All Issue

2026 Vol.17, Issue 1 Preview Page

General Article

Social network link prediction techniques for smart urban infrastructure management: Factors, parameters, and applications
Mukesh Kumar1*
,
Kulvinder Singh2
,
Sanjeev Dhawan2
1Research Scholar, Department of Computer Science and Engineering, University Institute of Engineering and Technology (UIET), Kurukshetra University, Kurukshetra, India
2Associate Professor, Department of Computer Science and Engineering, University Institute of Engineering and Technology (UIET), Kurukshetra University, Kurukshetra, India
*Corresponding Author
31 March 2026. pp. 138-153
PDF XML Citation
Abstract

A social network represents a dynamic system of interpersonal interactions among individuals, communities, or organizations. These networks support diverse activities such as information sharing, public communication, service coordination, and citizen engagement. In smart urban environments, social networks play a crucial role in understanding human behavior, optimizing resource usage, and enhancing service delivery. However, one of the major challenges is to identify and strengthen meaningful connections among users that facilitate efficient information flow and improved system performance. Link prediction addresses this challenge by estimating the likelihood of future or missing connections between currently unconnected nodes. It contributes to several smart city applications, including community-based decision making and intelligent transportation systems. The objective of this work is to review state-of-the-art techniques used for link prediction in social networks ranging from similarity-based heuristics to advanced machine learning methods and to analyze the key factors and parameters influencing the prediction of missing links within smart urban systems.

Keywords
community detection
urban connectivity patterns
missing references
similarity score
smart urban sustainability
References
1

Wikipedia Contributors, Wikipedia, The Free Encyclopedia: Social Network [Online], 2004. Available at: https://en.wikipedia.org/wiki/Social_network [Accessed 28/11/2025].

2

D. Singla, D. Gupta, and N. Goyal, Sustainable basil leaf disease classification: Benchmarking seven deep learning models using transfer learning for urban and rural farming. International Journal of Sustainable Building Technology and Urban Development. 16(1) (2025), pp. 141-157.

3

P. Gupta, K.K. Bhatia, and N. Duhan, A Socio-economic cost-effective budget allocation framework for real-time bidding in online advertisement for urban development. International Journal of Sustainable Building Technology and Urban Development. 16(2) (2025), pp. 234-250.

4

S. Ressler, Social network analysis as an approach to combat terrorism: Past, present, and future research. Homeland Security Affairs. 2(2) (2006), pp. 1-10.

5

E.M. Airoldi, D.M. Blei, S.E. Fienberg, E.P. Xing, and T. Jaakkola, Mixed membership stochastic block models for relational data with application to protein-protein interactions. Proc. International Biometrics Society Annual Meeting. 15 (2006), pp. 1-34.

6

A. Sharma, N. Kumar, C. Diwaker, B. Sharma, R. Baniwal, S.B. Bhattacharjee, and S. Rani, A Machine learning-based framework for energy-efficient load balancing in sustainable urban infrastructure and smart buildings. International Journal of Sustainable Building Technology and Urban Development. 15(4) (2024), pp. 498-512.

7

X. Li and H. Chen, Recommendation as link prediction: A graph kernel-based machine learning approach. Proc. 9th ACM/ IEEE-CS Joint Conference on Digital libraries, ACM. (2009), pp. 213-216.

10.1145/1555400.1555433
8

R. Sharma and A. Kamra, Enhancing diagnosis of breast cancer through mammographic image segmentation using Fuzzy C-Means. International Journal of Sustainable Building Technology and Urban Development. 14(4) (2023), pp. 488-499.

9

M. Hasan Al and M.J. Zaki, A survey of link prediction in social networks, Social Network Data Analytics. 2011, Springer, pp. 243-275.

10.1007/978-1-4419-8462-3_9
10

D.D. Lee and H.S. Seung, Algorithms for non-negative matrix factorization. Proc. Advances in Neural Information Processing Systems. (2001). pp. 556-562.

11

Y.-J. Wu, E. Levina, and J. Zhu, Link prediction for egocentrically sampled networks. arXiv preprint arXiv: 1803.04084. (2018).

12

M. Eck, Y. Zemlyanskiy, J. Zhang, and A. Waibel, Extracting translation pairs from social network content. Proc. International Workshop on Spoken Language Translation (IWSLT). (2014). pp. 200-205.

