Repository: xgi-org/xgi (language: python)

Issue:
Add tensor functionality to eigenvector centralities
https://github.com/pnnl/GENTTSV/tree/master/xgi and add license.


Discussion / hints:

Fix patch:
diff --git a/LICENSE.md b/LICENSE.md
index ac2d4fcc2..aed60deb3 100644
--- a/LICENSE.md
+++ b/LICENSE.md
@@ -31,3 +31,24 @@ DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
 THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+---------------------------------
+
+License
+
+Tensor methods for nonuniform hypergraphs
+
+* Tensor methods functionality for the CompleX Group Interactions library
+
+Copyright 2023, 2024 Battelle Memorial Institute
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
\ No newline at end of file
diff --git a/docs/source/api/algorithms/xgi.algorithms.centrality.rst b/docs/source/api/algorithms/xgi.algorithms.centrality.rst
index d6463cec3..abc827059 100644
--- a/docs/source/api/algorithms/xgi.algorithms.centrality.rst
+++ b/docs/source/api/algorithms/xgi.algorithms.centrality.rst
@@ -9,6 +9,8 @@ xgi.algorithms.centrality
    
    .. autofunction:: clique_eigenvector_centrality
    .. autofunction:: h_eigenvector_centrality
+   .. autofunction:: z_eigenvector_centrality
    .. autofunction:: node_edge_centrality
    .. autofunction:: line_vector_centrality
    .. autofunction:: katz_centrality
+   .. autofunction:: uniform_h_eigenvector_centrality
\ No newline at end of file
diff --git a/docs/source/api/stats/xgi.stats.nodestats.rst b/docs/source/api/stats/xgi.stats.nodestats.rst
index 7d9195039..24f9b9a05 100644
--- a/docs/source/api/stats/xgi.stats.nodestats.rst
+++ b/docs/source/api/stats/xgi.stats.nodestats.rst
@@ -9,12 +9,14 @@
 
    .. autofunction:: attrs
    .. autofunction:: average_neighbor_degree
-   .. autofunction:: clique_eigenvector_centrality
-   .. autofunction:: clustering_coefficient
    .. autofunction:: degree
+   .. autofunction:: clique_eigenvector_centrality
    .. autofunction:: h_eigenvector_centrality
-   .. autofunction:: local_clustering_coefficient
+   .. autofunction:: z_eigenvector_centrality
+   .. autofunction:: katz_centrality
    .. autofunction:: node_edge_centrality
+   .. autofunction:: clustering_coefficient
+   .. autofunction:: local_clustering_coefficient
    .. autofunction:: two_node_clustering_coefficient
    .. autofunction:: local_simplicial_fraction
    .. autofunction:: local_edit_simpliciality
diff --git a/docs/source/api/tutorials/case_studies.rst b/docs/source/api/tutorials/case_studies.rst
index 7a8840576..f57400572 100644
--- a/docs/source/api/tutorials/case_studies.rst
+++ b/docs/source/api/tutorials/case_studies.rst
@@ -8,4 +8,5 @@ Case studies
    :maxdepth: 1
 
    case_study_1
-   case_study_2
\ No newline at end of file
+   case_study_2
+   case_study_3
\ No newline at end of file
diff --git a/docs/source/api/tutorials/case_study_3.nblink b/docs/source/api/tutorials/case_study_3.nblink
new file mode 100644
index 000000000..92312b29c
--- /dev/null
+++ b/docs/source/api/tutorials/case_study_3.nblink
@@ -0,0 +1,3 @@
+{
+    "path": "../../../../tutorials/case_studies/comparing_centralities.ipynb"
+}
\ No newline at end of file
diff --git a/docs/source/assets/images/Filtering_2024_Fig1.png b/docs/source/assets/images/Filtering_2024_Fig1.png
deleted file mode 100644
index e5fc00714..000000000
Binary files a/docs/source/assets/images/Filtering_2024_Fig1.png and /dev/null differ
diff --git a/docs/source/assets/images/Simpliciality_2023_Fig3.png b/docs/source/assets/images/Simpliciality_2023_Fig3.png
deleted file mode 100644
index 9a0a620fd..000000000
Binary files a/docs/source/assets/images/Simpliciality_2023_Fig3.png and /dev/null differ
diff --git a/docs/source/assets/images/XGI_2023_Fig2.png b/docs/source/assets/images/XGI_2023_Fig2.png
deleted file mode 100644
index 3831c3c78..000000000
Binary files a/docs/source/assets/images/XGI_2023_Fig2.png and /dev/null differ
diff --git a/docs/source/user_guides.rst b/docs/source/user_guides.rst
index 3119a5050..94826d80e 100644
--- a/docs/source/user_guides.rst
+++ b/docs/source/user_guides.rst
@@ -59,39 +59,38 @@ User Guides
             To the in-depth tutorials
 
 .. grid::
-
+	
     .. grid-item-card:: 
     	:text-align: center
 
-    	Cookbook
+    	Case studies
     	^^^
 
-    	Recipes to solve specific tasks in a few lines
-
+    	To see how others have used XGI in their work
     	+++
 
