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Understanding mechanisms driving link formation in dynamic social networks is a long-standing problem that has implications to understanding social structure as well as link prediction and recommendation. Social networks exhibit a high degree of transitivity, which explains the successes of common neighbor-based methods for link prediction. In this paper, we examine mechanisms behind link formation from the perspective of an ego node. We introduce the notion of personalized degree for each neighbor node of the ego, which is the number of other neighbors a particular neighbor is connected to. From empirical analyses on four on-line social network datasets, we find that neighbors with higher personalized degree are more likely to lead to new link formations when they serve as common neighbors with other nodes, both in undirected and directed settings. This is complementary to the finding of Adamic and Adar that neighbor nodes with higher (global) degree are less likely to lead to new link formations. Furthermore, on directed networks, we find that personalized out-degree has a stronger effect on link formation than personalized in-degree, whereas global in-degree has a stronger effect than global out-degree. We validate our empirical findings through several link recommendation experiments and observe that incorporating both personalized and global degree into link recommendation greatly improves accuracy.
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Algorithmic Support for Personalized Course Selection and Scheduling
The work presented in this paper demonstrates the use of context-aware recommendation to facilitate personalized education, by assisting students in selecting courses and course content and mapping a trajectory to graduation. The recommendation algorithm considers a student's profile and their program's curricular requirements in generating a schedule of courses, while aiming to reduce attributes such as cost and time-to-degree. The resulting optimization problem is solved using integer linear programming and graph-based heuristics. The course selection algorithm has been developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with assurance that recommended selections are always valid.
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- Award ID(s):
- 1742523
- PAR ID:
- 10219704
- Date Published:
- Journal Name:
- 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)
- Page Range / eLocation ID:
- 143 to 152
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/COMPSAC48688.2020.00027
