![]() Graph.add_edge() takes two id's + any optional parameter of Edge.īoth methods have an optional base parameter that defines the subclass of Node or Edge to use.Graph.add_node() takes an id + any optional parameter of Node.Graph.update(iterations=10, weight=10, limit=0.5) inge(depth=0, traversable=lambda node, edge: True) Graph.shortest_paths(node, heuristic=None, directed=False) graph.shortest_path(node1, node2, heuristic=None, directed=False) Graph.betweenness_centrality() # Updates all Node.centrality values. Graph.eigenvector_centrality() # Updates all Node.weight values. Graph.split() # Returns a list of (unconnected) graphs. Graph.edge(id1, id2) # Returns edge connecting the given nodes. Graph.node(id) # Returns node with given id. Graph.prune(depth=0) # Removes nodes + edges if len(node.links) <= depth. Graph.remove(node) # Removes given Node + edges. Graph.add_edge(id1, id2) # Creates + returns new Edge. Graph.add_node(id) # Creates + returns new Node. Graph.distance # GraphSpringLayout spacing. graph = Graph(layout=SPRING, distance=10.0) graph # Node with given id (Graph is a subclass of dict). Otherwise, Graph.add_edge() simply returns the edge that already exists between the given nodes.Ī Graph is a network of nodes connected by edges, with methods for finding paths between (indirectly) connected nodes. Two nodes can be connected by at most two edges (one in each direction). Evidently, it produces different shortest paths and node weights. The Graph.shortest_path() and Graph.betweenness_centrality() methods have a directed parameter which can be set to True, so that edges are only traversed from node1 → node2. edge = Edge(node1, node2, weight=0.0, length=1.0, type=None, **kwargs) edge.node1 # Node (sender).Įdge.length # Length modifier for the visualization.Īn edge can be traversed in both directions: from node1 → node2, and from node2 → node1. ![]() Edges with a higher weight are preferred when traversing the path between two (indirectly) connected nodes.Īn Edge takes optional parameters stroke (a tuple of RGBA values between 0.0- 1.0) and strokewidth, which can be used to style the graph visualization. Its weight defines the importance of the connection. Nodes with more edges have a higher degree.Īn Edge is a connection between two nodes. gree is the node's degree centrality (= local traffic) as a value between 0.0- 1.0. ![]() They are often found at the intersection of different clusters of nodes (e.g., like a broker or a bridge). Nodes that occur more frequently in paths between other nodes have a higher betweenness. Node.centrality is the node's betweenness centrality (= passing traffic) as a value between 0.0- 1.0. For example, in the WWW, popular websites are those that are often linked to, where the popularity of the referring websites is taken into account. Nodes with more (indirect) incoming edges have a higher weight. Node.weight is the node's eigenvector centrality (= incoming traffic) as a value between 0.0- 1.0. ![]() The aph module has three centrality measurements, adopted from NetworkX. Node.flatten() returns a list with the node itself ( depth=0), directly connected nodes ( depth=1), nodes connected to those nodes ( depth=2), and so on.Ī well-known task in graph analysis is measuring how important or central each node in the graph is.Node.edge(node) returns the Edge from this node to the given node, or None.node.flatten(depth=1, traversable=lambda node, edge: True) Node.force # 2D Vector, updated by Graph.layout. Node.centrality # Betweenness centrality (0.0-1.0). Node.weight # Eigenvector centrality (0.0-1.0). node = Node(id="", **kwargs) aph # Parent Graph. For example, the World Wide Web (popular websites) and the shortest path between them.Ī Node takes a number of optional parameters used to style the graph visualization of the graph: radius (node size), text, fill and stroke (colors each a tuple of RGBA values between 0.0- 1.0), strokewidth, font, fontsize and fontweight. A graph is a network of nodes and edges (connections between nodes). It can be used by itself or with other pattern modules: web | db | en | search | vector | graph.Ī Node is an element with a unique id (a string or int) in a graph. It can be used for example to study social networks or to model semantic relationships between concepts. A graph is a network of nodes connected by edges. The aph module has tools for graph analysis (shortest path, centrality) and graph visualization in the browser.
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