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NetworkX is suitable for operation on large real-world graphs: e.g., graphs in excess of 10 million nodes and 100 million edges. [ clarification needed ] [ 19 ] Due to its dependence on a pure-Python "dictionary of dictionary" data structure, NetworkX is a reasonably efficient, very scalable , highly portable framework for network and social ...
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 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 ...
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.
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.
Bipartite network projection is an extensively used method for compressing information about bipartite networks. [1] Since the one-mode projection is always less informative than the original bipartite graph, an appropriate method for weighting network connections is often required.
The value of the modularity for unweighted and undirected graphs lies in the range [/,]. [3] It is positive if the number of edges within groups exceeds the number expected on the basis of chance. For a given division of the network's vertices into some modules, modularity reflects the concentration of edges within modules compared with random ...
A cyclical dependency graph. A rule is an expression of the form n :− a 1, ..., a n where: . a 1, ..., a n are the atoms of the body,; n is the atom of the head.; A rule allows to infer new knowledge starting from the variables that are in the body: when all the variables in the body of a rule are successfully assigned, the rule is activated and it results in the derivation of the head ...