-        .. button-ref:: api/tutorials/recipes
+        .. button-ref:: api/tutorials/case_studies
             :expand:
             :color: secondary
             :click-parent:
 
-            To the cookbook
-	
+            To the case studies
+    
     .. grid-item-card:: 
     	:text-align: center
 
-    	Case studies
+    	Cookbook
     	^^^
 
-    	To see how others have used XGI in their work
+    	Recipes to solve specific tasks in a few lines
+
     	+++
 
-        .. button-ref:: api/tutorials/case_studies
+        .. button-ref:: api/tutorials/recipes
             :expand:
             :color: secondary
             :click-parent:
 
-            To the case studies
-
+            To the cookbook
 
 For all specifications and options of a particular function, or to explore all existing functions, see the `API Reference <reference.html>`_.
\ No newline at end of file
diff --git a/docs/source/using-xgi.rst b/docs/source/using-xgi.rst
index dbd32d0d3..610b2f92a 100644
--- a/docs/source/using-xgi.rst
+++ b/docs/source/using-xgi.rst
@@ -12,6 +12,11 @@ Published work
 2024
 ----
 
+Sinan G. Aksoy, Ilya Amburg, and Stephen J. Young, "Scalable Tensor Methods for Nonuniform Hypergraphs", *SIAM Journal on Mathematics of Data Science*, Vol. 6, Iss. 2, 481-503 (2024).
+
+:bdg-link-primary-line:`Paper <https://doi.org/10.1137/23M1584472>`
+:bdg-link-primary-line:`Code <https://github.com/pnnl/GENTTSV>`
+
 Gonzalo Contreras-Aso, Regino Criado, and Miguel Romance, "Beyond directed hypergraphs: heterogeneous hypergraphs and spectral centralities", *Journal of Complex Networks*, Volume 12, Issue 4, cnae037 (2024).
 
 :bdg-link-primary-line:`Paper <https://doi.org/10.1093/comnet/cnae037>`
diff --git a/tutorials/case_studies/comparing_centralities.ipynb b/tutorials/case_studies/comparing_centralities.ipynb
new file mode 100644
index 000000000..acc22d758
--- /dev/null
+++ b/tutorials/case_studies/comparing_centralities.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Comparing centrality measures\n",
+    "\n",
+    "XGi has several different centrality measures. How do they stack up against one another? We were curious too! Below is a pairplot comparing every centrality to each other for a selected hypergraph."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xgi\n",
+    "import seaborn as sns\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xgi.core.hypergraph.Hypergraph at 0x168564ce0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = \"email-enron\"\n",
+    "\n",
+    "\n",
+    "H = xgi.load_xgi_data(dataset)\n",
+    "H.cleanup()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, we compute different measures of centrality on the hypergraph:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "c1 = H.nodes.clique_eigenvector_centrality.asnumpy()\n",
+    "c2 = H.nodes.h_eigenvector_centrality(max_iter=1000).asnumpy()\n",
+    "c3 = H.nodes.z_eigenvector_centrality(max_iter=1000).asnumpy()\n",
+    "c4 = H.nodes.katz_centrality.asnumpy()\n",
+    "c5 = H.nodes.node_edge_centrality(max_iter=1000).asnumpy()\n",
+    "\n",
+    "df = pd.DataFrame()\n",
+    "# df[\"node-edge\"] = c1\n",
+    "df[\"CEC\"] = c1\n",
+    "df[\"HEC\"] = c2\n",
+    "df[\"ZEC\"] = c3\n",
+    "df[\"Katz\"] = c4\n",
+    "df[\"Node-Edge\"] = c5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x16d885280>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1250x1250 with 30 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "xgi",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/xgi/algorithms/centrality.py b/xgi/algorithms/centrality.py
index 37c7a1dc4..57272d6d4 100644
--- a/xgi/algorithms/centrality.py
+++ b/xgi/algorithms/centrality.py
@@ -10,15 +10,18 @@
 from ..convert import to_line_graph
 from ..exception import XGIError
 from ..linalg import clique_motif_matrix, incidence_matrix
-from ..utils import convert_labels_to_integers
+from ..utils import convert_labels_to_integers, pairwise_incidence, ttsv1, ttsv2
+from .connected import is_connected
 from .properties import is_uniform
 
 __all__ = [
     "clique_eigenvector_centrality",
     "h_eigenvector_centrality",
+    "z_eigenvector_centrality",
     "node_edge_centrality",
     "line_vector_centrality",
     "katz_centrality",
+    "uniform_h_eigenvector_centrality",
 ]
 
 
@@ -41,6 +44,7 @@ def clique_eigenvector_centrality(H, tol=1e-6):
     See Also
     --------
     h_eigenvector_centrality
+    z_eigenvector_centrality
 
     References
     ----------
@@ -48,8 +52,6 @@ def clique_eigenvector_centrality(H, tol=1e-6):
     Austin R. Benson,
     https://doi.org/10.1137/18M1203031
     """
-    from ..algorithms import is_connected
-
     # if there aren't any nodes, return an empty dict
     if H.num_nodes == 0:
         return dict()
@@ -65,101 +67,6 @@ def clique_eigenvector_centrality(H, tol=1e-6):
     return {node_dict[n]: v[n].item() for n in node_dict}
 