13

M. Kim and J. Leskovec, The network completion problem: Inferring missing nodes and edges in networks. Proc. SIAM International Conference on Data Mining, SIAM. (2011), pp. 47-58.

10.1137/1.9781611972818.5
14

K. Jahanbakhsh, V. King, and G.C. Shoja, Predicting missing contacts in mobile social networks. Pervasive and Mobile Computing. 8(5) (2012), pp. 698-716.

10.1016/j.pmcj.2012.07.007
15

J. Mori, Y. Kajikawa, H. Kashima, and I. Sakata, Machine learning approach for finding business partners and building reciprocal relationships. Expert Systems with Applications. 39(12) (2012), pp. 10402-10407.

10.1016/j.eswa.2012.01.202
16

N. Talasu, A. Jonnalagadda, S.S.A. Pillai, and J. Rahul, A link prediction based approach for recommendation systems. Proc. International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE. (2017). pp. 2059-2062.

10.1109/ICACCI.2017.8126148
17

W. Almansoori, S. Gao, T.N. Jarada, A.M. Elsheikh, A.N. Murshed, J. Jida, R. Alhajj, and J. Rokne, Link prediction and classification in social networks and its application in healthcare and systems biology. Network Modeling Analysis in Health Informatics and Bioinformatics. 1(1-2) (2012), pp. 27-36.

10.1007/s13721-012-0005-7
18

D. Kagan, Y. Elovichi, and M. Fire, Generic anomalous vertices detection utilizing a link prediction algorithm. Social Network Analysis and Mining. 8(1) (2018), pp. 1-13.

10.1007/s13278-018-0503-4
19

Z. Huang and D.D. Zeng, A link prediction approach to anomalous email detection. Proc. IEEE International Conference on Systems, Man and Cybernetics, IEEE. (2) (2006), pp. 1131-1136.

10.1109/ICSMC.2006.384552
20

A.P. Appel, R.L. Cunha, C.C. Aggarwal, and M.M. Terakado, Temporally evolving community detection and prediction in content-centric networks. Proc. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. (2018), pp. 3-18.

10.1007/978-3-030-10928-8_1
21

H.-M. Cheng, Y.-Z. Ning, Z. Yin, C. Yan, X. Liu, and Z.-Y. Zhang, Community detection in complex networks using link prediction. Modern Physics Letters B. 32(1) (2018), 1850004.

10.1142/S0217984918500045
22

W. Yu, C.C. Aggarwal, and W. Wang, Temporally factorized network modeling for evolutionary network analysis. Proc. 10th ACM International Conference on Web Search and Data Mining, ACM. (2017), pp. 455-464.

10.1145/3018661.301866928626845PMC5470848
23

S. Aloufi, Trust-aware Link Prediction in Online Social Networks. Ph.D. Thesis, University of Ottawa/ University of Ottawa, 2012.

24

M. Kc, R. Chau, M. Hagenbuchner, A.C. Tsoi, and V. Lee, A machine learning approach to link prediction for interlinked documents. Proc. International Workshop of the Initiative for the Evaluation of XML Retrieval. (2009), pp. 342-354.

10.1007/978-3-642-14556-8_34
25

E. Weiss, K. Kurowski, S. Hischke, and B. Xu, Avoiding route breakage in ad hoc networks using link prediction. Proc. 9th IEEE Symposium on Computers and Communications (ISCC), IEEE. (2003), pp. 57-62.

10.1109/ISCC.2003.1214101
26

A. Yadav, Y.N. Singh, and R. Singh, Improving routing performance in AODV with link prediction in mobile ad hoc networks. Wireless Personal Communications. 83(1) (2015), pp. 603-618.

10.1007/s11277-015-2411-5
27

C. Hu and J.C. Hou, A link-indexed statistical traffic prediction approach to improving IEEE 802.11 PSM. Ad Hoc Networks. 3(5) (2005), pp. 529-545.

10.1016/j.adhoc.2004.08.003
28

M. Pavlov and R. Ichise, Finding experts by link prediction in co-authorship networks. Proc. 2nd International Conference on Finding Experts on the Web with Semantics (FEWS), CEUR-WS. 290 (2007), pp. 42-55.

29

T. Wohlfarth and R. Ichise, Semantic and event-based approach for link prediction. Proc. International Conference on Practical Aspects of Knowledge Management. (2008), pp. 50-61.

