Search results
Results From The WOW.Com Content Network
In April of 2005, NetworkX was made available as open source software. [1] Several Python packages focusing on graph theory, including igraph, graph-tool, and numerous others, are available. As of April 2024, NetworkX had over 50 million downloads, [6] surpassing the download count of the second most popular package, igraph, by more than 50 ...
Step 1: Generate a network with nodes following a power law distribution with exponent and choose extremes of the distribution and to get desired average degree is . Step 2: ( 1 − μ ) {\displaystyle (1-\mu )} fraction of links of every node is with nodes of the same community, while fraction μ {\displaystyle \mu } is with the other nodes.
This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, [1] mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map.
In network science, the Configuration Model is a family of random graph models designed to generate networks from a given degree sequence. Unlike simpler models such as the Erdős–Rényi model , Configuration Models preserve the degree of each vertex as a pre-defined property.
In knowledge representation and reasoning, a knowledge graph is a knowledge base that uses a graph-structured data model or topology to represent and operate on data. Knowledge graphs are often used to store interlinked descriptions of entities – objects, events, situations or abstract concepts – while also encoding the free-form semantics ...
Exponential Random Graph Models (ERGMs) are a family of statistical models for analyzing data from social and other networks. [1] [2] Examples of networks examined using ERGM include knowledge networks, [3] organizational networks, [4] colleague networks, [5] social media networks, networks of scientific development, [6] and others.
In representation learning, knowledge graph embedding (KGE), also referred to as knowledge representation learning (KRL), or multi-relation learning, [1] is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning.
Microsoft Automatic Graph Layout, open-source .NET library (formerly called GLEE) for laying out graphs [30] NetworkX is a Python library for studying graphs and networks. Tulip, [31] an open-source data visualization tool; yEd, a graph editor with graph layout functionality [32] PGF/TikZ 3.0 with the graphdrawing package (requires LuaTeX). [33]