How SingularityNET Will Leverage Aigents and OpenCog Part 1

Singularity OpenCog Aigents

SingularityNET has a series of researches that shows how the network uses a diagram to envision data and graph as a structure of data. This four-part series has been applied to partners like Aigents and OpenCog. This article will give an introductory view on different sort of graphs and will cover distinct competences of using OpenCog.

Brief Introduction on SingularityNET Graphs

Graph embodies a particular structure, storage pattern and solution such as triad stores, graph databases, and specific system or database configurations. It also serves as a way to visualize data with lots of visual languages and involves connected vertices or nodes through links and edges.

SingularityNET Graphs

Discovering Different Graph Types

Links have directions, therefore; it can be undirected or directed. Graphs with directed links are called directed while graphs with undirected links are undirected. Directed links indicate asymmetry. A person can vote for another but doesn’t inevitably mean the latter will vote the one who voted him. On the other hand, the undirected link is symmetric. This link connects two nodes. If a graph contains these two kinds of links, it has mixed direction.

Aigents Graphs
Undirected, directed and mixed direction graphs rendered with Aigents Graphs

Aside from the direction, the graphs can possess a structure which has several complexity levels. Two nodes are connected in a single path only. Moreover, there is also a cyclic graph. Here, nodes will lead to other nodes using multiple paths. Concentrators or hubs are nodes that concentrate on non-unique and unique paths from diverse segments and links.

Graph links can have single or multiple meanings and ideas. On first cases, a graph can be associative or can have one link expressing specific associate kind on between nodes including mutual similarities. While on complex cases, semantic graphs may have different links with several meanings at the exact time. With this, graph can then be referred as a labeled graph.

Hyper-graph is a more complicated graph that goes beyond plain links and nodes. More nodes can be aggregated with hyperlink. This kind of graph can be hypothetically presented with many atomic plain graphs containing numerous binary links or nodes.
Another kind of graph is meta-graph which is known for its more complicated form. A single graph can play the role of (subgraph) other graph’s nodes. Super-graph is the outer graph.

The manner by which links and nodes are rationally positioned in the screen is called layout. It can be natural and deterministic. For example, the deterministic layout on Aigents Graphs is calculated with parallel arrangement done based on normal alphabet order of node names. Also, graph is computed in vertical arrangements done based on node rankings.

Conclusion

Indeed, the above information connotes the very complex project of SingularityNET. Graphs will help makes data-understanding easier. It will show how each node systematically passes data in SingularityNET. Graphs provide the contents of all AI needs. It clearly defines the progress and development of every roadmap on SingularityNET.

The SingularityNET is a place for Machine and Artificial Intelligence. The network utilizes AGI tokens. These SingularityNET coins are used to perform processes or protocols within the system.

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