, Muhammad Arshad and Aleem, Muhammad. A Comprehensive Survey of Link Prediction Techniques for Social Network.. EAI Endorsed Trans. Ind. Networks Intell. Syst.. 7(23): e3, 2020. [DOI ] |
| [10] |
Arrar, Djihad and Kamel, Nadjet and Lakhfif, Abdelaziz. A comprehensive survey of link prediction methods: D. Arrar et al.. The journal of supercomputing. 80(3): 3902--3942, Springer. 2024. [DOI ] |
| [11] |
Sherkat, Ehsan and Rahgozar, Maseud and Asadpour, Masoud. Structural link prediction based on ant colony approach in social networks. Physica a: statistical mechanics and its applications. 419: 80--94, Elsevier. 2015. [DOI ] |
| [12] |
Nayyar, Anand and Singh, Rajeshwar. Ant colony optimization-computational swarm intelligence technique. 2016 3rd International conference on computing for sustainable global development (INDIACom). 1493--1499, IEEE. 2016. |
| [13] |
Chen, Bolun and Chen, Ling. A link prediction algorithm based on ant colony optimization. Applied Intelligence. 41(3): 694--708, Springer. 2014. [DOI ] |
| [14] |
Cao, Zhiwei and Zhang, Yichao and Guan, Jihong and Zhou, Shuigeng. Link prediction based on quantum-inspired ant colony optimization. Scientific reports. 8(1): 13389, Nature Publishing Group UK London. 2018. [DOI ] |
| [15] |
Cao, Zhiwei and Zhang, Yichao and Guan, Jihong and Zhou, Shuigeng and Wen, Guanghui. A chaotic ant colony optimized link prediction algorithm. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 51(9): 5274--5288, IEEE. 2019. [DOI ] |
| [16] |
Kumar, Shambhu and Jain, Arti and Bisht, Dinesh. Ant Colony Optimization for Link Prediction in Online Social Networks: A Swarm Intelligence Approach. Proceedings of the 2024 Sixteenth International Conference on Contemporary Computing. 632--641, 2024. [DOI ] |
| [17] |
Liben-Nowell, David and Kleinberg, Jon. The link prediction problem for social networks. Proceedings of the twelfth international conference on Information and knowledge management. 556--559, 2003. [DOI ] |
| [18] |
Adamic, Lada A and Adar, Eytan. Friends and neighbors on the web. Social networks. 25(3): 211--230, Elsevier. 2003. [DOI ] |
| [19] |
Sathre, Paul and Gondhalekar, Atharva and Feng, Wu-chun. Edge-connected jaccard similarity for graph link prediction on fpga. 2022 IEEE High Performance Extreme Computing Conference (HPEC). 1--10, 2022. [DOI ] |
| [20] |
Virinchi, Srinivas and Mitra, Pabitra. Similarity measures for link prediction using power law degree distribution. International Conference on Neural Information Processing. 257--264, 2013. [DOI ] |
| [21] |
Mart{\'\i}nez, V{\'\i}ctor and Berzal, Fernando and Cubero, Juan-Carlos. Adaptive degree penalization for link prediction. Journal of Computational Science. 13: 1--9, Elsevier. 2016. [DOI ] |
| [22] |
Liben-Nowell, David. An algorithmic approach to social networks. 2005. |
| [23] |
Gao, Zhie and Rezaeipanah, Amin. A novel link prediction model in multilayer online social networks using the development of Katz similarity metric. Neural Processing Letters. 55(4): 4989--5011, Springer. 2023. [DOI ] |
| [24] |
Co{\c{s}}kun, Mustafa and Baggag, Abdelkader and Koyut{\"u}rk, Mehmet. Fast computation of Katz index for efficient processing of link prediction queries. Data Mining and Knowledge Discovery. 35(4): 1342--1368, Springer. 2021. [DOI ] |
| [25] |
Liu, Weiping and L{\"u}, Linyuan. Link prediction based on local random walk. EPL (europhysics Letters). 89(5): 58007, 2010. [DOI ] |
| [26] |
Tong, Hanghang and Faloutsos, Christos and Pan, Jia-Yu. Fast random walk with restart and its applications. Sixth international conference on data mining (ICDM'06). 613--622, 2006. [DOI ] |
| [27] |