 
-def h_eigenvector_centrality(H, max_iter=100, tol=1e-6):
-    """Compute the H-eigenvector centrality of a uniform hypergraph.
-
-    Parameters
-    ----------
-    H : Hypergraph
-        The hypergraph of interest.
-    max_iter : int, optional
-        The maximum number of iterations before the algorithm terminates.
-        By default, 100.
-    tol : float > 0, optional
-        The desired L2 error in the centrality vector. By default, 1e-6.
-
-    Returns
-    -------
-    dict
-        Centrality, where keys are node IDs and values are centralities. The
-        centralities are 1-normalized.
-
-    Raises
-    ------
-    XGIError
-        If the hypergraph is not uniform.
-
-    See Also
-    --------
-    clique_eigenvector_centrality
-
-    References
-    ----------
-    Three Hypergraph Eigenvector Centralities,
-    Austin R. Benson,
-    https://doi.org/10.1137/18M1203031
-    """
-    from ..algorithms import is_connected
-
-    # if there aren't any nodes, return an empty dict
-    if H.num_nodes == 0:
-        return dict()
-    # if the hypergraph is not connected,
-    # this metric doesn't make sense and should return nan.
-    if not is_connected(H):
-        return {n: np.nan for n in H.nodes}
-
-    m = is_uniform(H)
-    if not m:
-        raise XGIError("This method is not defined for non-uniform hypergraphs.")
-
-    new_H = convert_labels_to_integers(H, "old-label")
-
-    f = lambda v, m: np.power(v, 1.0 / m)  # noqa: E731
-    g = lambda v, x: np.prod(v[list(x)])  # noqa: E731
-
-    x = np.random.uniform(size=(new_H.num_nodes))
-    x = x / norm(x, 1)
-
-    for iter in range(max_iter):
-        new_x = apply(new_H, x, g)
-        new_x = f(new_x, m)
-        # multiply by the sign to try and enforce positivity
-        new_x = np.sign(new_x[0]) * new_x / norm(new_x, 1)
-        if norm(x - new_x) <= tol:
-            break
-        x = new_x.copy()
-    else:
-        warn("Iteration did not converge!")
-    return {new_H.nodes[n]["old-label"]: c for n, c in zip(new_H.nodes, new_x)}
-
-
-def apply(H, x, g=lambda v, e: np.sum(v[list(e)])):
-    """Apply a vector to the hypergraph given a function.
-
-    Parameters
-    ----------
-    H : Hypergraph
-        Hypergraph of interest.
-    x : 1D numpy array
-        1D vector
-    g : lambda function, optional
-        function to apply. By default, sum.
-
-    Returns
-    -------
-    1D numpy array
-        vector post application
-    """
-    new_x = np.zeros(H.num_nodes)
-    for edge in H.edges.members():
-        edge = list(edge)
-        # ordered permutations
-        for shift in range(len(edge)):
-            new_x[edge[shift]] += g(x, edge[shift + 1 :] + edge[:shift])
-    return new_x
-
-
 def node_edge_centrality(
     H,
     f=lambda x: np.power(x, 2),
@@ -169,7 +76,7 @@ def node_edge_centrality(
     max_iter=100,
     tol=1e-6,
 ):
-    """Computes the node and edge centralities
+    r"""Computes the node and edge centralities
 
     Parameters
     ----------
@@ -214,15 +121,10 @@ def node_edge_centrality(
     Francesco Tudisco & Desmond J. Higham,
     https://doi.org/10.1038/s42005-021-00704-2
     """
-    from ..algorithms import is_connected
-
-    # if there aren't any nodes or edges, return an empty dict
+    # if the hypergraph is not connected or is empty,
+    # this metric doesn't make sense and should return nan.
     if H.num_nodes == 0 or H.num_edges == 0 or not is_connected(H):
         return {n: np.nan for n in H.nodes}, {e: np.nan for e in H.edges}
-    # if the hypergraph is not connected,
-    # this metric doesn't make sense and should return nan.
-    # if not is_connected(H):
-    #     return {n: np.nan for n in H.nodes}, {e: np.nan for e in H.edges}
 
     n = H.num_nodes
     m = H.num_edges
@@ -233,9 +135,9 @@ def node_edge_centrality(
 
     check = np.inf
 
-    for iter in range(max_iter):
-        u = np.multiply(x, g(I @ f(y)))
-        v = np.multiply(y, psi(I.T @ phi(x)))
+    for it in range(max_iter):
+        u = (x * g(I @ f(y))) ** 0.5
+        v = (y * psi(I.T @ phi(x))) ** 0.5
         # multiply by the sign to try and enforce positivity
         new_x = np.sign(u[0]) * u / norm(u, 1)
         new_y = np.sign(v[0]) * v / norm(v, 1)
@@ -273,8 +175,6 @@ def line_vector_centrality(H):
     https://doi.org/10.1016/j.chaos.2022.112397
 
