Name
Abstract | ||
|---|---|---|
Recent advances in network representation learning have enabled significant improvements in the link prediction task, which is at the core of many downstream applications. As an increasing amount of mobility data becoming available due to the development of location technologies, we argue that this resourceful user mobility data can be used to improve link prediction performance. In this paper, we propose a novel link prediction framework that utilizes user offline check-in behavior combined with user online social relations. We model user offline location preference via probabilistic factor model and represent user social relations using neural network embedding. Furthermore, we employ locality-sensitive hashing to project the aggregated user representation into a binary matrix, which not only preserves the data structure but also speeds up the followed convolutional network learning. By comparing with several baseline methods that solely rely on social network or mobility data, we show that our unified approach significantly improves the performance.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3269206.3269244 | CIKM |
Keywords | Field | DocType |
link prediction, network embedding, locality-sensitive hashing | Locality-sensitive hashing,Data mining,Data structure,Embedding,Social network,Logical matrix,Computer science,Hash function,Probabilistic logic,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-1-4503-6014-2 | 3 | 0.39 |
References | Authors | |
21 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Zhou | 1 | 39 | 14.05 |
| Bangying Wu | 2 | 3 | 0.39 |
| Yi Yang | 3 | 30 | 4.22 |
| Goce Trajcevski | 4 | 1732 | 141.26 |
| Kunpeng Zhang | 5 | 156 | 26.02 |
| Ting Zhong | 6 | 14 | 2.21 |