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.
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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.
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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.
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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
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