ra_index_soundarajan_hopcroft(G, ebunch=None, community='community')
For two nodes $u$ and $v$, this function computes the resource allocation index considering only common neighbors belonging to the same community as $u$ and $v$. Mathematically,
$$\sum_{w \in \Gamma(u) \cap \Gamma(v)} \frac{f(w)}{|\Gamma(w)|}$$where $f(w)$ equals 1 if $w$ belongs to the same community as $u$ and $v$ or 0 otherwise and $\Gamma(u)$ denotes the set of neighbors of $u$.
A NetworkX undirected graph.
The score will be computed for each pair of nodes given in the iterable. The pairs must be given as 2-tuples (u, v) where u and v are nodes in the graph. If ebunch is None then all non-existent edges in the graph will be used. Default value: None.
Nodes attribute name containing the community information. G[u][community] identifies which community u belongs to. Each node belongs to at most one community. Default value: 'community'.
An iterator of 3-tuples in the form (u, v, p) where (u, v) is a pair of nodes and p is their score.
Compute the resource allocation index of all node pairs in ebunch using community information.
>>> G = nx.Graph()See :
... G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3)])
... G.nodes[0]["community"] = 0
... G.nodes[1]["community"] = 0
... G.nodes[2]["community"] = 1
... G.nodes[3]["community"] = 0
... preds = nx.ra_index_soundarajan_hopcroft(G, [(0, 3)])
... for u, v, p in preds:
... print(f"({u}, {v}) -> {p:.8f}") (0, 3) -> 0.50000000
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them