PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
Network embedding has numerous practical applications and has received extensive
attention in graph learning, which aims at map** vertices into a low-dimensional and …
attention in graph learning, which aims at map** vertices into a low-dimensional and …
[HTML][HTML] Effective Temporal Graph Learning via Personalized PageRank
Z Liao, T Liu, Y He, L Lin - Entropy, 2024 - mdpi.com
Graph representation learning aims to map nodes or edges within a graph using low-
dimensional vectors, while preserving as much topological information as possible. During …
dimensional vectors, while preserving as much topological information as possible. During …
Scaling Up Graph Propagation Computation on Large Graphs: A Local Chebyshev Approximation Approach
Graph propagation (GP) computation plays a crucial role in graph data analysis, supporting
various applications such as graph node similarity queries, graph node ranking, graph …
various applications such as graph node similarity queries, graph node ranking, graph …
Fed-GLAD: Federated Graph Learning for Anomaly Detection
SA Sharna - 2024 - search.proquest.com
Graph-level anomaly detection (GLAD) has attracted significant attention due to its practical
applications in various real-world domains. Unlike other graph anomaly detection tasks such …
applications in various real-world domains. Unlike other graph anomaly detection tasks such …