Vanunu, Oron and Sharan, Roded. A propagation-based algorithm for inferring gene-disease associations. German conference on bioinformatics. 54--63, 2008. |
| [28] |
L\"u, Linyuan and Jin, Ci-Hang and Zhou, Tao. Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E. 80: 046122, American Physical Society. 2009. [DOI ] |
| [29] |
Papadimitriou, Alexis and Symeonidis, Panagiotis and Manolopoulos, Yannis. Friendlink: link prediction in social networks via bounded local path traversal. 2011 International Conference on Computational Aspects of Social Networks (CASoN). 66--71, 2011. [DOI ] |
| [30] |
Berahmand, Kamal and Nasiri, Elahe and Forouzandeh, Saman and Li, Yuefeng. A preference random walk algorithm for link prediction through mutual influence nodes in complex networks. Journal of king saud university-computer and information sciences. 34(8): 5375--5387, Elsevier. 2022. [DOI ] |
| [31] |
Liu, Shihu and Feng, Xueli and Yang, Jin. Asymmetric influence-based superposed random walk link prediction algorithm in complex networks. International Journal of Modern Physics C. 36(10): 2442002, World Scientific. 2025. [DOI ] |
| [32] |
Clauset, Aaron and Moore, Cristopher and Newman, Mark EJ. Hierarchical structure and the prediction of missing links in networks. Nature. 453(7191): 98--101, Nature Publishing Group UK London. 2008. [DOI ] |
| [33] |
Guimer{\`a}, Roger and Sales-Pardo, Marta. Missing and spurious interactions and the reconstruction of complex networks. Proceedings of the National Academy of Sciences. 106(52): 22073--22078, National Academy of Sciences. 2009. [DOI ] |
| [34] |
Stanley, Natalie and Bonacci, Thomas and Kwitt, Roland and Niethammer, Marc and Mucha, Peter J. Stochastic block models with multiple continuous attributes. Applied Network Science. 4(1): 54, Springer. 2019. [DOI ] |
| [35] |
Pulipati, Srilatha and Somula, Ramasubbareddy and Parvathala, Balakesava Reddy. Nature inspired link prediction and community detection algorithms for social networks: a survey. International Journal of System Assurance Engineering and Management. 1--18, Springer. 2021. [DOI ] |
| [36] |
Silva, Nitai B and Tsang, Ren and Cavalcanti, George DC and Tsang, Jyh. A graph-based friend recommendation system using genetic algorithm. IEEE congress on evolutionary computation. 1--7, 2010. [DOI ] |
| [37] |
Naruchitparames, Jeff and G{\"u}ne{\c{s}}, Mehmet Hadi and Louis, Sushil J. Friend recommendations in social networks using genetic algorithms and network topology. 2011 IEEE Congress of Evolutionary Computation (CEC). 2207--2214, 2011. [DOI ] |
| [38] |
Bliss, Catherine A and Frank, Morgan R and Danforth, Christopher M and Dodds, Peter Sheridan. An evolutionary algorithm approach to link prediction in dynamic social networks. Journal of Computational Science. 5(5): 750--764, Elsevier. 2014. [DOI ] |
| [39] |
Rezaee, Abbas Ali and Abravan, Navid. A hybrid friend-based recommendation system using the combination of Meta-heuristic Invasive weed and genetic algorithms. 2020 10th International Conference on Computer and Knowledge Engineering (ICCKE). 665--669, 2020. [DOI ] |
| [40] |
Krouska, Akrivi and Troussas, Christos and Sgouropoulou, Cleo. Applying genetic algorithms for recommending adequate competitors in mobile game-based learning environments. International Conference on Intelligent Tutoring Systems. 196--204, 2020. [DOI ] |
| [41] |
Chen, Guangfu and Wang, Haibo and Fang, Yili and Jiang, Ling. Link prediction by deep non-negative matrix factorization. Expert Systems with Applications. 188: 115991, Elsevier. 2022. [DOI ] |
| [42] |
Agibetov, Asan. Neural graph embeddings as explicit low-rank matrix factorization for link prediction. Pattern Recognition. 133: 108977, Elsevier. 2023. [DOI ] |
