Large language model meets graph neural network in knowledge distillation
In service-oriented architectures, accurately predicting the Quality of Service (QoS) is crucial
for maintaining reliability and enhancing user satisfaction. However, significant challenges …
for maintaining reliability and enhancing user satisfaction. However, significant challenges …
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 mapping vertices into a low-dimensional and …
attention in graph learning, which aims at mapping vertices into a low-dimensional and …
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs
Graph representation learning (GRL) is to encode graph elements into informative vector
representations, which can be used in downstream tasks for analyzing graph-structured data …
representations, which can be used in downstream tasks for analyzing graph-structured data …
Effective Clustering on Large Attributed Bipartite Graphs
Attributed bipartite graphs (ABGs) are an expressive data model for describing the
interactions between two sets of heterogeneous nodes that are associated with rich …
interactions between two sets of heterogeneous nodes that are associated with rich …
Spectral Subspace Clustering for Attributed Graphs
Subspace clustering seeks to identify subspaces that segment a set of n data points into k
(k<< n) groups, which has emerged as a powerful tool for analyzing data from various …
(k<< n) groups, which has emerged as a powerful tool for analyzing data from various …