Abstract
In social networks, link prediction is the task to identify links in future. Many existing link prediction techniques used similarity scores to predict links. An essential concern in the link prediction problem is identifying missing links between the nodes when there are no common neighbors between the nodes. Considering this, a new algorithm proposed, namely Similarity-based Algorithm using Degree and Common Neighbour (SADCN) which includes a node's degree in the shortest path and common neighbor. For experiment evaluation, three datasets are used to test our method performance against some standard similarity index and the recently proposed algorithms for link prediction, which depicts that our approach achieved comparable AUC values to those that consider common neighbors and it gives better AUC for those links, where no mutual neighbour between the two nodes exists. Finally, we create feature vectors and use XGB classifiers for predicting links. It shows that our proposed algorithm can improve the F-measure and accuracy in a feature based link prediction model.



Similar content being viewed by others
References
Adamic L, Adar E (2005) How to search a social network. Social Networks 27(3):187–203
Ahmad I, Akhtar MU, Noor S, Shahnaz A (2020) Missing link prediction using common neighbor and centrality based parameterized algorithm. Sci Rep. https://doi.org/10.1038/s41598-019-57304-y
Ahmed C, ElKorany A, Bahgat R (2016) A supervised learning approach to link prediction in Twitter. Soc Netw Anal Min 6:24. https://doi.org/10.1007/s13278-016-0333-1
Ahmed C, ElKorany A (2015) Enhancing link prediction in Twitter using semantic user attributes, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Paris, France, pp 1155–1161. https://doi.org/10.1145/2808797.2810056
Al Hasan M, Chaoji V, Salem S, Zaki M (2006) Link prediction using supervised learning. In SDM06: Workshop on link analysis, counter-terrorism and security 30 798–805
Aouay S, Jamoussi S, Gargouri F (2014) Feature based link prediction, 2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA), pp. 523–527, doi: https://doi.org/10.1109/AICCSA.2014.7073243.
Biggs NL, Lloyd EK, Wilson RJ (1986) Graph Theory, New York, NY, USA, The Clarendon Press, 2nd edition
Chowdhury GG (2010) Introduction to modern information retrieval. Facet publishing
Chuan PM, Son LH, Ali M, Khang TD, Huong LT, Dey N (2017) Link prediction in co-authorship networks based on hybrid content similarity metric. Appl Intell 48(8):2470–2486. https://doi.org/10.1007/s10489-017-1086-x
Chuan PM, Giap CN, Son LH, Bhatt C, Khang TD (2018) Enhance Link Prediction in Online Social Networks Using Similarity Metrics, Sampling, and Classification. In: Bhateja V, Nguyen B, Nguyen N, Satapathy S, Le DN (eds) Information systems design and intelligent applications. Advances in intelligent systems and computing, vol 672. Springer, Singapore. https://doi.org/10.1007/978-981-10-7512-4_81
Clause A, Moore C, Newman MEJ (2008) Hierarchical structure and the prediction of missing links in network’. Nature 453:98–101
Erdős P, Rényi A (1959) On random graphs. I, Publicationes Mathematicae Debrecen 6:290–297
Erdős P, Rényi A (1960) On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences 5:17–61
Esslimani I, Brun A, Boyer A (2011) Densifying a behavioral recommender system by social networks link prediction methods. Social Netw Analys Mining 1(3):159–172
Gündoğan E, Kaya B (2017) A link prediction approach for drug recommendation in disease-drug bipartite network 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pp 1–4. https://doi.org/10.1109/IDAP.2017.8090219.
