Network representation learning: from preprocessing, feature extraction to node embedding
Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …
networks, knowledge graphs, and complex biomedical and physics information networks …
A time-dependent SIR model for COVID-19 with undetectable infected persons
In this paper, we conduct mathematical and numerical analyses for COVID-19. To predict the
trend of COVID-19, we propose a time-dependent SIR model that tracks the transmission …
trend of COVID-19, we propose a time-dependent SIR model that tracks the transmission …
Multi-scale attributed node embedding
We present network embedding algorithms that capture information about a node from the
local distribution over node attributes around it, as observed over random walks following an …
local distribution over node attributes around it, as observed over random walks following an …
Machine learning on graphs: A model and comprehensive taxonomy
There has been a surge of recent interest in graph representation learning (GRL). GRL
methods have generally fallen into three main categories, based on the availability of …
methods have generally fallen into three main categories, based on the availability of …
Influence maximization in social networks using graph embedding and graph neural network
With the boom in technologies and mobile networks in recent years, online social networks
have become an integral part of our daily lives. These virtual networks connect people …
have become an integral part of our daily lives. These virtual networks connect people …
Characteristic functions on graphs: Birds of a feather, from statistical descriptors to parametric models
In this paper, we propose a flexible notion of characteristic functions defined on graph
vertices to describe the distribution of vertex features at multiple scales. We introduce …
vertices to describe the distribution of vertex features at multiple scales. We introduce …
A comprehensive review of community detection in graphs
The study of complex networks has significantly advanced our understanding of community
structures which serves as a crucial feature of real-world graphs. Detecting communities in …
structures which serves as a crucial feature of real-world graphs. Detecting communities in …
Karate Club: an API oriented open-source python framework for unsupervised learning on graphs
Graphs encode important structural properties of complex systems. Machine learning on
graphs has therefore emerged as an important technique in research and applications. We …
graphs has therefore emerged as an important technique in research and applications. We …
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …
fundamental yet challenging task. Benefiting from the powerful representation capability of …
Influence maximization in social networks using transfer learning via graph-based LSTM
Social networks have emerged as efficient platforms to connect people worldwide and
facilitate the rapid spread of information. Identifying influential nodes in social networks to …
facilitate the rapid spread of information. Identifying influential nodes in social networks to …