10.1007/978-3-540-89447-6_7
30

J.-S. Liu and K.-C. Ning, Applying link prediction to ranking candidates for high-level government post. Proc. International Conference on Advances in Social Networks Analysis and Mining, IEEE. (2011), pp. 145-152.

10.1109/ASONAM.2011.54
31

E. Perez-Cervantes, J.P. Mena-Chalco, M.C.F. de Oliveira, and R.M. Cesar, Using link prediction to estimate the collaborative influence of researchers. Proc. 9th IEEE International Conference on e-Science, IEEE. (2013). pp. 293-300.

10.1109/eScience.2013.32
32

T. Nguyen, D. Phung, B. Adams, and S. Venkatesh, Towards discovery of influence and personality traits through social link prediction. Proc. 5th International AAAI Conference on Weblogs and Social Media. (2011). pp. 566-569.

10.1609/icwsm.v5i1.14151
33

F. Folino and C. Pizzuti, Link prediction approaches for disease networks. Proc. International Conference on Information Technology in Bio and Medical Informatics. (2012), pp. 99-108.

10.1007/978-3-642-32395-9_8
34

D. Liben-Nowell and J. Kleinberg, The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology. 58(7) (2007), pp. 1019-1031.

10.1002/asi.20591
35

J. Zhao, L. Miao, J. Yang, H. Fang, Q.-M. Zhang, M. Nie, P. Holme, and T. Zhou, Prediction of links and weights in networks by reliable routes. Scientific reports. 5 (2015), 12261.

10.1038/srep1226126198206PMC4510530
36

J. Lee and R. Tukhvatov, Evaluations of similarity measures on VK for link prediction. Data Science and Engineering. 3(3) (2018), pp. 277-289.

10.1007/s41019-018-0073-5
37

V. Martinez, F. Berzal, and J.-C. Cubero, Adaptive degree penalization for link prediction. Journal of Computational Science. 13 (2016), pp. 1-9.

10.1016/j.jocs.2015.12.003
38

L. Lü, C.-H. Jin, and T. Zhou, Similarity index based on local paths for link prediction of complex networks. Physical Review E. 80(4), 046122.

10.1103/PhysRevE.80.046122
39

C. Wang, V. Satuluri, and S. Parthasarathy, Local probabilistic models for link prediction. Proc. 7th IEEE international conference on data mining (ICDM), IEEE. (2007), pp. 322-331.

10.1109/ICDM.2007.108
40

B. Meng, H. Ke, and T. Yi, Link prediction based on a semi-local similarity index. Chinese Physics B. 20(12) (2011), 128902.

10.1088/1674-1056/20/12/128902
41

A. Papadimitriou, P. Symeonidis, and Y. Manolopoulos, Fast and accurate link prediction in social networking systems. Journal of Systems and Software (JSS). 85(9) (2012), pp. 2119-2132.

10.1016/j.jss.2012.04.019
42

C.A. Bliss, M.R. Frank, C.M. Danforth, and P.S. Dodds, An evolutionary algorithm approach to link prediction in dynamic social networks. Journal of Computational Science. 5(5) (2014), pp. 750-764.

10.1016/j.jocs.2014.01.003
43

P. Wang, B. Xu, Y. Wu, and X. Zhou, Link prediction in social networks: The state-of the-art. Science China Information Sciences. 58(1) (2015), pp. 1-38.

10.1007/s11432-014-5237-y
44

L. Lü and T. Zhou, prediction in complex networks: A survey. Physica A: statistical mechanics and its applications. 390(6) (2011), pp. 1150-1170.

10.1016/j.physa.2010.11.027
45

T. Zhou, L. Lü, and Y.-C. Zhang, Predicting missing links via local information. The European Physical Journal B. 71(4) (2009), pp. 623-630.

10.1140/epjb/e2009-00335-8
46

L.A. Adamic and E. Adar, Friends and neighbors on the web. Social networks. 25(3) (2000), pp. 211-230.

10.1016/S0378-8733(03)00009-1
47

L. Katz, A new status index derived from sociometric analysis. Psychometrika. 18(1) (1953), pp. 39-43.

10.1007/BF02289026
48

G. Jeh and J. Widom, Simrank: A measure of structural-context similarity. Proc. 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. (2002), pp. 538-543.

10.1145/775047.775126
49

V.D. Blondel, A. Gajardo, M. Heymans, P. Senellart, and P. Van Dooren, A measure of similarity between graph vertices: Applications to synonym extraction and web searching. SIAM Review. 46(4) (2004), pp. 647-666.