Güneş İ, Gündüz-Öğüdücü Ş, Çataltepe Z (2016) Link prediction using time series of neighborhood-based node similarity scores. Data Min Knowl Disc 30(1):147–180
Huang Z, Zeng DD (2006) A link prediction approach to anomalous email detection. In: 2006 IEEE international conference on systems, man and cybernetics, vol 2. IEEE, pp 1131-1136
Huang Z, Li X, Chen, H (2005) Link prediction approach to collaborative filtering. In: ACM/IEEE Joint Conference on Digital Libraries, JCDL 2005, Proceedings, pp. 141–142. Denver, CO, USA, 7–11 June
Kashima H, Abe N (2006) A parameterized probabilistic model of network evolution for supervised link prediction[C]//Data Mining, ICDM’06. Sixth International Conference on IEEE 2006:340–349
Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18(1):39–43
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58:1019–1031
Liu M, Guo JF, Luo X (2016) Link prediction based on the similarity of transmission nodes of multiple paths in weighted social networks. Journal of Information Hiding and Multimedia Signal Processing 7(4):771–780
Lusseau D et al (2003) Te bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav Ecol Sociobiol 54:396–405
Mandal H, Mirchev M, Gramatikov S, Mishkovski I (2018) Multilayer link prediction in online social networks, 26th telecommunications forum (TELFOR). Belgrade 2018:1–4. https://doi.org/10.1109/TELFOR.2018.8612122
Murata T , Moriyasu S (2007) Link prediction of social networks based on weighted proximity measures. Proceedings of the IEEE/WIC/ACM international conference on web intelligence 85–8
Narasimhan J, Holder L (2014) Feature engineering for supervised link prediction on dynamic social networks. In:Proceedings of the 10th international conference on data mining, p 1
Newman M (2001) Clustering and preferential attachment in growing networks. Physical ReviewE 64(2):025102
Newman M (2003) The structure and function of complex networks. SIAM Rev 45(2):167256
Papadimitriou A, Symeonidis P, Manolopoulos Y (2012) Fast and accurate link prediction in social networking systems. J Syst Softw 85:2119–2132. https://doi.org/10.1016/j.jss.2012.04.019
Rafiee S, Salavati C, Abdollahpouri A (2020) CNDP: Link prediction based on common neighbors degree penalization, physica a: statistical mechanics and its applications, volume 539. ISSN 122950:0378–4371. https://doi.org/10.1016/j.physa.2019.122950
Singh AN (2019a) Improved link prediction using PCA. Int J Anal Appl 17(4):578–585
Singh N (2019b) A link prediction model using similarity and centrality based features. In: 6th International conference on computing for sustainable global development (INDIACom), pp 415–417
Sun Y, Barber R, Gupta M (2011) Co-author relationship prediction in heterogeneous bibliographic networks’, In: IEEE 2011 international conference on advances in social networks analysis and mining (ASONAM), pp 121
Symeonidis P, Tiakas E, Manolopoulos Y (2010) Transitive Node Similarity for Link Prediction in Social Networks with Positive and Negative Links,” In proceedings of the 4th ACM conference on Recommender systems (RecSys '10)
Travers J, Milgram S (1969) An experimental study of the small world problem”. Sociometry 32(4):425–443
Wang C, Satuluri V, Parthasarathy S (2007) Local Probabilistic Models for Link Prediction, Seventh IEEE International Conference on Data Mining (ICDM 2007), pp. 322–331, doi: https://doi.org/10.1109/ICDM.2007.108.
Wu Z, Lin Y, Wang Y, Gregory S (2016a) Link prediction with node clustering coefficient, Physica A: Statistical Mechanics and its Applications, vol. 452, pp. 1–8, 2016.
Wu Z, Lin Y, Wan H, Jamil W (2016b) Predicting top-L missing links with node and link clustering information in large-scale networks. J Stat Mech Theory Exper 8:083202. https://doi.org/10.1088/1742-5468/2016/08/083202
Yadav AK, Maurya AK, Ranvijay, Yadav RS (2021) Extractive text summarization using recent approaches: A survey. Ingénierie des Systèmes d’Information. https://doi.org/10.18280/isi.260112
Yang J, Zhang XD (2016) Predicting missing links in complex networks based on common neighbors and distance. Sci Rep 6:38208
Yao L, Wang L, Pan Lv, Yao K (2016) Link prediction based on common-neighbors for dynamic social network, procedia computer science, volume 83. ISSN 82–89:1877–2509. https://doi.org/10.1016/j.procs.2016.04.102
Yu C, Zhao X, An Lu, Lin X (2016) Similarity-based link prediction in social networks: a path and node combined approach. J Inf Sci. https://doi.org/10.1177/0165551516664039
Yu Z, Kening G, Feng L, Ge, Y (2014) A New Method for Link Prediction Using Various Features in Social Networks," 2014 11th Web Information System and Application Conference, Tianjin,, pp. 144–147, doi: https://doi.org/10.1109/WISA.2014.34.
Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropological Res 33:452–473
Zareie A, Sakellariou R (2020) Similarity-based link prediction in social networks using latent relationships between the users. Sci Rep 10:20137. https://doi.org/10.1038/s41598-020-76799-4
Zheleva E, Getoor L, Golbeck J, Kuter U (2010) Using friendship ties and family circles for link prediction. In: Giles L, Smith M, Yen J, Zhang H (eds) Advances in social network mining and analysis. Springer, Berlin, pp 97–113–128
Zhou T, Lu L, Zhang Y-C (2009) Predicting missing links via local information. Eur Phys J B 71:623
Funding
No external funding is done for this reasearch work. The dataset used here is available online.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interests
The authors declare that they have no conflict of interest.
Human and animals rights
In Social network users make relationships and express their thoughts to each other. Link prediction is one of the main research areas where links/relationship between users can be predicted which may further apply in different fields like recommendation systems, coauthorship networks etc. This research involves finding missing links between users of any social network. For this we have used three dataset which is publicly available: Karat club dataset, dolphin dataset and Polbook dataset. There is no biological material used in the research paper.
Informed consent
Not Applicable as no such study has been done, the dataset used for implementation is publicly available (online). Related reference to the same has been added in the research paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Singh, A., Singh, N. An approach for predicting missing links in social network using node attribute and path information. Int J Syst Assur Eng Manag 13, 944–956 (2022). https://doi.org/10.1007/s13198-021-01371-w
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-021-01371-w