A comprehensive survey on deep graph representation learning methods
IA Chikwendu, X Zhang, IO Agyemang… - Journal of Artificial …, 2023 - jair.org
There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …
representation learning aims to produce graph representation vectors to represent the …
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 …
Bring your own view: Graph neural networks for link prediction with personalized subgraph selection
Graph neural networks (GNNs) have received remarkable success in link prediction
(GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then …
(GNNLP) tasks. Existing efforts first predefine the subgraph for the whole dataset and then …
Constrained social community recommendation
In online social networks, users with similar interests tend to come together, forming social
communities. Nowadays, user-defined communities become a prominent part of online …
communities. Nowadays, user-defined communities become a prominent part of online …
Collaborative graph neural networks for attributed network embedding
Graph neural networks (GNNs) have shown prominent performance on attributed network
embedding. However, existing efforts mainly focus on exploiting network structures, while …
embedding. However, existing efforts mainly focus on exploiting network structures, while …
Towards a Better Tradeoff between quality and efficiency of community detection: An inductive embedding method across graphs
Many network applications can be formulated as NP-hard combinatorial optimization
problems of community detection (CD) that partitions nodes of a graph into several groups …
problems of community detection (CD) that partitions nodes of a graph into several groups …
A learned sketch for subgraph counting
Subgraph counting, as a fundamental problem in network analysis, is to count the number of
subgraphs in a data graph that match a given query graph by either homomorphism or …
subgraphs in a data graph that match a given query graph by either homomorphism or …
Scaling attributed network embedding to massive graphs
Given a graph G where each node is associated with a set of attributes, attributed network
embedding (ANE) maps each node v∈ G to a compact vector Xv, which can be used in …
embedding (ANE) maps each node v∈ G to a compact vector Xv, which can be used in …
Efficient and effective edge-wise graph representation learning
Graph representation learning (GRL) is a powerful tool for graph analysis, which has gained
massive attention from both academia and industry due to its superior performance in …
massive attention from both academia and industry due to its superior performance in …
Personalized pagerank on evolving graphs with an incremental index-update scheme
\em Personalized PageRank (PPR) stands as a fundamental proximity measure in graph
mining. Given an input graph G with the probability of decay α, a source node s and a target …
mining. Given an input graph G with the probability of decay α, a source node s and a target …