A comprehensive survey of graph embedding: Problems, techniques, and applications
Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …
scenarios. Effective graph analytics provides users a deeper understanding of what is …
Network representation learning: A survey
With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …
increasingly popular to capture complex relationships across various disciplines, such as …
A survey on knowledge graph embedding: Approaches, applications and benchmarks
Y Dai, S Wang, NN **ong, W Guo - Electronics, 2020 - mdpi.com
A knowledge graph (KG), also known as a knowledge base, is a particular kind of network
structure in which the node indicates entity and the edge represent relation. However, with …
structure in which the node indicates entity and the edge represent relation. However, with …
Attributed social network embedding
Embedding network data into a low-dimensional vector space has shown promising
performance for many real-world applications, such as node classification and entity …
performance for many real-world applications, such as node classification and entity …
Flow prediction in spatio-temporal networks based on multitask deep learning
Predicting flows (eg, the traffic of vehicles, crowds, and bikes), consisting of the in-out traffic
at a node and transitions between different nodes, in a spatio-temporal network plays an …
at a node and transitions between different nodes, in a spatio-temporal network plays an …
Graph representation learning: a survey
Research on graph representation learning has received great attention in recent years
since most data in real-world applications come in the form of graphs. High-dimensional …
since most data in real-world applications come in the form of graphs. High-dimensional …
Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation
Recommender system is one of the most popular data mining topics that keep drawing
extensive attention from both academia and industry. Among them, POI (point of interest) …
extensive attention from both academia and industry. Among them, POI (point of interest) …
PMF: A privacy-preserving human mobility prediction framework via federated learning
With the popularity of mobile devices and location-based social network, understanding and
modelling the human mobility becomes an important topic in the field of ubiquitous …
modelling the human mobility becomes an important topic in the field of ubiquitous …
Deepiot: Compressing deep neural network structures for sensing systems with a compressor-critic framework
Recent advances in deep learning motivate the use of deep neutral networks in sensing
applications, but their excessive resource needs on constrained embedded devices remain …
applications, but their excessive resource needs on constrained embedded devices remain …
From itdl to place2vec: Reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts
Understanding, representing, and reasoning about Points Of Interest (POI) types such as
Auto Repair, Body Shop, Gas Stations, or Planetarium, is a key aspect of geographic …
Auto Repair, Body Shop, Gas Stations, or Planetarium, is a key aspect of geographic …