A Survey of Intelligent End-to-End Networking Solutions: Integrating Graph Neural Networks and Deep Reinforcement Learning Approaches
This paper provides a comprehensive survey of the integration of graph neural networks
(GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions …
(GNN) and deep reinforcement learning (DRL) in end-to-end (E2E) networking solutions …
T2-gnn: Graph neural networks for graphs with incomplete features and structure via teacher-student distillation
Abstract Graph Neural Networks (GNNs) have been a prevailing technique for tackling
various analysis tasks on graph data. A key premise for the remarkable performance of …
various analysis tasks on graph data. A key premise for the remarkable performance of …
Homophily-oriented heterogeneous graph rewiring
With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG)
have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown …
have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown …
Unveiling privacy vulnerabilities: Investigating the role of structure in graph data
The public sharing of user information opens the door for adversaries to infer private data,
leading to privacy breaches and facilitating malicious activities. While numerous studies …
leading to privacy breaches and facilitating malicious activities. While numerous studies …
Osgnn: Original graph and subgraph aggregated graph neural network
Abstract Heterogeneous Graph Embedding (HGE) is receiving a great attention from
researchers, as it can be widely and effectively used to solve problems from various real …
researchers, as it can be widely and effectively used to solve problems from various real …
Graph neural networks with diverse spectral filtering
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph
machine learning, with polynomial filters applied for graph convolutions, where all nodes …
machine learning, with polynomial filters applied for graph convolutions, where all nodes …
The devil is in the conflict: Disentangled information graph neural networks for fraud detection
Graph-based fraud detection has heretofore received considerable attention. Owning to the
great success of Graph Neural Networks (GNNs), many approaches adopting GNNs for …
great success of Graph Neural Networks (GNNs), many approaches adopting GNNs for …
A Re-evaluation of Deep Learning Methods for Attributed Graph Clustering
Attributed graph clustering aims to partition the nodes in a graph into groups such that the
nodes in the same group are close in terms of graph proximity and also have similar attribute …
nodes in the same group are close in terms of graph proximity and also have similar attribute …
ETGraph: A Pioneering Dataset Bridging Ethereum and Twitter
While numerous public blockchain datasets are available, their utility is constrained by a
singular focus on blockchain data. This constraint limits the incorporation of relevant social …
singular focus on blockchain data. This constraint limits the incorporation of relevant social …
Simga: A simple and effective heterophilous graph neural network with efficient global aggregation
Graph neural networks (GNNs) realize great success in graph learning but suffer from
performance loss when meeting heterophily, ie neighboring nodes are dissimilar, due to …
performance loss when meeting heterophily, ie neighboring nodes are dissimilar, due to …