#node graph

[agatedragongames]

Random Mouse Algorithm

The 'Random Mouse Algorithm' is the best maze solving algorithm in the universe. It uses less memory than other wasteful pathfinding algorithms like 'Breadth First Search' and 'A-star'. Since it picks a random node at every junction.

So there is no need for complicated containers like queues and stacks. The only downside, it's very slow… But it's guaranteed to find the goal (or cheese) eventually! It also won't find you the shortest path, but there is nothing wrong with taking the scenic route 🐭🧀

[agkavm2000]

Relearning some of Nuke's features through Natron for an uncoming video.

So far, it has been pretty fun :3

[raspbian-official]

i do love it when i wade through a mass of online-only proprietary bullshit software to eventually find that there's a small foss library that does exactly what i want it to and a kde tool to view the output, we love happy endings

[putnamspuppeteer]

A map of every single band on the Metal Archives as of March 1st of this year, using the same dataset I used for this site. Each individual dot represents a single band, and each line indicates that two bands have a member in common.

A closeup, showing the lines in a bit more detail.

There are around 177k bands on the Metal Archives, and, of them, about two-thirds can be connected to one another by common members.

[a-continuous-cutting-motion]

If you work in information security you too can study the world’s most boring, obvious diagrams

[mira0000000-blog]

Touched blender again yey

[lindenattic]

This is what music looks like . Well I think this is more like one hemisphere of music maybe. This is what a LOT of music, but not all of it, looks like

[mycringefactory]

They finally say who reblogged on the reblog graph!!!

[ghelgheli]

if the nodes on the reblog graph feature were accounts rather than individual reblogs i think it would be possible to manufacture a non-planar reblog graph if you could get together five people and produce K5 (or, perhaps more interestingly, six people to coordinate the utility graph)

[glitterdoe]

ohhh you knowww

[agatedragongames]

Pathfinding tutorial: Exploring a node graph using depth first search whilst searching for a goal point.

The depth first search algorithm will explore the first node it finds and continue picking the first connected node until it either finds the goal or runs into a dead end. At which point it will backtrack and pick the next unexplored node. It will continue doing this until the goal is found or every node is explored.

Exploring all the nodes in a node graph using depth first search

[enbyss]

i will give you all only a SINGLE guess as to which part of this giant node graph is about SCPs. only one guess. very difficult. impossible even.

[wavesmp3]

reblog graph for oasis got me emo

[guillaumelauzier]

Graph Neural Networks: Revolutionizing Data Analysis in Graph-Structured Domains

Graph Neural Networks (GNNs) represent a paradigm shift in the realm of neural networks, uniquely tailored for graph-structured data. They are pivotal in addressing complex data scenarios where traditional neural networks fall short. This comprehensive article delves into the core functionalities, applications, and future potential of GNNs.

Understanding Graph Neural Networks

Direct Application to Graphs GNNs' foremost strength lies in their direct application to graphs, facilitating node-level, edge-level, and graph-level prediction tasks. This flexibility proves invaluable across various fields where data is intrinsically relational, such as analyzing social networks, understanding molecular structures, and optimizing communication networks . Processing Complex Graph-Structured Data GNNs excel at processing and analyzing intricate graph-structured data. This capacity unlocks new avenues in numerous domains, including network analysis, computational biology, and the development of advanced recommender systems . Dependence on Graph Structure Central to GNNs' functionality is their ability to capture the dependence of graphs through message passing between nodes. By leveraging the inherent structural information of graphs, GNNs can make more accurate predictions and analyses, a critical aspect in fields like network security and structural health monitoring .

Expansive Applications of GNNs

Versatility in Various Fields GNNs' adaptability to graph data makes them invaluable in areas where relationships and connections are crucial. This includes, but is not limited to, social network analysis, drug discovery and chemistry, traffic flow prediction, and biological network analysis . From Foundations to Frontiers Spanning from basic concepts to cutting-edge advancements, GNNs are continually evolving. Ongoing research and development are likely to amplify their capabilities, making them even more effective in handling diverse, graph-related challenges .

How can Graph Neural Networks be used in Generative Art?

Graph Neural Networks (GNNs) have significant potential in the realm of generative art, leveraging their unique capabilities in understanding and manipulating graph-structured data. Here are some ways GNNs can be applied in this field: - Modeling Complex Relationships: GNNs can model intricate relationships and patterns within data. In generative art, they can analyze the structure of artistic elements, like color, form, and composition, to generate new artworks that maintain stylistic coherence or offer novel artistic interpretations. - Link Prediction for Artistic Elements: GNNs are adept at inferring missing links or detecting spurious ones in graph data. This capability can be used in generative art to predict and create connections between different artistic elements, leading to the generation of visually cohesive and complex artworks . - Learning Node Embeddings: In the context of generative art, GNNs can learn embeddings (representations) of various artistic elements. These embeddings can capture the nuances of style, technique, and other artistic features, which can then be used to generate new art pieces that reflect certain styles or artistic trends . - Message Passing for Artistic Interpretation: GNNs use message passing to understand graph structures, which can be applied to the way different elements in an artwork relate to each other. This can help in creating art that dynamically changes or evolves based on certain rules or inputs, adding an interactive or adaptive element to the artwork .

