Scan the underlying storage to get the whole graph.Nebula Graph provides two methods to get graph structures:
This post uses Nebula Graph as the graph database for storing the graph data. At this time, we’d better persist the entire change process in a database, and load subgraphs or whole graphs directly from the database for real-time analysis.
But when the graph network changes a lot, for example, some central nodes are deleted or important network topology changes are introduced, it is a little troublesome to generate, load, and analyze the new static files. NetworkX usually uses local files as the data source, which is totally okay for static network researches. In the last post (part one of this series), we have displayed the community detection algorithm Girvan-Newman provided by NetworkX. MultiDiGraph: A directed version of a MultiGraph.MultiGraph: A flexible graph class that allows multiple undirected edges between pairs of nodes.The following four basic graph types are provided in NetworkX: Nodes and edges can also have many attributes so that more information can be stored. Edges are uniquely determined by two nodes, representing the relationship between the two nodes. Attributes are often associated with nodes and/or edges and are optional. In NetworkX, a graph (network) is a collection of nodes together with a collection of edges. With its rich, easy-to-use built-in graphs and analysis algorithms, it’s easy to perform complex network analysis and simulation modeling. NetworkX is a modeling tool for the graph theory and complex networks written by Python.
In this post, we will show you how to access data in Nebula Graph by using NetworkX. In the last post, we showed the character relationship for the Game of Thrones by using NetworkX and Gephi.
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