| [43] |
Zhao, Zhili and Hu, Ahui and Zhang, Nana and Xie, Jiquan and Du, Zihao and Wan, Li and Yan, Ruiyi. Mining node attributes for link prediction with a non-negative matrix factorization-based approach. Knowledge-Based Systems. 299: 112045, Elsevier. 2024. [DOI ] |
| [44] |
Yang, Jing and Wang, Xiaofen and Yang, Laurence T and Gao, Yuan and Yang, Shundong and Wang, Xiaokang. Learning schema embeddings for service link prediction: a coupled matrix-tensor factorization approach. IEEE Transactions on Services Computing. IEEE. 2025. [DOI ] |
| [45] |
Yu, Wei and Fu, Jiale and Zhao, Yanxia and Shi, Hongjin and Chen, Xue and Shen, Shigen and Gao, Xiao-Zhi. Link prediction in bipartite networks via deep autoencoder-like nonnegative matrix factorization. Applied Soft Computing. 169: 112616, Elsevier. 2025. [DOI ] |
| [46] |
Shan, Na and Li, Longjie and Zhang, Yakun and Bai, Shenshen and Chen, Xiaoyun. Supervised link prediction in multiplex networks. Knowledge-Based Systems. 203: 106168, Elsevier. 2020. [DOI ] |
| [47] |
Giubilei, Riccardo and Brutti, Pierpaolo. Supervised classification for link prediction in facebook ego networks with anonymized profile information. Journal of Classification. 39(2): 302--325, Springer. 2022. [DOI ] |
| [48] |
Kumari, Anisha and Behera, Ranjan Kumar and Sahoo, Kshira Sagar and Nayyar, Anand and Kumar Luhach, Ashish and Prakash Sahoo, Satya. Supervised link prediction using structured-based feature extraction in social network. Concurrency and Computation: practice and Experience. 34(13): e5839, Wiley Online Library. 2022. [DOI ] |
| [49] |
Zhou, Yinzuo and Chen, Weilun and Zou, Huangrong. A link prediction algorithm based on support vector machine. Physica A: Statistical Mechanics and its Applications. 673: 130674, Elsevier. 2025. [DOI ] |
| [50] |
Ghorbanzadeh, Hossien and Sheikhahmadi, Amir and Jalili, Mahdi and Sulaimany, Sadegh. A hybrid method of link prediction in directed graphs. Expert Systems with Applications. 165: 113896, Elsevier. 2021. [DOI ] |
| [51] |
Mavromatis, Costas and Karypis, George. Graph infoclust: Maximizing coarse-grain mutual information in graphs. Pacific-Asia conference on knowledge discovery and data mining. 541--553, 2021. [DOI ] |
| [52] |
Chen, Jinyin and Zhang, Jian and Xu, Xuanheng and Fu, Chenbo and Zhang, Dan and Zhang, Qingpeng and Xuan, Qi. E-LSTM-D: A deep learning framework for dynamic network link prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 51(6): 3699--3712, IEEE. 2019. [DOI ] |
| [53] |
Keikha, Mohammad Mehdi and Rahgozar, Maseud and Asadpour, Masoud. DeepLink: A novel link prediction framework based on deep learning. Journal of Information Science. 47(5): 642--657, SAGE Publications Sage UK: London, England. 2021. [DOI ] |
| [54] |
Zulaika, Unai and Sanchez-Corcuera, Ruben and Almeida, Aitor and Lopez-de-Ipina, Diego. LWP-WL: Link weight prediction based on CNNs and the Weisfeiler--Lehman algorithm. Applied Soft Computing. 120: 108657, Elsevier. 2022. [DOI ] |
| [55] |
Robinson, Joshua and Ranjan, Rishabh and Hu, Weihua and Huang, Kexin and Han, Jiaqi and Dobles, Alejandro and Fey, Matthias and Lenssen, Jan Eric and Yuan, Yiwen and Zhang, Zecheng and others. Relbench: A benchmark for deep learning on relational databases. Advances in Neural Information Processing Systems. 37: 21330--21341, 2024. [DOI ] |
| [56] |
Choudhary, Sonia and Kumar, Gyanender. Enhancing link prediction in dynamic social networks through hybrid GCN-LSTM models. Knowledge and Information Systems. 67(8): 6717--6751, Springer. 2025. [DOI ] |
| [57] |
Zeb, Adnan and Saif, Summaya and Chen, Junde and Haq, Anwar Ul and Gong, Zhiguo and Zhang, Defu. Complex graph convolutional network for link prediction in knowledge graphs. Expert systems with applications. 200: 116796, Elsevier. 2022. [DOI ] |
| [58] |
Chen, Hongxu and Yin, Hongzhi and Sun, Xiangguo and Chen, Tong and Gabrys, Bogdan and Musial, Katarzyna. Multi-level graph convolutional networks for cross-platform anchor link prediction. Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery \& data mining. 1503--1511, 2020. [DOI ] |
| [59] |
Zhang, Peiliang and Chen, Jiatao and Che, Chao and Zhang, Liang and Jin, Bo and Zhu, Yongjun. IEA-GNN: Anchor-aware graph neural network fused with information entropy for node classification and link prediction. Information Sciences. 634: 665--676, Elsevier. 2023. [DOI ] |
| [60] |
He, Chaobo and Cheng, Junwei and Fei, Xiang and Weng, Yu and Zheng, Yulong and Tang, Yong. Community preserving adaptive graph convolutional networks for link prediction in attributed networks. Knowledge-Based Systems. 272: 110589, Elsevier. 2023. [DOI ] |
| [61] |
Wang, Yuxin and Hu, Xiannian and Gan, Quan and Huang, Xuanjing and Qiu, Xipeng and Wipf, David. Efficient link prediction via gnn layers induced by negative sampling. IEEE Transactions on Knowledge and Data Engineering. 37(1): 253--264, IEEE. 2024. [DOI ] |
| [62] |
Mao, Yuren and Hao, Yu and Cao, Xin and Gao, Yunjun and Yao, Chang and Lin, Xuemin. Boosting GNN-based link prediction via PU-AUC optimization. IEEE Transactions on Knowledge and Data Engineering. IEEE. 2025. [DOI ] |
| [63] |
Lim, Marcus and Abdullah, Azween and Jhanjhi, NZ. Data fusion-link prediction for evolutionary network with deep reinforcement learning. International Journal of Advanced Computer Science and Applications. 11(6): Science and Information (SAI) Organization Limited. 2020. |
| [64] |
Chen, Ling and Cui, Jun and Tang, Xing and Qian, Yuntao and Li, Yansheng and Zhang, Yongjun. RLPath: a knowledge graph link prediction method using reinforcement learning based attentive relation path searching and representation learning: RLPath: A Knowledge Graph Link Prediction Method Using Reinforcement Learning Based Attentive Relation Path Searching and Representation Learning. Applied Intelligence. 52(4): 4715--4726, Springer. 2022. [DOI ] |
| [65] |
Nie, Mingshuo and Chen, Dongming and Wang, Dongqi and Chen, Huilin. Local optimization policy for link prediction via reinforcement learning. IEEE Transactions on Network Science and Engineering. 12(2): 1224--1236, IEEE. 2025. [DOI ] |
| [66] |
Wang, Jingwei and Ma, Yunlong and Liu, Min and Yuan, Han and Shen, Weiming and Li, Ling. A vertex similarity index using community information to improve link prediction accuracy. 2017 IEEE international conference on systems, man, and cybernetics (SMC). 158--163, 2017. [DOI ] |
| [67] |
Kerkache, Mohamed Hassen and Sadeg-Belkacem, Lamia and Benbouzid-Si Tayeb, Fatima and Ali, Amri. Supervised learning using community detection for link prediction. International Conference on Computing Systems and Applications. 85--94, 2022. [DOI ] |
| [68] |
Biswas, Anupam and Biswas, Bhaskar. Community-based link prediction. Multimedia Tools and Applications. 76(18): 18619--18639, Springer. 2017. [DOI ] |
| [69] |
Kumari, Anisha and Behera, Ranjan Kumar and Sahoo, Bibudatta and Sahoo, Satya Prakash. Prediction of link evolution using community detection in social network. Computing. 104(5): 1077--1098, Springer. 2022. [DOI ] |
| [70] |
Iqbal, M Mohamed and Latha, K. An effective community-based link prediction model for improving accuracy in social networks. Journal of Intelligent \& Fuzzy Systems. 42(3): 2695--2711, SAGE Publications Sage UK: London, England. 2022. [DOI ] |
| [71] |
Kuang, Rong and Liu, Qun and Yu, Hong. Community-based link prediction in social networks. International Conference in Swarm Intelligence. 341--348, 2016. [DOI ] |
| [72] |
Wang, Jingwei and Ma, Yunlong and Liu, Min and Shen, Weiming. Link prediction based on community information and its parallelization. IEEE Access. 7: 62633--62645, IEEE. 2019. [DOI ] |
| [73] |
Soundarajan, Sucheta and Hopcroft, John. Using community information to improve the precision of link prediction methods. Proceedings of the 21st international conference on world wide web. 607--608, 2012. [DOI ] |
| [74] |
Bai, Shenshen and Fang, Shiyu and Li, Longjie and Liu, Rui and Chen, Xiaoyun. Enhancing link prediction by exploring community membership of nodes. International Journal of Modern Physics B. 33(31): 1950382, World Scientific. 2019. [DOI ] |
| [75] |
Li, Longjie and Fang, Shiyu and Bai, Shenshen and Xu, Shijin and Cheng, Jianjun and Chen, Xiaoyun. Effective link prediction based on community relationship strength. IEEE Access. 7: 43233--43248, IEEE. 2019. [DOI ] |
| [76] |
Yang, Zhao and Hu, Rongjing and Zhang, Ruisheng. An improved link prediction algorithm based on common neighbors index with community membership information. 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS). 90--93, 2016. [DOI ] |
| [77] |
Saxena, Akrati and Fletcher, George and Pechenizkiy, Mykola. HM-EIICT: Fairness-aware link prediction in complex networks using community information. Journal of Combinatorial Optimization. 44(4): 2853--2870, Springer. 2022. [DOI ] |
| [78] |
Garza, Sara E and Schaeffer, Satu Elisa. Community detection with the label propagation algorithm: a survey. Physica A: Statistical Mechanics and its Applications. 534: 122058, Elsevier. 2019. [DOI ] |
| [79] |
Hagberg, Aric and Swart, Pieter J and Schult, Daniel A. Exploring network structure, dynamics, and function using NetworkX. 2007. |
| [80] |
Zachary, Wayne W. An information flow model for conflict and fission in small groups. Journal of anthropological research. 33(4): 452--473, University of New Mexico. 1977. [DOI ] |
| [81] |
Lusseau, David and Schneider, Karsten and Boisseau, Oliver J and Haase, Patti and Slooten, Elisabeth and Dawson, Steve M. The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations: can geographic isolation explain this unique trait?. Behavioral ecology and sociobiology. 54(4): 396--405, Springer. 2003. [DOI ] |
| [82] |
Girvan, Michelle and Newman, Mark EJ. Community structure in social and biological networks. Proceedings of the national academy of sciences. 99(12): 7821--7826, The National Academy of Sciences. 2002. [DOI ] |
| [83] |
Newman, Mark EJ. Modularity and community structure in networks. Proceedings of the national academy of sciences. 103(23): 8577--8582, National Academy of Sciences. 2006. [DOI ] |
| [84] |
Leskovec, Jure and Kleinberg, Jon and Faloutsos, Christos. Graph evolution: Densification and shrinking diameters. ACM transactions on Knowledge Discovery from Data (TKDD). 1(1): 2--es, ACM New York, NY, USA. 2007. [DOI ] |
| [85] |
Leskovec, Jure and Mcauley, Julian. Learning to discover social circles in ego networks. Advances in neural information processing systems. 25: 2012. |
| [86] |
Rozemberczki, Benedek and Allen, Carl and Sarkar, Rik. Multi-scale attributed node embedding. Journal of Complex Networks. 9(2): cnab014, Oxford University Press. 2021. [DOI ] |
| [87] |
Blondel, Vincent D and Guillaume, Jean-Loup and Lambiotte, Renaud and Lefebvre, Etienne. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment. 2008(10): P10008, 2008. [DOI ] |
|
