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Advanced algorithm to study catalysts on material surfaces could lead to better batteries
A new algorithm opens the door for using artificial intelligence and machine learning to study the interactions that happen ...
IEEE2mon
A visual canonical adjacency matrix for graphs - IEEE Xplore
Abstract: Graph data mining algorithms rely on graph canonical forms to compare different graph structures. These canonical form definitions depend on node and edge labels. In this paper, we introduce ...
IEEE2y
Contrastive Graph Convolutional Networks With Generative Adjacency Matrix - IEEE Xplore
Semi-supervised node classification with Graph Convolutional Network (GCN) is an attractive topic in social media analysis and applications. Recent studies show that GCN-based classification methods ...
datanami.com3y
TigerGraph Unveils Enhanced Graph Data Science Library With More Than 50 Algorithms
Nov. 11, 2021 — TigerGraph, provider of the leading graph analytics platform, has announced its enhanced Graph Data Science Library (previously named the GSQL Graph Algorithm Library.) This latest ...
syncedreview3y
Graph Kernel Attention Transformers: Toward Expressive and Scalable Graph Processing
The ability of graph neural networks (GNNs) to deal with graph data structures has made them suitable for real-life applications in social networks, bioinformatics, and navigation and planning ...
Simon Fraser University4y
Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data | Pixels & Predictions - Simon Fraser University
Compound graphs, a frequently encountered type of data set, have a hierarchical tree structure with parent-child relations (‘inclusion’ relations) and non-hierarchical relations between leaf nodes ...