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- """Generators for Harary graphs
- This module gives two generators for the Harary graph, which was
- introduced by the famous mathematician Frank Harary in his 1962 work [H]_.
- The first generator gives the Harary graph that maximizes the node
- connectivity with given number of nodes and given number of edges.
- The second generator gives the Harary graph that minimizes
- the number of edges in the graph with given node connectivity and
- number of nodes.
- References
- ----------
- .. [H] Harary, F. "The Maximum Connectivity of a Graph."
- Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
- """
- import networkx as nx
- from networkx.exception import NetworkXError
- __all__ = ["hnm_harary_graph", "hkn_harary_graph"]
- @nx._dispatchable(graphs=None, returns_graph=True)
- def hnm_harary_graph(n, m, create_using=None):
- r"""Return the Harary graph with given numbers of nodes and edges.
- The Harary graph $H_{n, m}$ is the graph that maximizes node connectivity
- with $n$ nodes and $m$ edges.
- This maximum node connectivity is known to be $\lfloor 2m/n \rfloor$. [1]_
- Parameters
- ----------
- n: integer
- The number of nodes the generated graph is to contain.
- m: integer
- The number of edges the generated graph is to contain.
- create_using : NetworkX graph constructor, optional (default=nx.Graph)
- Graph type to create. If graph instance, then cleared before populated.
- Returns
- -------
- NetworkX graph
- The Harary graph $H_{n, m}$.
- See Also
- --------
- hkn_harary_graph
- Notes
- -----
- This algorithm runs in $O(m)$ time.
- The implementation follows [2]_.
- References
- ----------
- .. [1] F. T. Boesch, A. Satyanarayana, and C. L. Suffel,
- "A Survey of Some Network Reliability Analysis and Synthesis Results,"
- Networks, pp. 99-107, 2009.
- .. [2] Harary, F. "The Maximum Connectivity of a Graph."
- Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
- """
- if n < 1:
- raise NetworkXError("The number of nodes must be >= 1!")
- if m < n - 1:
- raise NetworkXError("The number of edges must be >= n - 1 !")
- if m > n * (n - 1) // 2:
- raise NetworkXError("The number of edges must be <= n(n-1)/2")
- # Get the floor of average node degree.
- d = 2 * m // n
- offset = d // 2
- H = nx.circulant_graph(n, range(1, offset + 1), create_using=create_using)
- half = n // 2
- if (n % 2 == 0) or (d % 2 == 0):
- # If d is odd; n must be even.
- if d % 2 == 1:
- # Add edges diagonally.
- H.add_edges_from((i, i + half) for i in range(half))
- r = 2 * m % n
- # Add remaining edges at offset + 1.
- H.add_edges_from((i, i + offset + 1) for i in range(r // 2))
- else:
- # Add the remaining m - n * offset edges between i and i + half.
- H.add_edges_from((i, (i + half) % n) for i in range(m - n * offset))
- return H
- @nx._dispatchable(graphs=None, returns_graph=True)
- def hkn_harary_graph(k, n, create_using=None):
- r"""Return the Harary graph with given node connectivity and node number.
- The Harary graph $H_{k, n}$ is the graph that minimizes the number of
- edges needed with given node connectivity $k$ and node number $n$.
- This smallest number of edges is known to be $\lceil kn/2 \rceil$ [1]_.
- Parameters
- ----------
- k: integer
- The node connectivity of the generated graph.
- n: integer
- The number of nodes the generated graph is to contain.
- create_using : NetworkX graph constructor, optional (default=nx.Graph)
- Graph type to create. If graph instance, then cleared before populated.
- Returns
- -------
- NetworkX graph
- The Harary graph $H_{k, n}$.
- See Also
- --------
- hnm_harary_graph
- Notes
- -----
- This algorithm runs in $O(kn)$ time.
- The implementation follows [2]_.
- References
- ----------
- .. [1] Weisstein, Eric W. "Harary Graph." From MathWorld--A Wolfram Web
- Resource. http://mathworld.wolfram.com/HararyGraph.html.
- .. [2] Harary, F. "The Maximum Connectivity of a Graph."
- Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
- """
- if k < 1:
- raise NetworkXError("The node connectivity must be >= 1!")
- if n < k + 1:
- raise NetworkXError("The number of nodes must be >= k+1 !")
- # In case of connectivity 1, simply return the path graph.
- if k == 1:
- return nx.path_graph(n, create_using)
- offset = k // 2
- H = nx.circulant_graph(n, range(1, offset + 1), create_using=create_using)
- half = n // 2
- if (k % 2 == 0) or (n % 2 == 0):
- # If k is odd; n must be even.
- if k % 2 == 1:
- # Add edges diagonally.
- H.add_edges_from((i, i + half) for i in range(half))
- else:
- # Add half + 1 edges between i and i + half.
- H.add_edges_from((i, (i + half) % n) for i in range(half + 1))
- return H
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