Abstract
Link mining/analysis over network has received widespread attention from researchers. Recently, there has been growing interest in measuring relationship strength between entities based on attribute similarity. However, limited work has assessed the competitive advantage of functional elements in relationship strength quantification. The functional elements embody the growth/development nature of the relationship. Motivated by the availability of large volumes of online event records that can potentially reveal underlying functional socio-economic characteristics, we study the problem of offline relationship strength estimation with functional elements awareness from online events. Two major challenges are identified as follows: (1) informal information, online events are of high dimensions, and not all the learnt functions of online events are predictive to offline interactions; (2) heterogeneous dependency, it’s hard to measure the relationship strength by modeling functional elements with network effects jointly. To handle these challenges, we propose generalized relationship strength estimation model (gStrength), a novel approach for relationship strength estimation. First, we define the combination of latent roles and observed groups as generalized roles, and present generalized role constrained latent topic model to make the extracted latent functions compatible with offline interactions. Second, we model the functional elements and further extend them to structural dependency settings to quantify relationship strength. We apply this approach to the political and economic application scenario of measuring international investment relations. The experimental results demonstrate the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
In the rest of the paper, both \(\mathbf {Cr}\) and \(\mathbf {Xr}\) are used directly for simplicity and convenience.
- 2.
Role is exchangeable with function element and latent topic.
- 3.
We refer both attribute \(\mathbf {B}\) and group \(\mathbf {C}\) as generalized attribute \(\bar{\mathbf {{B}}}\).
- 4.
Function/Topic is assigned to every relationship and indicated by \(\mathbf {\Theta }^{(i,j)}\) from relationship representation, here v is the order number for interaction \(\mathbf {Y}^{(i,j)}\).
- 5.
Group is assigned to every relationship and indicated by \(\mathbf {C}^{(cr)}\) from relationship representation, here cr is the order number for group information \(\mathbf {Cr}^{(i,j)}\).
- 6.
\(n_{\varUpsilon ,u}\) and \(n_{v,\varUpsilon }\) denotes the count number of events and topics correspondingly, others are of the same styles and omitted for explanation due to limited space.
- 7.
Auxiliary information \(\mathbf {A}\) is not specified around the whole paper, but we take it in the framework whenever interaction auxiliary information is available for generality.
- 8.
The similarity function is specified as \([{{ }{{\mathbf {B}}^{(i)}\bullet {\mathbf {B}}^{(j)}} }, C(i)\bullet C(j)]\), where \(\bullet \) is the dot product-based score operator.
References
Hasan, M.A., Zaki, M.J.: A survey of link prediction in social networks. In: Aggarwal, C. (ed.) Social Network Data Analytics, pp. 243–275. Springer, Boston (2011)
Althoff, T., Jindal, P., Leskovec, J.: Online actions with offline impact: how online social networks influence online and offline user behavior, pp. 537–546 (2017)
Andrieu, C., Freitas, N.D., Doucet, A., Jordan, M.I.: An introduction to MCMC for machine learn. Mach. Learning 50(1–2), 5–43 (2003)
Backstrom, L., Leskovec, J.: Supervised random walks: predicting and recommending links in social networks. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 635–644. ACM (2011)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Boldi, P.: TotalRank: ranking without damping. In: Special Interest Tracks and Posters of the International Conference on World Wide Web, pp. 898–899 (2005)
Connelly, R., Playford, C.J., Gayle, V., Dibben, C.: The role of administrative data in the big data revolution in social science research. Soc. Sci. Res. 59, 1–12 (2016)
Einav, L., Levin, J.: The data revolution and economic analysis. Innov. Policy Econ. 14(1), 1–24 (2014)
Gao, H., Wang, X., Tang, J., Liu, H.: Network denoising in social media. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 564–571. IEEE (2013)
Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 211–220 (2009)
Granovetter, M.: The strength of weak ties: a network theory revisited. Sociol. Theor. 1(6), 201–233 (1983)
Han, Y., Tang, J.: Probabilistic community and role model for social networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 407–416. ACM (2015)
Hong, L., Frias-Martinez, E., Frias-Martinez, V.: Topic models to infer socio-economic maps. In: Thirtieth AAAI Conference on Artificial Intelligence, pp. 3835–3841 (2016)
Hospedales, T.M., Li, J., Gong, S., Xiang, T.: Identifying rare and subtle behaviors: a weakly supervised joint topic model. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2451–2464 (2011)
Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S.: Combining satellite imagery and machine learning to predict poverty. Science 353(6301), 790–794 (2016)
Leetaru, K., Schrodt, P.A.: Gdelt: global data on events, location, and tone, 1979–2012. In: ISA Annual Convention, vol. 2, pp. 1–49 (2013)
Lou, T., Tang, J., Hopcroft, J., Fang, Z., Ding, X.: Learning to predict reciprocity and triadic closure in social networks. ACM Trans. Knowl. Discov. Data 7(2), 5 (2013)
Luo, P., Yang, S., Yang, S., Yang, S., Yang, S., He, Q.: From online behaviors to offline retailing. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 175–184 (2016)
Ma, C., Zhu, C., Fu, Y., Zhu, H., Liu, G., Chen, E.: Social user profiling: a social-aware topic modeling perspective. In: Candan, S., Chen, L., Pedersen, T.B., Chang, L., Hua, W. (eds.) DASFAA 2017. LNCS, vol. 10178, pp. 610–622. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55699-4_38
Minka, T.: Estimating a Dirichlet distribution. In: UAI-2002, vol. 39, no. 3273, p. 115 (2013)
Pattison, P., Robins, G.: Neighborhood-based models for social networks. Sociol. Method. 32(1), 301–337 (2010)
Robins, G., Elliott, P., Pattison, P.: Network models for social selection processes. Soc. Netw. 23(1), 1–30 (2001)
Rozenshtein, P., Tatti, N., Gionis, A.: Inferring the strength of social ties: a community-driven approach. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1017–1025. ACM (2017)
Schein, A., Paisley, J., Blei, D.M., Wallach, H.: Bayesian poisson tensor factorization for inferring multilateral relations from sparse dyadic event counts. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1045–1054. ACM (2015)
Sintos, S., Tsaparas, P.: Using strong triadic closure to characterize ties in social networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1466–1475. ACM (2014)
Wang, S., Chen, Z., Fei, G., Liu, B., Emery, S.: Targeted topic modeling for focused analysis. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235–1244. ACM (2016)
Xiang, R., Neville, J., Rogati, M.: Modeling relationship strength in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 981–990. ACM (2010)
Zhang, Y., Levina, E., Zhu, J.: Estimating network edge probabilities by neighborhood smoothing. Biometrika 104(4) (2015)
Acknowledgment
This work is supported in part by the National Natural Science Foundation of China Projects No. 91546105, No. U1636207, the National High Technology Research and Development Program of China No. 2015AA020105, the Shanghai Science and Technology Development Fund No. 16JC1400801, No. 17511105502, No. 16511102204.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Liao, C., Xiong, Y., Kong, X., Zhu, Y., Zhao, S., Li, S. (2018). Functional-Oriented Relationship Strength Estimation: From Online Events to Offline Interactions. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-91452-7_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91451-0
Online ISBN: 978-3-319-91452-7
eBook Packages: Computer ScienceComputer Science (R0)