Abstract: Graph neural networks (GNNs) are capable of modeling graph data using various types of nodes and edges, and thus can be widely used in the fields of recommender systems and bioinformatics.

A lightweight React + D3.js component for rendering interactive node-edge (force‐directed) graphs. Supports drag, click tooltips (via MUI Popover), dynamic data updates, custom styling, and fully ...

Abstract: Graph neural networks (GNNs) play an increasingly significant role in node classification. Most GNNs have been conducted on balanced datasets. However, real-world graphs often reflect ...

Introduction: Traditional Graph Pattern Matching (GPM) research mainly focuses on improving the accuracy and efficiency of complex network analysis and fast subgraph retrieval. Despite their ability ...

In today’s world, Graph similarity computation (GSC) plays an important role in various applications such as code detection, molecular graph similarity, image matching, etc., by evaluating the ...

Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. This process captures local and global graph ...

I am trying to use graph_nets for the case where there is a graph of one-word nodes and one-word edges for each example in a batch, and the node and edge features are from the word embeddings that are ...