Source code for the EMNLP 2019 paper: "Connecting the Dots: Document-level Relation Extraction with Edge-oriented Graphs"

[CVPR 2021] Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis (official pytorch implementation)

go-fetch: Graph Oriented Fast Edge Traversal Cache Hub (Graph AI Agent Memory for Dgraph and Neo4j)

D3 force oriented graph can realize the transmission animation of disease in the network, add and delete edges and nodes, and print the changed data///D3力导向图，实现了疾病在网络中的传播动画，增加删除边与节点，并可以打印变更的数据

MARGOT: Motif-based pARtitioning of Graphs with OrienTed edges

Graiphic Toolkits for LabVIEW provide advanced AI, GPU, and graph-oriented computing capabilities directly inside LabVIEW. Built on ONNX Runtime, they enable seamless integration of SOTA, Accelerator, and Deep Learning Toolkit for high-performance execution across CPUs, GPUs, and edge devices.

I‑eats is a graph‑oriented Windows CLI simulation of a quick‑commerce logistics platform: users act as customers or drivers across islands (nodes) and capacity‑limited bridges (edges) to create and fulfill on‑demand pickup & delivery orders, illustrating core q‑commerce last‑mile routing dynamics.

University algorithm-oriented project that accurately detects complex networks of influential mafia families represented as vertices and edges of rare graphs. The algorithms are being reduced to the Satisfiability Problem using a boolean formula that will be evaluated by an SAT Solver, deciding whether the initial clause has been satisfied or not.

Image processing This work consists of implementing and testing three image processing methods, this process is possible through the use of libraries which facilitate the implementation while testing different methods namely thresholding, edge detection, erosion and expansion. These techniques have processed the images in different ways as the purpose of image processing is to study the images, to transform them from the given information. As programming language we used python which is an interpreted programming language, multi-paradigm and multiplatform. It promotes structured, functional and object-oriented imperative programming, its use is effective in image processing. As libraries we used: Open CV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. Open CV was designed to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products. Numpy: is an extension of the Python programming language, intended to handle multidimensional matrices or arrays as well as mathematical functions operating on these arrays. Matploblib: is a library of the Python programming language intended to plot and visualize data in the form of graphs.

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Edge-oriented Attention Mechanism for Graph Convolution Networks

Object oriented design of graph without duplicate edges and fast lookup

Host the software Motif-based pARtitioning of Graphs with OrienTed edges (MARGOT) to keep real authors' anonymity.

An experimental, async-first, type-safe Object Graph Mapper (OGM) for Rust — a graph‑oriented ORM-ish toolkit focused on Nodes, Edges & fluent traversal-friendly queries.

A C++ implementation of dynamic graph data structures, supporting both directed and undirected graphs. Features include efficient edge management, neighbor retrieval, reachability checking, and object-oriented design principles.

A CS project aiming at creating algorithms to minimize the number of edge crossings among all the straight-line upward grid drawings of a given oriented a-cyclic graph.

GRAFF SLIDE is a software design project for interactive graph creation and visualization. It allows users to add and modify vertices and edges, visualize directed and undirected graphs, and demonstrate basic graph algorithms. The focus is on object-oriented design, clean architecture, and modular structure.

A signal-flow graph or signal-flow-graph (SFG), invented by Claude Shannon,[1] but often called a Mason graph after Samuel Jefferson Mason who coined the term,[2] is a specialized flow graph, a directed graph in which nodes represent system variables, and branches (edges, arcs, or arrows) represent functional connections between pairs of nodes. Thus, signal-flow graph theory builds on that of directed graphs (also called digraphs), which includes as well that of oriented graphs. This mathematical theory of digraphs exists, of course, quite apart from its applications

A Java-based maze and pathfinding simulator designed to visualize traversal through a grid of obstacles and safe zones. The project uses object-oriented structures to represent graphs, nodes, and edges — allowing the program to model and solve complex mazes.

This project simulates how social media platforms analyse and visualise user relationships through graph theory. Each user is represented as a node, and each connection (friendship/follow) is represented as an edge. The application is built in Python using Object-Oriented Programming principles.

The system enables users to load, analyze, and manipulate 3D mesh objects, implements fundamental data structures and algorithms for manipulating three-dimensional objects represented through vertices, edges, and faces. It serves as a practical demonstration of graph theory, geometric algorithms, and object-oriented design principles.

• Implemented an object-oriented system simulating a simplified London Underground route planner. • Built a bidirectional graph representation of the Tube network and implemented Dijkstra’s shortest path algorithm to compute optimal routes. • Developed robust JSON file parsing and handled edge cases (invalid input, disconnected nodes).

I present the Rieffel method, a cutting-edge technology algorithm based on data mining, graph theory, variable analysis, statistical models, object-oriented programming, digital marketing, decision trees and neural networks. Finally, and to put an end to the abstract, the most curious people will still wonder where the word "Rieffel" comes from: R from Friedrich Riemann, I from Isaac Newton, E from Leonhard Euler, F from Richard Feynmann, F from Jean-Baptiste-Joseph Fourier and L from Wilhelm Leibniz.