Are heterophily-specific gnns and homophily metrics really effective? evaluation pitfalls and new benchmarks
Over the past decade, Graph Neural Networks (GNNs) have achieved great success on
machine learning tasks with relational data. However, recent studies have found that …
machine learning tasks with relational data. However, recent studies have found that …
Diverse joint nonnegative matrix tri-factorization for attributed graph clustering
Cluster analysis of attributed graphs is a demanding and challenging task in the analysis of
network-structured data. It involves learning node representation by leveraging both node …
network-structured data. It involves learning node representation by leveraging both node …
On performance discrepancies across local homophily levels in graph neural networks
Abstract Graph Neural Network (GNN) research has highlighted a relationship between high
homophily (ie, the tendency of nodes of the same class to connect) and strong predictive …
homophily (ie, the tendency of nodes of the same class to connect) and strong predictive …
SkyMap: a generative graph model for GNN benchmarking
Graph Neural Networks (GNNs) have gained considerable attention in recent years. Despite
the surge in innovative GNN architecture designs, research heavily relies on the same 5-10 …
the surge in innovative GNN architecture designs, research heavily relies on the same 5-10 …
Integrating Fuzzy Clustering and Graph Convolution Network to Accurately Identify Clusters From Attributed Graph
Attributed graph clustering is of significance for an in-depth understanding of the intrinsic
organization of complex networks. Recently, owing to the powerful learning capability of …
organization of complex networks. Recently, owing to the powerful learning capability of …
Efficient Dynamic Attributed Graph Generation
Data generation is a fundamental research problem in data management due to its diverse
use cases, ranging from testing database engines to data-specific applications. However …
use cases, ranging from testing database engines to data-specific applications. However …
[HTML][HTML] From attributes to communities: a novel approach in social network generation
Generating networks with attributes would be useful in computer game development by
enabling dynamic social interactions, adaptive storylines, realistic economic systems …
enabling dynamic social interactions, adaptive storylines, realistic economic systems …
Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance
Graph Neural Networks (GNNs) have excelled in predicting graph properties in various
applications ranging from identifying trends in social networks to drug discovery and …
applications ranging from identifying trends in social networks to drug discovery and …