A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Graph representation learning and its applications: a survey
Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …
representation learning is a significant task since it could facilitate various downstream …
Mgat: Multi-view graph attention networks
Multi-view graph embedding is aimed at learning low-dimensional representations of nodes
that capture various relationships in a multi-view network, where each view represents a …
that capture various relationships in a multi-view network, where each view represents a …
Cross-network embedding for multi-network alignment
Recently, data mining through analyzing the complex structure and diverse relationships on
multi-network has attracted much attention in both academia and industry. One crucial …
multi-network has attracted much attention in both academia and industry. One crucial …
Role-based multiplex network embedding
In recent years, multiplex network embedding has received great attention from researchers.
However, existing multiplex network embedding methods neglect structural role information …
However, existing multiplex network embedding methods neglect structural role information …
Multiplex graph neural networks for multi-behavior recommendation
W Zhang, J Mao, Y Cao, C Xu - … of the 29th ACM international conference …, 2020 - dl.acm.org
This paper focuses on the multi-behavior recommendation problem, ie, generating
personalized recommendation based on multiple types of user behaviors. Methods …
personalized recommendation based on multiple types of user behaviors. Methods …
Multilayer network simplification: approaches, models and methods
Multilayer networks have been widely used to represent and analyze systems of
interconnected entities where both the entities and their connections can be of different …
interconnected entities where both the entities and their connections can be of different …
The atlas for the aspiring network scientist
M Coscia - arXiv preprint arXiv:2101.00863, 2021 - arxiv.org
Network science is the field dedicated to the investigation and analysis of complex systems
via their representations as networks. We normally model such networks as graphs: sets of …
via their representations as networks. We normally model such networks as graphs: sets of …
Contrastive multi-view multiplex network embedding with applications to robust network alignment
Despite its success in learning network node representations, network embedding is still
relatively new for multiplex networks (MNs) with multiple types of edges. In such networks …
relatively new for multiplex networks (MNs) with multiple types of edges. In such networks …