Edge Classification
22 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Edge Classification
Most implemented papers
Charged particle tracking via edge-classifying interaction networks
GageDeZoort/interaction_network_paper
•
•
Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high energy particle physics.
STaCK: Sentence Ordering with Temporal Commonsense Knowledge
declare-lab/sentence-ordering
•
•
EMNLP 2021
Sentence order prediction is the task of finding the correct order of sentences in a randomly ordered document.
Classifying Dyads for Militarized Conflict Analysis
conflict-ai/conflictwiki
•
•
EMNLP 2021
We do this by devising a set of textual and graph-based features which represent each of the causes.
HEAT: Holistic Edge Attention Transformer for Structured Reconstruction
This paper presents a novel attention-based neural network for structured reconstruction, which takes a 2D raster image as an input and reconstructs a planar graph depicting an underlying geometric structure.
Graph Neural Network for Cell Tracking in Microscopy Videos
By modeling the entire time-lapse sequence as a direct graph where cell instances are represented by its nodes and their associations by its edges, we extract the entire set of cell trajectories by looking for the maximal paths in the graph.
Graph Representation Learning Beyond Node and Homophily
Unsupervised graph representation learning aims to distill various graph information into a downstream task-agnostic dense vector embedding.
DiGress: Discrete Denoising diffusion for graph generation
cvignac/digress
•
•
This work introduces DiGress, a discrete denoising diffusion model for generating graphs with categorical node and edge attributes.
A Framework for Large Scale Synthetic Graph Dataset Generation
NVIDIA/DeepLearningExamples
•
•
Recently there has been increasing interest in developing and deploying deep graph learning algorithms for many tasks, such as fraud detection and recommender systems.
Edgeformers: Graph-Empowered Transformers for Representation Learning on Textual-Edge Networks
petergriffinjin/edgeformers
•
•
Edges in many real-world social/information networks are associated with rich text information (e. g., user-user communications or user-product reviews).
Retrieval Augmented Generation using Engineering Design Knowledge
Large-language Models (LLMs) need to adopt Retrieval-Augmented Generation (RAG) to generate factual responses that are better suited to knowledge-based applications in the design process.