10.1137/S0036144502415960
50

E.A. Leicht, P. Holme, and M.E. Newman, Vertex similarity in networks. Physical Review E. 73(2) (2006), 026120.

10.1103/PhysRevE.73.026120
51

W. Liu and L. Lü, Link prediction based on local random walk. Europhysics Letters (EPL). 89(5) (2010), 58007.

10.1209/0295-5075/89/58007
52

V. Martínez, F. Berzal, and J.-C. Cubero, A survey of link prediction in complex networks. ACM Computing Surveys (CSUR). 49(4) (2017), pp. 1-33.

10.1145/3012704
53

P. Srilatha and R. Manjula, Similarity index based link prediction algorithms in social networks: A survey. Journal of Telecommunications and Information Technology. 2 (2016), pp. 87-94.

10.26636/jtit.2016.2.725
54

M.E. Newman, Clustering and preferential attachment in growing networks. Physical Review E. 64(2) (2001), 025102.

10.1103/PhysRevE.64.025102
55

P. Jaccard, Étude comparative de la distribution florale dans une portion des alpes et des jura. Bull Soc Vaudoise Sci Nat. 37 (1901), pp. 547-579.

56

P. Bhattacharyya, A. Garg, and S.F. Wu, Analysis of user keyword similarity in online social networks. Social Network Analysis and Mining. 1(3) (2011), pp. 143-158.

10.1007/s13278-010-0006-4
57

L. Lü and T. Zhou, Role of weak ties in link prediction of complex networks. Proc. 1st ACM International Workshop on Complex Networks Meet Information and Knowledge Management, ACM. (2009), pp. 55-58.

10.1145/1651274.1651285
58

H. Chen, X. Li, and Z. Huang, Link prediction approach to collaborative filtering. Proc. 5th ACM/ IEEE-CS Joint Conference on Digital Libraries (JCDL), IEEE. (2005), pp. 141-142.

10.1145/1065385.1065415
59

T. Murata and S. Moriyasu, Link prediction of social networks based on weighted proximity measures. Proc. IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society. (2007), pp. 85-88.

10.1109/WI.2007.52
60

H.H. Song, T.W. Cho, V. Dave, Y. Zhang, and L. Qiu, Scalable proximity estimation and link prediction in online social networks. Proc. 9th ACM SIGCOMM Conference on Internet Measurement, ACM. (2009), pp. 322-335.

10.1145/1644893.1644932
61

S. Izudheen and S. Mathew, Identifying negative interactions in protein-protein interaction network using weak edge-edge domination set. Procedia Technology. 24 (2016), pp. 1423-1430.

10.1016/j.protcy.2016.05.167
62

Y. Lu, Y. Guo, and A. Korhonen, Link prediction in drug-target interactions network using similarity indices. BMC Bioinformatics. 18(1) (2017), pp. 39-48.

10.1186/s12859-017-1460-z28095781PMC5240398
63

S. Tariq, M. Saleem, and M. Shahbaz, User similarity determination in social networks. Technologies. 7(2) (2019), pp. 36-51.

10.3390/technologies7020036
64

H. Tian and R. Zafarani, Exploiting common neighbor graph for link prediction. in Proc. 29th ACM Int. Conf. Inf. & Knowl. Manage. (CIKM ’20). (2020), pp. 3333-3336. DOI: 10.1145/3340531.3417464.

10.1145/3340531.3417464
65

R.E. Tillman, V.K. Potluru, J. Chen, P.P. Reddy and M.M. Veloso, Heuristics for link prediction in multiplex networks, in Proc. 24th European Conf. Artificial Intelligence (ECAI 2020). (2020). Available at: https://arxiv.org/abs/2004.04704.

66

L. Wang, J. Ren, B. Xu, J. Li, W. Luo, and F. Xia, MODEL: Motif-based deep feature learning for link prediction. arXiv:2008.03637, Aug. (2020). [Preprint]. Available at: https://arxiv.org/abs/2008.03637.

67

G. Sharma, A. Challa, P. Gupta, and M.N. Murty, Higher-order relations skew link prediction in graphs. CoRR, arXiv:2111.00271, Oct. (2021). [Preprint]. Available at: https://arxiv.org/abs/2111.00271.

68

H. Gul, F. Al-Obeidat, A. Amin, M. Tahir, and K. Huang, Efficient link prediction model for real-world complex networks using matrix-forest metric with local similarity features. J. Complex Networks. 10(5) (2022), cnac039. DOI: 10.1093/comnet/cnac039.

