Abstract
Online Social Networks have embarked on the importance of connection strength measures which has a broad array of applications such as analyzing diffusion behaviors, community detection, link predictions and recommender systems. Though there are some existing connection strength measures, the density that a connection shares with it’s neighbors and the directionality aspect has not received much attention. In this paper, we have proposed an asymmetric edge similarity measure, namely Neighborhood Density-based Edge Similarity (NDES) which provides a fundamental support to derive the strength of connection. The time complexity of NDES is \(O(nk^2)\). An application of NDES for community detection in social networks is shown. We have considered a similarity based community detection technique and substituted its similarity measure with NDES. The performance of NDES is evaluated on several small real-world datasets in terms of the effectiveness in detecting communities and compared with three widely used similarity measures. Empirical results show NDES enables detecting comparatively better communities both in terms of accuracy and quality.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818
Lü L, Zhou T (2011) Link prediction in complex networks: a survey. Physica A Stat Mech Appl 390(6):1150–1170
Al Hasan M, Zaki MJ (2011) A survey of link prediction in social networks. In: Social network data analytics. Springer, pp 243–275
Martínez V, Berzal F, Cubero J-C (2016) A survey of link prediction in complex networks. ACM Comput Surv (CSUR) 49(4):1–33
Dunlavy DM, Kolda TG, Acar E (2011) Temporal link prediction using matrix and tensor factorizations. ACM Trans Knowl Discov Data (TKDD) 5(2):1–27
Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58(7):1019–1031
Jaccard P (1901) Étude comparative de la distribution florale dans uneportion des alpes et des jura. Bull Soc Vaudoise Sci Nat 37:547–579
Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc Netw 25(3):211–230
Zhou T, L ü L, Zhang Y-C (2009) Predicting missing links via local information. Euro Phys J B 71(4):623–630
Sorensen TA (1948) A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. Biol Skar 5:1–34
Salton G, McGill MJ (1983) Introduction to modern information retrieval. Mcgraw-Hill
Tan F, Xia Y, Zhu B (2014) Link prediction in complex networks: a mutual information perspective. PloS one 9(9):e107056
Barabâsi A-L, Jeong H, Néda Z, Ravasz E, Schubert A, Vicsek T (2002) Evolution of the social network of scientific collaborations. Phys A Stat Mech Appl 311(3-4):590–614
Ghorbanzadeh H, Sheikhahmadi A, Jalili M, Sulaimany S (2021) Ahybrid method of link prediction in directed graphs. Exp Syst Appl 165:113896
Aghabozorgi F, Khayyambashi MR (2018) A new similarity measure for link prediction based on local structures in social networks. Physica A Stat Mech Appl 501:12–23
Li S, Huang J, Zhang Z, Liu J, Huang T, Chen H (2018) Similarity-based future common neighbors model for link prediction in complex networks. Sci Rep 8(1):1–11
Jiang Y, Jia C, Yu J (2013) An efficient community detection method based on rank centrality. Phys A Stat Mech Appl 392(9):2182–2194
Li Y, Jia C, Yu J (2015) A parameter-free community detection methodbased on centrality and dispersion of nodes in complex networks. Phys A Stat Mech Appl 438:321–334
Wang T, Wang H, Wang X (2015) A novel cosine distance for detecting communities in complex networks. Phys A Stat Mech Appl 437:21–35
Eustace J, Wang X, Cui Y (2015) Community detection using local neighborhood in complex networks. Phys A Stat Mech Appl 436:665–677
Zhou H (2003) Distance, dissimilarity index, and network community structure. Phys Rev E 67(6):061901
Clauset A (2005) Finding local community structure in networks. Phys Rev E 72(2):026132
Bagrow JP, Bollt EM (2005) Local method for detecting communities. Phys Rev E 72(4):046108
Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106
Das S, Biswas A (2021) Community detection in social networks using local topology and information exchange. In: 2021 international conference on intelligent technologies (CONIT). IEEE, Hubli, pp 1–7
Das S, Biswas A (2021) Deployment of information diffusion for community detection in online social networks: a comprehensive review. IEEE Trans Comput Soc Syst 8(5):1083–1107
Chakraborty T, Dalmia A, Mukherjee A, Ganguly N (2017) Metrics for community analysis: a survey. ACM Comput Surv (CSUR) 50(4):1–37
SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data. Accessed 27 Sep 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Das, S., Biswas, A. (2023). The Ties that Matter: From the Perspective of Similarity Measure in Online Social Networks. In: Doriya, R., Soni, B., Shukla, A., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. Lecture Notes in Electrical Engineering, vol 946. Springer, Singapore. https://doi.org/10.1007/978-981-19-5868-7_47
Download citation
DOI: https://doi.org/10.1007/978-981-19-5868-7_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-5867-0
Online ISBN: 978-981-19-5868-7
eBook Packages: Computer ScienceComputer Science (R0)