Abstract
As social networks continue growing in size and popularity, so does the amount of data that is publically available pertaining to the members of these networks, which results in a rapid growth of the types of connections that can potentially be established between members of the network. With this increasing amount of available information, the task of examining social networks becomes more exciting and more difficult at the same time. Researchers now have to filter the large field of potentially useful information to find the golden nuggets that will serve their needs, while ignoring data that would actually hamper their experiments. To address this issue, we propose a novel integration of a variogram-based system for selecting potentially informative relationships with a Gaussian Conditional Random Field (GCRF) model that is able to perform node attribute prediction in social networks that are both temporal and contain a wide variety of relationship types. By first evaluating the large pool of relationships via variograms, which can be done very quickly, we are able to fully utilize the power of the GCRF model, which becomes computationally expensive when the network size or the number of relationships examined grows. Our experiments show that we can use variograms to narrow the potential field of relationships drastically and consequently make the GCRF model run in a reasonable amount of time and remain accurate.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
References
Bethard S, Jurafsky D(2010) Who should i cite: learning literature search models from citation behavior. In: Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM ’10, ACM, New York, NY, USA, p 609–618
Castillo C, Donato D, Gionis A (2007) Estimating number of citations using author reputation. In: Proceedings of the 14th international conference on String processing and information retrieval, SPIRE’07, Springer-Verlag, Berlin, Heidelberg, p 107–117
Lafferty JD, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML ’01, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, p 282–289
Liu C, Adelson EH, Freeman WT (2007) Learning Gaussian Conditional Random Fields for low-level vision. In: Proc. of CVPR, p 7
Manjunatha JN, Sivaramakrishnan KR, Pandey RK, Murthy MN (2003) Citation prediction using time series approach kdd cup 2003 (task 1). SIGKDD Explor Newsl 5(2):152–153
Ouzienko V, Guo Y, Obradovic Z (2011) A decoupled exponential random graph model for prediction of structure and attributes in temporal social networks. Stat Anal Data Min 4(5):470–486
Qin T, Liu TY, Zhang XD, Wang DS, Li H (2008) Global ranking using continuous conditional random fields. In Koller D, Schuurmans D, Bengio Y, Bottou L, Koller D, Schuurmans D, Bengio Y, Bottou L (eds) NIPS, MIT Press, USA, p 1281–1288
Radosavljevic V, Vucetic S, Obradovic Z (2010) Continuous conditional random fields for regression in remote sensing. In: Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence, IOS Press, Amsterdam, The Netherlands, p 809–814
Ristovski K, Radosavljevic V, Vucetic S, Obradovic Z (2013) Continuous conditional random fields for efficient regression in large fully connected graphs. In desJardins M, Littman ML (eds) AAAI press, USA, p 840–846
Shibata N, Kajikawa Y, Sakata I (2012) Link prediction in citation networks. J Am Soc Inf Sci Technol 63(1):78–85
Sutton C, Mccallum A (2007) An introduction to conditional random fields for relational learning. In Getoor L, Taskar B (eds) Introduction to statistical relational learning
Uversky A, Ramljak D, Radosavljevic V, Ristovski K, Obradovic Z (2013) Which links should i use?: a variogram-based selection of relationship measures for prediction of node attributes in temporal multigraphs. In Rokne JG, Faloutsos C (eds) ASONAM, ACM, USA, p 676–683
Walter FE, Battiston S, Schweitzer F (2008) A model of a trust-based recommendation system on a social network. Auton Agents Multi-Agent Syst 16(1):57–74
Yan, R., Huang, C., Tang, J., Zhang, Y., and Li, X. (2012). To better stand on the shoulder of giants. In: JCDL, p 51–60
Yang W-S, Dia J-B, Cheng H-C, Lin H-T (2006) Mining social networks for targeted advertising. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences, volume 06, HICSS ’06, IEEE Computer Society, Washington, DC, USA, p 137.1
Yu X, Gu Q, Zhou M, Han J (2012) Citation prediction in heterogeneous bibliographic networks. In: SDM’12, p 1119–1130
Acknowledgments
We are very grateful to the anonymous reviewers whose excellent suggestions really helped us revise the initial submission of this work. This work is supported in part by DARPA Grant FA9550-12-1-0406 negotiated by AFOSR.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Uversky, A., Ramljak, D., Radosavljević, V. et al. Panning for gold: using variograms to select useful connections in a temporal multigraph setting. Soc. Netw. Anal. Min. 4, 211 (2014). https://doi.org/10.1007/s13278-014-0211-7
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13278-014-0211-7