10.1093/comnet/cnac039
69

S. Yun, S. Kim, J. Lee, J. Kang, and H. J. Kim, Neo-GNNs: Neighborhood overlap-aware graph neural networks for link prediction. arXiv:2206.04216, Jun. (2022). [Preprint]. Available at: https://arxiv.org/abs/2206.04216.

70

N. Singh and I. Singh, Application of resource allocation similarity-based link prediction in wireless networks. Int. J. Wireless Security & Networks. 1(2) (2023), pp. 37-42. Available at: https://journals.stmjournals.com/ijwsn/article=2023/view=118836.

71

J. Zhang, L. Wei, Z. Xu, and Q. Yao, Heuristic learning with graph neural networks: A unified framework for link prediction. arXiv:2406.07979, Jun. (2024). [Preprint]. Available at: https://arxiv.org/abs/2406.07979.

10.1145/3637528.3671946
72

Y.V. Nandini, T.J. Lakshmi, M.K. Enduri, and H. Sharma, Link prediction in complex networks using average centrality-based similarity score. Entropy. 26(6) (2024), 433. DOI: 10.3390/e26060433.

10.3390/e2606043338920442PMC11202912
73

P. Kapoor, S. Kaushal, H. Kumar, and K. Kanwar, A survey on feature extraction and learning techniques for link prediction in homogeneous and heterogeneous complex networks. Artif. Intell. Rev. 57 (2024). DOI: 10.1007/s10462-024-10998-7.

10.1007/s10462-024-10998-7
74

Z. Zhou, G. Wan, and B. Du, Common neighbor completion with information entropy for link prediction in social networks. Data Science and Engineering. 10 (2025), pp. 40-53. DOI: 10.1007/s41019-024-00267-6.

10.1007/s41019-024-00267-6
75

S.D. Pandey and S. Samanta, Strength prominence (SP) index: A link prediction method in fuzzy social networks. Complex & Intelligent Systems. 11 (2025). DOI: 10.1007/s40747-025-01925-6.

10.1007/s40747-025-01925-6
76

L. La Cava, D. Mandaglio, L. Zangari, and A. Tagarelli, Heuristic-informed mixture of experts for link prediction in multilayer networks (MoE-ML-LP). arXiv:2501.17557, Jan. (2025). [Preprint]. Available at: https://arxiv.org/abs/2501.17557.

10.1016/j.ins.2026.123106
77

M. Al Hasan, V. Chaoji, S. Salem, and M. Zaki, Link prediction using supervised learning. Proc. Workshop on Link Analysis, Counter-Terrorism and Security (SDM). (2006), pp. 1-10.

78

N. Benchettara, R. Kanawati, and C. Rouveirol, Supervised machine learning applied to link prediction in bipartite social networks. Proc. International Conference on Advances in Social Networks Analysis and Mining, IEEE. (2010), pp. 326-330.

10.1109/ASONAM.2010.87
79

J. O’Madadhain, J. Hutchins, and P. Smyth, Prediction and ranking algorithms for event based network data. ACM SIGKDD Explorations Newsletter. 7(2) (2005), pp. 23-30.

10.1145/1117454.1117458
80

N.Z. Gong, A. Talwalkar, L. Mackey, L. Huang, E.C.R. Shin, E. Stefanov, E.R. Shi, and D. Song, Joint link prediction and attribute inference using a social-attribute network. ACM Transactions on Intelligent Systems and Technology (TIST). 5(2) (2014), pp. 1-20.

10.1145/2594455
81

F. Liu, B. Liu, C. Sun, M. Liu, and X. Wang, Deep belief network-based approaches for link prediction in signed social networks. Entropy. 17(4) (2015), pp. 2140-2169.

10.3390/e17042140
82

S. Scellato, A. Noulas, and C. Mascolo, Exploiting place features in link prediction on location-based social networks. Proc. 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. (2011), pp. 1046-1054.

10.1145/2020408.2020575
83

E. Tasnádi and G. Berend, Supervised prediction of social network links using implicit sources of information. Proc. 24th International Conference on World Wide Web (WWW), ACM. (2015), pp. 1117-1122.

10.1145/2740908.2743037
84

J. Valverde-Rebaza and A. de Andrade Lopes, Exploiting behaviors of communities of twitter users for link prediction. Social Network Analysis and Mining. 3(4) (2013), pp. 1063-1074.