     """
-    from ..algorithms import is_connected
-
     # If the hypergraph is empty, then return an empty dictionary
     if H.num_nodes == 0:
         return dict()
@@ -392,3 +292,272 @@ def katz_centrality(H, cutoff=100):
         c *= 1 / norm(c, 1)
     nodedict = dict(zip(range(n), H.nodes))
     return {nodedict[idx]: c[idx] for idx in nodedict}
+
+
+def h_eigenvector_centrality(H, max_iter=100, tol=1e-6):
+    """Compute the H-eigenvector centrality of a hypergraph.
+
+    The H-eigenvector terminology comes from Qi (2005) which
+    defines a "tensor H-eigenpair".
+
+    Parameters
+    ----------
+    H : Hypergraph
+        The hypergraph of interest.
+    max_iter : int, optional
+        The maximum number of iterations before the algorithm terminates.
+        By default, 100.
+    tol : float > 0, optional
+        The desired convergence tolerance. By default, 1e-6.
+
+    Returns
+    -------
+    dict
+        Centrality, where keys are node IDs and values are centralities. The
+        centralities are 1-normalized.
+
+    See Also
+    --------
+    clique_eigenvector_centrality
+    z_eigenvector_centrality
+    uniform_h_eigenvector_centrality
+
+    References
+    ----------
+    Scalable Tensor Methods for Nonuniform Hypergraphs,
+    Sinan Aksoy, Ilya Amburg, Stephen Young,
+    https://doi.org/10.1137/23M1584472
+
+    Three Hypergraph Eigenvector Centralities,
+    Austin R. Benson,
+    https://doi.org/10.1137/18M1203031
+
+    Computing tensor Z-eigenvectors with dynamical systems
+    Austin R. Benson and David F. Gleich
+    https://doi.org/10.1137/18M1229584
+
+    Liqun Qi
+    "Eigenvalues of a real supersymmetric tensor"
+    Journal of Symbolic Computation, **40**, *6* (2005).
+    https://doi.org/10.1016/j.jsc.2005.05.007.
+    """
+    # if there aren't any nodes, return an empty dict
+    if H.num_nodes == 0:
+        return dict()
+    # if the hypergraph is not connected,
+    # this metric doesn't make sense and should return nan.
+    if not is_connected(H):
+        return {n: np.nan for n in H.nodes}
+
+    new_H = convert_labels_to_integers(H, "old-label")
+    edge_dict = new_H.edges.members(dtype=dict)
+    node_dict = new_H.nodes.memberships()
+    r = new_H.edges.size.max()
+
+    x = np.random.uniform(size=(new_H.num_nodes))
+    x = x / norm(x, 1)
+    y = np.abs(np.array(ttsv1(node_dict, edge_dict, r, x)))
+
+    converged = False
+    it = 0
+    while it < max_iter and not converged:
+        y_scaled = [_y ** (1 / (r - 1)) for _y in y]
+        x = y_scaled / norm(y_scaled, 1)
+        y = np.abs(np.array(ttsv1(node_dict, edge_dict, r, x)))
+        s = [a / (b ** (r - 1)) for a, b in zip(y, x)]
+        if (np.max(s) - np.min(s)) / np.min(s) < tol:
+            break
+        it += 1
+    else:
+        warn("Iteration did not converge!")
+    return {
+        new_H.nodes[n]["old-label"]: c.item()
+        for n, c in zip(new_H.nodes, x / norm(x, 1))
+    }
+
+
+def z_eigenvector_centrality(H, max_iter=100, tol=1e-6):
+    """Compute the Z-eigenvector centrality of a hypergraph.
+
+    The Z-eigenvector terminology comes from Qi (2005) which
+    defines a "tensor Z-eigenpair".
+
+    Parameters
+    ----------
+    H : Hypergraph
+        The hypergraph of interest.
+    max_iter : int, optional
+        The maximum number of iterations before the algorithm terminates.
+        By default, 100.
+    tol : float > 0, optional
+        The desired convergence tolerance. By default, 1e-6.
+
+    Returns
+    -------
+    dict
+        Centrality, where keys are node IDs and values are centralities. The
+        centralities are 1-normalized.
+
+    Raises
+    ------
+    XGIError
+        If the hypergraph is not uniform.
+
+    See Also
+    --------
+    clique_eigenvector_centrality
+    h_eigenvector_centrality
+
+    References
+    ----------
+    Scalable Tensor Methods for Nonuniform Hypergraphs,
+    Sinan Aksoy, Ilya Amburg, Stephen Young,
+    https://doi.org/10.1137/23M1584472
+
+    Three Hypergraph Eigenvector Centralities,
+    Austin R. Benson,
+    https://doi.org/10.1137/18M1203031
+
+    Liqun Qi
+    "Eigenvalues of a real supersymmetric tensor"
+    Journal of Symbolic Computation, **40**, *6* (2005).
+    https://doi.org/10.1016/j.jsc.2005.05.007.
+    """
+    # if there aren't any nodes, return an empty dict
+    n = H.num_nodes
+    if n == 0:
+        return dict()
+
+    # if the hypergraph is not connected,
