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 …
Topic analysis and development in knowledge graph research: A bibliometric review on three decades
Abstract Knowledge graph as a research topic is increasingly popular to represent structural
relations between entities. Recent years have witnessed the release of various open-source …
relations between entities. Recent years have witnessed the release of various open-source …
Fast gradient attack on network embedding
Network embedding maps a network into a low-dimensional Euclidean space, and thus
facilitate many network analysis tasks, such as node classification, link prediction and …
facilitate many network analysis tasks, such as node classification, link prediction and …
Topological recurrent neural network for diffusion prediction
In this paper, we study the problem of using representation learning to assist information
diffusion prediction on graphs. In particular, we aim at estimating the probability of an …
diffusion prediction on graphs. In particular, we aim at estimating the probability of an …
Is a single embedding enough? learning node representations that capture multiple social contexts
Recent interest in graph embedding methods has focused on learning a single
representation for each node in the graph. But can nodes really be best described by a …
representation for each node in the graph. But can nodes really be best described by a …
Ddgk: Learning graph representations for deep divergence graph kernels
Can neural networks learn to compare graphs without feature engineering? In this paper, we
show that it is possible to learn representations for graph similarity with neither domain …
show that it is possible to learn representations for graph similarity with neither domain …
[PDF][PDF] Survey on graph embeddings and their applications to machine learning problems on graphs
Dealing with relational data always required significant computational resources, domain
expertise and task-dependent feature engineering to incorporate structural information into a …
expertise and task-dependent feature engineering to incorporate structural information into a …
Semantic proximity search on heterogeneous graph by proximity embedding
Many real-world networks have a rich collection of objects. The semantics of these objects
allows us to capture different classes of proximities, thus enabling an important task of …
allows us to capture different classes of proximities, thus enabling an important task of …
Smoothing adversarial training for GNN
J Chen, X Lin, H Xiong, Y Wu… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Recently, a graph neural network (GNN) was proposed to analyze various graphs/networks,
which has been proven to outperform many other network analysis methods. However, it is …
which has been proven to outperform many other network analysis methods. However, it is …
Envisioning insight-driven learning based on thick data analytics with focus on healthcare
J Fiaidhi - Ieee Access, 2020 - ieeexplore.ieee.org
Detecting and analyzing patient insights from social media enables healthcare givers to
better understand what patients want and also to identify their pain points. Healthcare …
better understand what patients want and also to identify their pain points. Healthcare …