10.1007/s13278-013-0142-8
85

B. Qiu, K. Ivanova, J. Yen, and P. Liu, Behavior evolution and event-driven growth dynamics in social networks. Proc. 2nd IEEE International Conference on Social Computing, IEEE. (2010), pp. 217-224.

10.1109/SocialCom.2010.38
86

R.N. Lichtenwalter, J.T. Lussier, and N.V. Chawla, New perspectives and methods in link prediction. Proc. 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. (2010), pp. 243-252.

10.1145/1835804.1835837
87

P. Symeonidis, N. Iakovidou, N. Mantas, and Y. Manolopoulos, From biological to social networks: Link prediction based on multi-way spectral clustering. Data and Knowledge Engineering. 87 (2013), pp. 226-242.

10.1016/j.datak.2013.05.008
88

H. Kashima and N. Abe, A parameterized probabilistic model of network evolution for supervised link prediction. Proc. 6th International Conference on Data Mining (ICDM), IEEE. (2006), pp. 340-349.

10.1109/ICDM.2006.8
89

T.-T. Kuo, R. Yan, Y.-Y. Huang, P.-H. Kung, and S.-D. Lin, Unsupervised link prediction using aggregative statistics on heterogeneous social networks. Proc. 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. (2013), pp. 775-783.

10.1145/2487575.2487614
90

J. Zhang, J. Tang, J. Li, Y. Liu, and C. Xing, Who influenced you? Predicting retweet via social influence locality. ACM Transactions on Knowledge Discovery from Data (TKDD). 9(3) (2015), pp. 1-26.

10.1145/2700398
91

H. Liu, Z. Hu, H. Haddadi, and H. Tian, Hidden link prediction based on node centrality and weak ties. Europhysics Letters (EPL). 101(1) (2013), pp. 1-6.

10.1209/0295-5075/101/18004
92

X. Fang, P. Hu, Z. Li, and W. Tsai, Predicting Adoption Probabilities in Social Networks. Information Systems Research. 24(1) (2013), pp. 128-145.

10.1287/isre.1120.0461
93

X. Fang, O.R. Liu Sheng, P. Goes, When Is the Right Time to Refresh Knowledge Discovered from Data?. Operations Research. 61(1) (2013), pp. 32-44.

10.1287/opre.1120.1148
94

X. Fang, Inference-based Naïve Bayes: Turning Naïve Bayes Cost-sensitive. IEEE Transactions on Knowledge and Data Engineering. 25(10) (2013), pp. 2302-2313.

10.1109/TKDE.2012.196
95

Z. Li, X. Fang, X. Bai, and O.R. Liu Sheng, Utility-based Link Recommendation for Online Social Networks. Working Paper. 63(6) (2015).

10.1287/mnsc.2016.2446
96

M.A. Brandão, M.M. Moro, G.R. Lopes, and J.P.M. Oliveira, Using Link Semantics to Recommend Collaborations in Academic Social Networks. In Proceedings of the 22nd International Conference on World Wide Web Companion (WWW). (2013), pp. 833-840.

10.1145/2487788.2488058
97

J. Ugander, L. Backstrom, C. Marlow, and J. Kleinberg, Structural Diversity in Social Contagion. Proceedings of the National Academy of Sciences. 109(16) (2012), pp. 5962-5966.

10.1073/pnas.111650210922474360PMC3341012
98

S. Vargas and P. Castells, Rank and Relevance in Novelty and Diversity Metrics for Recommender Systems. In Proceedings of the 5th ACM Conference on Recommender Systems. (2011), pp. 109-116.

10.1145/2043932.2043955
Information
  • Publisher :Sustainable Building Research Center (ERC) Innovative Durable Building and Infrastructure Research Center
  • Publisher(Ko) :건설구조물 내구성혁신 연구센터
  • Journal Title :International Journal of Sustainable Building Technology and Urban Development
  • Volume : 17
  • No :1
  • Pages :138-153
  • Received Date : 2026-01-12
  • Accepted Date : 2026-01-26
  • DOI :https://doi.org/10.22712/susb.20260009
Journal Informaiton International Journal of Sustainable Building Technology and Urban Development International Journal of Sustainable Building Technology and Urban Development
Online Submission
  • scopus
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
  • isc
Journal Informaiton Journal Informaiton - close