+    # this metric doesn't make sense and should return nan.
+    if not is_connected(H):
+        return {n: np.nan for n in H.nodes}
+    new_H = convert_labels_to_integers(H, "old-label")
+    max_size = new_H.edges.size.max()
+    edge_dict = new_H.edges.members(dtype=dict)
+    pairs_dict = pairwise_incidence(edge_dict, max_size)
+
+    r = H.edges.size.max()
+
+    def LR_evec(A):
+        """Compute the largest real eigenvalue of the matrix A"""
+        _, v = eigsh(A, k=1, which="LM", tol=1e-5, maxiter=200)
+        evec = np.array([_v for _v in v[:, 0]])
+        if evec[0] < 0:
+            evec = -evec
+        return evec / norm(evec, 1)
+
+    def f(u):
+        return LR_evec(ttsv2(pairs_dict, edge_dict, r, u, n)) - u
+
+    x = np.ones(n) / n
+
+    h = 0.5
+    converged = False
+    it = 0
+    while it < max_iter and not converged:
+        x_new = x + h * f(x)
+        s = np.array([a / b for a, b in zip(x_new, x)])
+        if (np.max(s) - np.min(s)) / np.min(s) < tol:
+            break
+        x = x_new
+        it += 1
+    else:
+        warn("Iteration did not converge!")
+    return {
+        new_H.nodes[n]["old-label"]: c.item()
+        for n, c in zip(new_H.nodes, x / norm(x, 1))
+    }
+
+
+def uniform_h_eigenvector_centrality(H, max_iter=100, tol=1e-6):
+    """Compute the H-eigenvector centrality of a uniform hypergraph.
+
+    Parameters
+    ----------
+    H : Hypergraph
+        The hypergraph of interest.
+    max_iter : int, optional
+        The maximum number of iterations before the algorithm terminates.
+        By default, 100.
+    tol : float > 0, optional
+        The desired L2 error in the centrality vector. By default, 1e-6.
+
+    Returns
+    -------
+    dict
+        Centrality, where keys are node IDs and values are centralities. The
+        centralities are 1-normalized.
+
+    Raises
+    ------
+    XGIError
+        If the hypergraph is not uniform.
+
+    See Also
+    --------
+    clique_eigenvector_centrality
+
+    References
+    ----------
+    Three Hypergraph Eigenvector Centralities,
+    Austin R. Benson,
+    https://doi.org/10.1137/18M1203031
+    """
+    # if there aren't any nodes, return an empty dict
+    if H.num_nodes == 0:
+        return dict()
+    # if the hypergraph is not connected,
+    # this metric doesn't make sense and should return nan.
+    if not is_connected(H):
+        return {n: np.nan for n in H.nodes}
+
+    m = is_uniform(H)
+    if not m:
+        raise XGIError("This method is not defined for non-uniform hypergraphs.")
+
+    new_H = convert_labels_to_integers(H, "old-label")
+
+    f = lambda v, m: np.power(v, 1.0 / m)  # noqa: E731
+    g = lambda v, x: np.prod(v[list(x)])  # noqa: E731
+
+    x = np.random.uniform(size=(new_H.num_nodes))
+    x = x / norm(x, 1)
+
+    for iter in range(max_iter):
+        x_new = apply(new_H, x, g)
+        x_new = f(x_new, m)
+        # multiply by the sign to try and enforce positivity
+        x_new = np.sign(x_new[0]) * x_new / norm(x_new, 1)
+        if norm(x - x_new) <= tol:
+            break
+        x = x_new.copy()
+    else:
+        warn("Iteration did not converge!")
+    return {new_H.nodes[n]["old-label"]: c for n, c in zip(new_H.nodes, x_new)}
+
+
+def apply(H, x, g=lambda v, e: np.sum(v[list(e)])):
+    """Apply a vector to the hypergraph given a function.
+
+    Parameters
+    ----------
+    H : Hypergraph
+        Hypergraph of interest.
+    x : 1D numpy array
+        1D vector
+    g : lambda function, optional
+        function to apply. By default, sum.
+
+    Returns
+    -------
+    1D numpy array
+        vector post application
+    """
+    new_x = np.zeros(H.num_nodes)
+    for edge in H.edges.members():
+        edge = list(edge)
+        # ordered permutations
+        for shift in range(len(edge)):
+            new_x[edge[shift]] += g(x, edge[shift + 1 :] + edge[:shift])
+    return new_x
diff --git a/xgi/stats/nodestats.py b/xgi/stats/nodestats.py
index b09a871e9..7f75b19bf 100644
--- a/xgi/stats/nodestats.py
+++ b/xgi/stats/nodestats.py
@@ -31,6 +31,7 @@
     "two_node_clustering_coefficient",
     "clique_eigenvector_centrality",
     "h_eigenvector_centrality",
+    "z_eigenvector_centrality",
     "node_edge_centrality",
     "katz_centrality",
 ]
@@ -347,6 +348,9 @@ def clique_eigenvector_centrality(net, bunch, tol=1e-6):
 def h_eigenvector_centrality(net, bunch, max_iter=10, tol=1e-6):
     """Compute the H-eigenvector centrality of a hypergraph.
 
