The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have proposed, which can generate sequence description. However, the existing ignore global relationships among API methods, are important for understanding usage APIs. this paper, we propose to model dependencie...

With the booming of Internet Things (IoT) and speedy advancement Location-Based Social Networks (LBSNs), Point-Of-Interest (POI) recommendation has become a vital strategy for supporting people's ability to mine their POIs. However, classical models, such as collaborative filtering, are not effective structuring POI recommendations due sparseness user check-ins. Furthermore, LBSN distinct from ...

Knowledge graphs (KGs) have proven to be effective for high-quality recommendation, where the connectivities between users and items provide rich complementary information user-item interactions. Most existing methods, however, are insufficient exploit KGs capturing user preferences, as they either represent via paths with limited expressiveness or implicitly model them by propagating over enti...

Graph embedding, representing local and global neighbourhood information by numerical vectors, is a crucial part of the mathematical modeling wide range real-world systems. Among embedding algorithms, random walk-based algorithms have proven to be very successful. These collect creating numerous walks with predefined number steps. Creating most demanding process. The computation demand increase...

The existing Diffusion Maps method brings diffusion to data samples by Markov random walk. In this paper, to provide a general solution form of Diffusion Maps, first, we propose the generalized single-graph-diffusion embedding framework on the basis of graph embedding framework. Second, by designing the embedding graph of the framework, an algorithm, namely Locally Discriminant Diffusion Projec...

This paper considers the problem of embedding Knowledge Graphs (KGs) consisting of entities and relations into lowdimensional vector spaces. Most of the existing methods perform this task based solely on observed facts. The only requirement is that the learned embeddings should be compatible within each individual fact. In this paper, aiming at further discovering the intrinsic geometric struct...

Kleinberg [17] proposed in 2000 the first random graph model achieving to reproduce small world navigability, i.e. the ability to greedily discover polylogarithmic routes between any pair of nodes in a graph, with only a partial knowledge of distances. Following this seminal work, a major challenge was to extend this model to larger classes of graphs than regular meshes, introducing the concept...

We present a dynamic data structure for the incremental construction of a planar embedding of a planar graph. The data structure supports the following Ž . operations: i testing if a new edge can be added to the embedding without Ž . introducing crossing; and ii adding vertices and edges. The time complexity of Ž . Ž . each operation is O log n amortized for edge insertion , and the memory spac...