KNN classification is an improvisational learning mode, in which they are carried out only
when a test data is predicted that set a suitable K value and search the K nearest neighbors …

In this work, we propose a robust road network representation learning framework called
Toast, which comes to be a cornerstone to boost the performance of numerous demanding …

With the advent of a wide spectrum of recent applications, querying heterogeneous
information networks (HINs) has received a great deal of attention from both academic and …

Computing the shortest path for any two given vertices is an important problem in road
networks. Since real road networks are dynamically updated due to real-time traffic …

In this paper, we study the Time-Dependent k Nearest Neighbor (TD-k NN) query on moving
objects that aims to return k objects arriving at the query location with the least traveling cost …

Modern graphs exert colossal time and space pressure on graph analytics applications. In
2022, Facebook social graph reaches 2.91 billion users with trillions of edges. Many …

With the gradual growth of road networks, the graphs used to represent road networks have
been becoming larger and larger. It is a preferable choice for the user to outsource large …

Shortest path computation is a fundamental operation in many application domains and is
especially challenging in frequently evolving small-world networks (ie, graphs in which …

Dynamic Graph Neural Networks (DGNNs) have demonstrated exceptional performance at
dynamic-graph analysis tasks. However, the costs exceed those incurred by other learning …

Network Kernel Density Visualization (NKDV) has often been used in a wide range of
applications, eg, criminology, transportation science, and urban planning. However, NKDV …