+    The H-eigenvector terminology comes from Qi (2005) which
+    defines a "tensor H-eigenpair".
+
     Parameters
     ----------
     net : xgi.Hypergraph
@@ -368,11 +372,57 @@ def h_eigenvector_centrality(net, bunch, max_iter=10, tol=1e-6):
     Three Hypergraph Eigenvector Centralities,
     Austin R. Benson,
     https://doi.org/10.1137/18M1203031
+
+    Scalable Tensor Methods for Nonuniform Hypergraphs,
+    Sinan Aksoy, Ilya Amburg, Stephen Young,
+    https://doi.org/10.1137/23M1584472
+
+    Liqun Qi
+    "Eigenvalues of a real supersymmetric tensor"
+    Journal of Symbolic Computation, **40**, *6* (2005).
+    https://doi.org/10.1016/j.jsc.2005.05.007.
     """
     c = xgi.h_eigenvector_centrality(net, max_iter, tol)
     return {n: c[n] for n in c if n in bunch}
 
 
+def z_eigenvector_centrality(net, bunch, max_iter=10, tol=1e-6):
+    r"""Compute the Z-eigenvector centrality of a hypergraph.
+
+    The Z-eigenvector terminology comes from Qi (2005) which
+    defines a "tensor Z-eigenpair".
+
+    Parameters
+    ----------
+    net : xgi.Hypergraph
+        The hypergraph of interest.
+    bunch : Iterable
+        Nodes in `net`.
+    max_iter : int, default: 10
+        The maximum number of iterations before the algorithm terminates.
+    tol : float > 0, default: 1e-6
+        The desired L2 error in the centrality vector.
+
+    Returns
+    -------
+    dict
+        Centrality, where keys are node IDs and values are centralities.
+
+    References
+    ----------
+    Three Hypergraph Eigenvector Centralities,
+    Austin R. Benson,
+    https://doi.org/10.1137/18M1203031
+
+    Liqun Qi
+    "Eigenvalues of a real supersymmetric tensor"
+    Journal of Symbolic Computation, **40**, *6* (2005).
+    https://doi.org/10.1016/j.jsc.2005.05.007.
+    """
+    c = xgi.z_eigenvector_centrality(net, max_iter, tol)
+    return {n: c[n] for n in c if n in bunch}
+
+
 def node_edge_centrality(
     net,
     bunch,
@@ -383,7 +433,7 @@ def node_edge_centrality(
     max_iter=100,
     tol=1e-6,
 ):
-    """Computes node centralities.
+    """Computes nonlinear node-edge centralities.
 
     Parameters
     ----------
@@ -436,7 +486,7 @@ def node_edge_centrality(
 
 
 def katz_centrality(net, bunch, cutoff=100):
-    """Compute the H-eigenvector centrality of a hypergraph.
+    r"""Compute the Katz centrality of a hypergraph.
 
