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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 ...
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 ...
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.
This source is using Tarjan's implementation for the dense graph. NetworkX, a python library distributed under BSD, has an implementation of Edmonds' Algorithm. (spanning-forest-builder 0.0.2) – Library for constructing oriented forests of minimum weight.
Signed graphs allow for both favorable and adverse relationships and serve as a common model choice for various data analysis applications, e.g., correlation clustering. The stochastic block model can be trivially extended to signed graphs by assigning both positive and negative edge weights or equivalently using a difference of adjacency ...
Network motifs are recurrent and statistically significant subgraphs or patterns of a larger graph.All networks, including biological networks, social networks, technological networks (e.g., computer networks and electrical circuits) and more, can be represented as graphs, which include a wide variety of subgraphs.
Richard Blondel, co-author of the paper that originally published the Louvain method, seems to support this notion, [6] but other sources claim the time complexity is "essentially linear in the number of links in the graph," [7] meaning the time complexity would instead be (), where m is the number of edges in the graph. Unfortunately, no ...
The Collective Knowledge (CK) project is an open-source framework and repository to enable collaborative, reproducible and sustainable research and development of complex computational systems. [2] CK is a small, portable, customizable and decentralized infrastructure helping researchers and practitioners: