Social networks like Facebook, X (Twitter), and LinkedIn provide an interaction and communication environment for users to generate and share content, allowing for the observation of social behaviours in the digital world. These networks can be viewed as a collection of nodes and edges, where users and their interactions are represented as nodes and the connections between them as edges. Understanding the factors that contribute to the formation of these edges is important for studying network structure and processes. This knowledge can be applied to various areas such as identifying communities, recommending friends, and targeting online advertisements. Several factors, including node popularity and friends-of-friends relationships, influence edge formation and network growth. This research focuses on the temporal activity of nodes and its impact on edge formation. Specifically, the study examines how the minimum age of friends-of-friends edges and the average age of all edges connected to potential target nodes influence the formation of network edges. Discrete choice analysis is used to analyse the combined effect of these temporal factors and other well-known attributes like node degree ( i.e. , the number of connections a node has) and network distance between nodes. The findings reveal that temporal properties have a similar impact as network proximity in predicting the creation of links. By incorporating temporal features into the models, the accuracy of link prediction can be further improved.
Atteveldt, W. and Peng, T.Q. (2018) When Communication Meets Computation: Opportunities, Challenges, and Pitfalls in Computational Communication Science. Communication Methods and Measures, 12, 81-92. https://doi.org/10.1080/19312458.2018.1458084
Bhagat, S., Cormode, G. and Muthukrishnan, S. (2011) Node Classification in Social Networks. In: Aggarwal, C., Ed., Social Network Data Analytics, Springer, Boston, 115-148. https://doi.org/10.1007/978-1-4419-8462-3_5
Fortunato, S. (2010) Community Detection in Graphs. Physics Reports, 486, 75-174. https://doi.org/10.1016/j.physrep.2009.11.002
Liben-Nowell, D. and Kleinberg, J. (2007) The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58, 1019-1031. https://doi.org/10.1002/asi.20591
Zhou, T., Ren, J., Medo, M. and Zhang, Y.C. (2007) Bipartite Network Projection and Personal Recommendation. Physical Review E, 76, Article ID: 046115. https://doi.org/10.1103/PhysRevE.76.046115
Li, Y., Zhang, D. and Tan, K.L. (2015) Real-Time Targeted Influence Maximization for Online Advertisements. Proceedings of the VLDB Endowment, 8, 1070-1081. https://doi.org/10.14778/2794367.2794376
Tang, X. and Yang, C.C. (2012) Ranking User Influence in Healthcare Social Media. ACM Transactions on Intelligent Systems and Technology, 3, Article No. 73. https://doi.org/10.1145/2337542.2337558
Peng, S., Wang, G. and Xie, D. (2017) Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges. IEEE Network, 31, 11-17. https://doi.org/10.1109/MNET.2016.1500104NM
Guo, Z., Zhang, Z.M., Zhu, S., Chi, Y. and Gong, Y. (2014) A Two-Level Topic Model towards Knowledge Discovery from Citation Networks. IEEE Transactions on Knowledge and Data Engineering, 26, 780-794. https://doi.org/10.1109/TKDE.2013.56
Train, K.E. (2009) Discrete Choice Methods with Simulation. 2nd Edition, Cambridge University Press, Cambridge.