     Parameters
     ----------
diff --git a/xgi/utils/__init__.py b/xgi/utils/__init__.py
index 570aa121e..11ace38e6 100644
--- a/xgi/utils/__init__.py
+++ b/xgi/utils/__init__.py
@@ -1,3 +1,4 @@
-from . import trie, utilities
+from . import tensor, trie, utilities
+from .tensor import *
 from .trie import *
 from .utilities import *
diff --git a/xgi/utils/tensor.py b/xgi/utils/tensor.py
new file mode 100644
index 000000000..2742301c6
--- /dev/null
+++ b/xgi/utils/tensor.py
@@ -0,0 +1,333 @@
+## Tensor times same vector in all but one (TTSV1) and all but two (TTSV2)
+from collections import defaultdict
+from itertools import combinations
+from math import factorial
+
+import numpy as np
+from numpy import prod
+from scipy.signal import convolve
+from scipy.sparse import coo_array
+from scipy.special import binom as binomial
+
+__all__ = [
+    "pairwise_incidence",
+    "ttsv1",
+    "ttsv2",
+]
+
+
+def pairwise_incidence(edge_dict, max_size):
+    """Create pairwise incidence dictionary from hyperedge list dictionary
+
+    Parameters
+    ----------
+    edge_dict : dict
+        edge IDs are keys, edges are values
+    max_size : int
+        the size of the largest edge in the hypergraph
+
+    Returns
+    -------
+    pairs : dict
+        a dictionary with node pairs as keys and the hyperedges they appear in as values
+    """
+    pairs = defaultdict(set)
+    for e, edge in edge_dict.items():
+        for i, j in combinations(sorted(edge), 2):
+            pairs[(i, j)].add(e)
+        for n in edge:
+            pairs[(n, n)].add(e)
+
+        if len(edge) < max_size:
+            for n in edge:
+                pairs[(n, n)].add(e)
+    return pairs
+
+
+def banerjee_coeff(size, max_size):
+    r"""Return the Banerjee alpha coefficient
+
+    This coefficient measures the size of the set of edge blowups
+    defined in the corresponding references below. For example,
+    for the edge :math:`e=\{1, 3\}` in a rank 3 hypergraph, we have
+    the following blowup.
+
+    .. math::
+        \beta(e) = \{1, 1, 3\}, \{1, 3, 1\}, \{1, 3, 3\}, \{3, 1, 1\}, \{3, 1, 3\}, \{3, 3, 1\}
+
+    Parameters
+    ----------
+    size : int
+        size of given hyperedge
+    max_size : int
+        maximum hyperedge size
+
+    Returns
+    -------
+    float
+        the Banerjee coefficient
+
+    References
+    ----------
+    Anirban Banerjee, Arnab Char, and Bibhash Mondal,
+    "Spectra of general hypergraphs"
+    Linear Algebra and its Applications, **518**, 14-30 (2017),
+    https://doi.org/10.1016/j.laa.2016.12.022
+
+    Scalable Tensor Methods for Nonuniform Hypergraphs,
+    Sinan Aksoy, Ilya Amburg, Stephen Young,
+    https://doi.org/10.1137/23M1584472
+    """
+    return sum(
+        ((-1) ** j) * binomial(size, j) * (size - j) ** max_size
+        for j in range(size + 1)
+    )
+
+
+def ttsv1(node_dict, edge_dict, r, a):
+    """Computes the tensor times same vector in all modes but 1.
+
+    This method uses generating functions as described in the corresponding reference.
+
+    Parameters
+    ----------
+    node_dict : dict
+        A dictionary with nodes as keys and hyperedges they appear in
+        as values.
+    edge_dict : dict
+        A dictionary with edges as keys and nodes which are members as
+        values.
+    r : int
+        maximum hyperedge size
+    a : NumPy array
+        the vector to multiply the tensor by.
+
+    Returns
+    -------
+    NumPy array
+        The tensor multiplied by the vector in all modes but 1.
+
+    See Also
+    --------
+    ttsv2
+
+    References
+    ----------
+    Scalable Tensor Methods for Nonuniform Hypergraphs,
+    Sinan Aksoy, Ilya Amburg, Stephen Young,
+    https://doi.org/10.1137/23M1584472
+    """
+    n = len(node_dict)
+    s = np.zeros(n)
+    r_minus_1_factorial = factorial(r - 1)
+    for node, edges in node_dict.items():
+        c = 0
+        for e in edges:
+            l = len(edge_dict[e])
+            alpha = banerjee_coeff(l, r)
+            edge_without_node = [v for v in edge_dict[e] if v != node]
+            if l == r:
+                gen_fun_coef = prod(a[edge_without_node])
+            elif 2 ** (l - 1) < r * (l - 1):
+                gen_fun_coef = _get_gen_coef_subset_expansion(
+                    a[edge_without_node], a[node], r - 1
+                )
+            else:
+                gen_fun_coef = _get_gen_coef_fft_fast_array(
+                    edge_without_node, a, node, l, r
+                )
+            c += r_minus_1_factorial * l * gen_fun_coef / alpha
+        s[node] = c
+    return s
+
+
+def ttsv2(pair_dict, edge_dict, r, a, n):
+    """Computes the tensor times same vector in all modes but 2.
+
+    Parameters
+    ----------
+    pair_dict : dict
+        A dictionary with node pairs as keys and hyperedges they appear in
+        as values.
+    edge_dict : dict
+        A dictionary with edges as keys and nodes which are members as
+        values.
+    r : int
+        maximum hyperedge size
+    a : NumPy array
+        the vector to multiply the tensor by.
+    n : int
+        Number of nodes
+
+    Returns
+    -------
+    Scipy sparse array
+        A 2D array, which is the result of the tensor
+        multiplied by the vector in all modes but 2.
+
+    See Also
+    --------
+    ttsv1
+
+    References
+    ----------
+    Scalable Tensor Methods for Nonuniform Hypergraphs,
+    Sinan Aksoy, Ilya Amburg, Stephen Young,
+    https://doi.org/10.1137/23M1584472
+    """
+    s = {}
+    r_minus_2_factorial = factorial(r - 2)
+    for (v1, v2), edges in pair_dict.items():
+        c = 0
+        for e in edges:
+            l = len(edge_dict[e])
+            alpha = banerjee_coeff(l, r)
