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P2P lending is an emerging Internet-based application where individuals can directly borrow money from each other. The past decade has witnessed the rapid development and prevalence of online P2P lending platforms, examples of which include Prosper, LendingClub, and Kiva. Meanwhile, extensive research has been done that mainly focuses on the studies of platform mechanisms and transaction data. In this article, we provide a comprehensive survey on the research about P2P lending, which, to the best of our knowledge, is the first focused effort in this field. Specifically, we first provide a systematic taxonomy for P2P lending by summarizing different types of mainstream platforms and comparing their working mechanisms in detail. Then, we review and organize the recent advances on P2P lending from various perspectives (e.g., economics and sociology perspective, and data-driven perspective). Finally, we propose our opinions on the prospects of P2P lending and suggest some future research directions in this field. Meanwhile, throughout this paper, some analysis on real-world data collected from Prosper and Kiva are also conducted.

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Social network embedding, namely, embedding social network nodes into a low-dimensional space, is the foundation of social network analysis, such as node classification and link prediction. Although many existing methods attempt to address this task, most of them only consider the shallow relationship between two nodes in the network, which ignore capturing multiple and semantic-rich social relationships between users. To this end, we define such multiple and semantic-rich relationships as multi-path relationships, and propose a multi-path relationship preserved social network embedding method named MPR-SNE, which is based on the recurrent neural network framework that incorporates both social network structure and node profile information. Specifically, we first utilize random walks to explore the multiple social relationship paths between nodes. Then, a new recurrent unit called bi-directional multi-path relationship unit is proposed to better capture the properties of multi-path relationships. Finally, two objective functions are designed to seamlessly integrate social network structure and node profile information into node representation. The experimental results on two real-world networks show that MPR-SNE outperforms the state-of-the-art baselines on node classification task and link prediction task. INDEX TERMS Social network embedding, multi-path relationship, node profile information, RNN.