Barabási, A.L. and Albert, R. (1999) Emergence of Scaling in Random Networks. Science, 286, 509-512. https://doi.org/10.1126/science.286.5439.509
Bagrow, J.P. and Dirk Brockmann, D. (2013) Natural Emergence of Clusters and Bursts in Network Evolution. Physical Review X, 3, Article ID: 021016. https://doi.org/10.1103/PhysRevX.3.021016
Caldarelli, G., Capocci, A., Rios, P.D.L. and Munoz, M.A. (2002) Scale-Free Networks from Varying Vertex Intrinsic Fitness. Physical Review Letters, 89, Article ID: 258702. https://doi.org/10.1103/PhysRevLett.89.258702
Holme, P. and Kim, B.J. (2002) Growing Scale-Free Networks with Tunable Clustering. Physical Review E, 65, Article ID: 026107. https://doi.org/10.1103/PhysRevE.65.026107
Jackson, M.O. and Rogers, B.W. (2007) Meeting Strangers and Friends of Friends: How Random Are Social Networks? American Economic Review, 97, 890-915. https://doi.org/10.1257/aer.97.3.890
Mislove, A., Koppula, H.S., Gummadi, K.P., Druschel, P. and Bhattacharjee, B. (2008) Growth of the Flickr Social Network. WOSN’08: Proceedings of the First Workshop on Online Social Networks, Seattle, 18 August 2008, 25-30. https://doi.org/10.1145/1397735.1397742
Callaway, D.S., Hopcroft, J.E., Kleinberg, J.M., Newman, M.E.J. and Strogatz, S.H. (2001) Are Randomly Grown Graphs Really Random? Physical Review E, 64, Article ID: 041902. https://doi.org/10.1103/PhysRevE.64.041902
Rapoport, A. (1953) Spread of Information through a Population with Socio-Structural Bias: I. Assumption of Transitivity. Bulletin of Mathematical Biophysics, 15, 523-533. https://doi.org/10.1007/BF02476440
Overgoor, J., Benson, A.R. and Ugander, J. (2019) Choosing to Grow a Graph: Modeling Network Formation as Discrete Choice. Proceedings of the Web Conference 2019 (WWW’19), San Francisco, 13-17 May 2019, 1409-1420. https://doi.org/10.1145/3308558.3313662
Zhang, R., Lee, D.S. and Chang, C.S. (2022) A Generalized Configuration Model with Triadic Closure. https://arxiv.org/pdf/2105.11688.pdf
Toprak, M., Boldrini, C., Passarella, A. and Conti, M. (2022) Harnessing the Power of Ego Network Layers for Link Prediction in Online Social Networks. IEEE Transactions on Computational Social Systems, 10, 48-60. https://doi.org/10.1109/TCSS.2022.3155946
Martínez, V., Berzal, F. and Cubero, J.C. (2016) A Survey of Link Prediction in Complex Networks. ACM Computing Surveys, 49, 1-33. https://doi.org/10.1145/3012704
Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A. and Vandergheynst, P. (2017) Geometric Deep Learning: Going beyond Euclidean Data. IEEE Signal Processing Magazine, 34, 18-42. https://doi.org/10.1109/MSP.2017.2693418
Zhang, M. and Chen, Y. (2018) Link Prediction Based on Graph Neural Networks. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, 3-8 December 2018, 5171-5181.
Zhang, M. (2022) Graph Neural Networks: Link Prediction. In: Wu, L., Cui, P., Pei, J. and Zhao, L., Eds., Graph Neural Networks: Foundations, Frontiers, and Applications, Springer, Singapore, 195-223. https://doi.org/10.1007/978-981-16-6054-2_10
Xu, D., Ruan, C., Korpeoglu, E., Kumar, S. and Achan, K. (2020) Inductive Representation Learning on Temporal Graphs. Proceedings of the International Conference on Learning Representations, Addis Ababa, 30 April 2020. https://openreview.net/forum?id=rJeW1yHYwH
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F. and Bronstein, M. (2020) Temporal Graph Networks for Deep Learning on Dynamic Graphs. arXiv: 2006.10637.
Carchiolo, V., Cavallo, C., Grassia, M., Malgeri, M. and Mangioni, G. (2022) Link Prediction in Time Varying Social Networks. Information, 13, Article 123. https://doi.org/10.3390/info13030123
Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P. and Bhattacharjee, B. (2007) Measurement and Analysis of Online Social Networks. Proceedings of the 7th SIGCOMM Conference on Internet Measurement, San Diego, 24-26 October 2007, 29-42. https://doi.org/10.1145/1298306.1298311
Croissant, Y. (2020) Estimation of Random Utility Models in R: The Mlogit Package. Journal of Statistical Software, 95, 1-41. https://doi.org/10.18637/jss.v095.i11