Python code example of a Graph Neural Networks

Here's a basic example of implementing a Graph Neural Network (GNN) using PyTorch. This code demonstrates the creation of a simple GNN for node classification on a graph: import torch import torch.nn as nn import torch.nn.functional as F from torch_geometric.nn import GCNConv # Define a simple GNN model class GCN(nn.Module): def __init__(self, num_features, num_classes): super(GCN, self).__init__() self.conv1 = GCNConv(num_features, 16) self.conv2 = GCNConv(16, num_classes) def forward(self, data): x, edge_index = data.x, data.edge_index # First Graph Convolutional Layer x = self.conv1(x, edge_index) x = F.relu(x) x = F.dropout(x, training=self.training) # Second Graph Convolutional Layer x = self.conv2(x, edge_index) return F.log_softmax(x, dim=1) # Example usage num_features = 10 # Number of features per node num_classes = 3 # Number of classes for classification model = GCN(num_features, num_classes) This code defines a simple two-layer Graph Convolutional Network (GCN) using PyTorch and PyTorch Geometric. The model takes in the number of features per node and the number of classes for classification. Each convolutional layer (GCNConv) in the network processes the graph data, applying a graph convolution followed by a ReLU activation and dropout. Note: This is a basic example. For a real-world application, you would need to provide graph data (nodes, edges, node features) to the model and train it on a specific task like node classification, link prediction, etc.

🌐 Sources

- AssemblyAI - AI trends in 2023: Graph Neural Networks - ScienceDirect - Graph neural networks: A review of methods and applications - arXiv - Generative Graph Neural Networks for Link Prediction - YouTube - AI Explained: Graph Neural Networks and Generative AI - Medium - Top Applications of Graph Neural Networks 2021 - Towards Data Science - Applications of Graph Neural Networks - XenonStack - Graph Neural Network Applications and its Future - arXiv - Graph Neural Networks: Methods, Applications, and - neptune.ai - Graph Neural Network and Some of GNN Applications - sciencedirect.com - Graph neural networks: A review of methods and applications - frontiersin.org - Graph Neural Networks and Their Current Applications in - Jonathan Hui - Applications of Graph Neural Networks (GNN) - Medium - GNN python code in Keras and pytorch - Towards Data Science - How to Create a Graph Neural Network in Python - DataCamp - A Comprehensive Introduction to Graph Neural Networks - GitHub - Hands-On-Graph-Neural-Networks-Using-Python - Towards Data Science - Graph Neural Networks in Python - Analytics Vidhya - Getting Started with Graph Neural Networks Read the full article

[purified-zone]

tags to frighten a vinny in 2015

[sea-of-flxmes]

        ❝ ...A core, huh? ❞

        Nywe turned her head to look at the payphone located near the stage and squinted. Indeed, there was a blue flame popping out of the object, with a bright white spot at the center that seemed to beckon her.

❝ Oh! I see it now. I'm gonna try and reach it— ❞

She let out an undignified yelp as her (spiritual) body got dragged from the spotlight to the payphone cabin. Ghost travel was so fast! If only tangible human bodies were capable of something like this.

Once inside the cabin, Nywe's eyes widened at the display in front of her. A myriad of telephone lines extended in all directions like crimson tendrils, pulsing rhythmically with small bursts of energy. It was breathtaking and terrifying at the same time; one of the many mysteries of the Ghost World that she had come to witness.

❝ Sissel! ❞ Nywe exclaimed, her voice trembling with fear. ❝ I think the payphone is cursed or something! ❞

❝ I can see many red lines sprouting from it! ❞

"Well, I don't exactly blame them. It looked like an accident to me too."

A rarity in his career of changing people's fates. Far more often it was murder or murder-adjacent.

"Got it. Sounds like you know this area better than I do. The dead don't really need to go to hospitals often, so."

Though Sissel frowns as the fellow spirit struggles to jump from core to core. It always was one of the more difficult things to explain how to do.

"It's a bit tricky to explain..."

The pun was not intended.

"We can if there are no other cores to jump to but usually no. See that pay telephone down there? It has a core. Try reaching out to it. Kind of like reaching out your arm toward it."

Once on the ground, more cores would be within reach for them to jump to in order to try and navigate their way to the hospital this girl spoke of. Unfortunately, Sissel didn't have a phone line saved to it, for it would make everything much easier. Maybe they could use a number he had saved to go somewhere closer to the hospital? It depended on what direction the hospital was in.