+            edge_without_node = [v for v in edge_dict[e] if v != v1 and v != v2]
+            if v1 != v2:
+                if 2 ** (l - 2) < (r - 2) * (l - 2):
+                    gen_fun_coef = _get_gen_coef_subset_expansion(
+                        a[edge_without_node], a[v1] + a[v2], r - 2
+                    )
+                else:
+                    coefs = [1]
+                    for i in range(1, r - 1):
+                        coefs.append(coefs[-1] * (a[v1] + a[v2]) / i)
+                    coefs = np.array(coefs)
+                    for u in edge_dict[e]:
+                        if u != v1 and u != v2:
+                            _coefs = [1]
+                            for i in range(1, r - l + 2):
+                                _coefs.append(_coefs[-1] * a[u] / i)
+                            _coefs = np.array(_coefs)
+                            _coefs[0] = 0
+                            coefs = convolve(coefs, _coefs)[0 : r - 1]
+                    gen_fun_coef = coefs[-1]
+            else:
+                if 2 ** (l - 1) < (r - 2) * (l - 1):
+                    gen_fun_coef = _get_gen_coef_subset_expansion(
+                        a[edge_without_node], a[v1], r - 2
+                    )
+                else:
+                    coefs = [1]
+                    for i in range(1, r - 1):
+                        coefs.append(coefs[-1] * (a[v1]) / i)
+                    coefs = np.array(coefs)
+                    for u in edge_dict[e]:
+                        if u != v1 and u != v2:
+                            _coefs = [1]
+                            for i in range(1, r - l + 1):
+                                _coefs.append(_coefs[-1] * a[v1] / i)
+                            _coefs = np.array(_coefs)
+                            _coefs[0] = 0
+                            coefs = convolve(coefs, _coefs)[0 : r - 1]
+                    gen_fun_coef = coefs[-1]
+            c += r_minus_2_factorial * l * gen_fun_coef / alpha
+        s[(v1, v2)] = c
+        if v1 == v2:
+            s[(v1, v2)] /= 2
+    first = np.zeros(len(s))
+    second = np.zeros(len(s))
+    value = np.zeros(len(s))
+    for i, k in enumerate(s.keys()):
+        first[i] = k[0]
+        second[i] = k[1]
+        value[i] = s[k]
+    Y = coo_array((value, (first, second)), (n, n))
+    return Y + Y.T
+
+
+## Helper functions for the tensor methods.
+
+
+def _get_gen_coef_subset_expansion(edge_values, node_value, r):
+    """Computes the generating funciton coefficient of order r using subset expansion.
+
+    Parameters
+    ----------
+    edge_values : NumPy array
+        Array of values from the `a` vector corresponding to
+        nodes in the hyperedge.
+    node_value : float
+        The value in a corresponding to the node being processed.
+    r : int
+        Desired order to get coefficient for.
+
+    Returns
+    -------
+    float
+        Generating function coefficient of order r.
+
+    See Also
+    --------
+    _get_gen_coef_fft_fast_array
+
+    References
+    ----------
+    Scalable Tensor Methods for Nonuniform Hypergraphs,
+    Sinan Aksoy, Ilya Amburg, Stephen Young,
+    https://doi.org/10.1137/23M1584472
+    """
+    k = len(edge_values)
+    subset_vector = [0]
+    subset_lengths = [0]
+    for i in range(k):
+        for t in range(len(subset_vector)):
+            subset_vector.append(subset_vector[t] + edge_values[i])
+            subset_lengths.append(subset_lengths[t] + 1)
+    for i in range(len(subset_lengths)):
+        subset_lengths[i] = (-1) ** (k - subset_lengths[i])
+    total = sum(
+        [
+            (node_value + subset_vector[i]) ** r * subset_lengths[i]
+            for i in range(len(subset_lengths))
+        ]
+    )
+    return total / factorial(r)
+
+
+def _get_gen_coef_fft_fast_array(edge_without_node, a, node, l, r):
+    """Computes the generating funciton coefficient of order r using the Fast Fourier Transform.
+
+    Parameters
+    ----------
+    edge_without_node : list
+        Array of node indices corresponding to
+        all nodes in the hyperedge but the one being processed.
+    a : NumPy array
+        The vector to multiply the tensor by.
+    node : int
+        The index of the node being processed.
+    l : int
+        Number of nodes in the hyperedge.
+    r : int
+        Desired order to get coefficient for.
+
+    Returns
+    -------
+    float
+        Generating function coefficient of order r.
+
+    See Also
+    --------
+    _get_gen_coef_subset_expansion
+
+    References
+    ----------
+    Scalable Tensor Methods for Nonuniform Hypergraphs,
+    Sinan Aksoy, Ilya Amburg, Stephen Young,
+    https://doi.org/10.1137/23M1584472
+    """
+    coefs = [1]
+    for i in range(1, r):
+        coefs.append(coefs[-1] * a[node] / i)
+    coefs = np.array(coefs)
+    for u in edge_without_node:
+        _coefs = [1]
+        for i in range(1, r - l + 2):
+            _coefs.append(_coefs[-1] * a[u] / i)
+        _coefs = np.array(_coefs)
+        _coefs[0] = 0
+        coefs = convolve(coefs, _coefs)[0:r]
+    gen_fun_coef = coefs[-1]
+    print("hi")
+    return gen_fun_coef


Test patch:
diff --git a/tests/algorithms/test_centrality.py b/tests/algorithms/test_centrality.py
index 80fecabe3..c800cef43 100644
--- a/tests/algorithms/test_centrality.py
+++ b/tests/algorithms/test_centrality.py
@@ -28,47 +28,54 @@ def test_clique_eigenvector_centrality():
     H = xgi.sunflower(3, 1, 3)
     c = H.nodes.clique_eigenvector_centrality.asnumpy()
     assert norm(c[1:] - c[1]) < 1e-4
-    assert abs(c[0] / c[1] - ratio(3, 3, kind="CEC")) < 1e-4
+    assert abs(c[0] / c[1] - _ratio(3, 3, kind="CEC")) < 1e-4
 
     H = xgi.sunflower(5, 1, 7)
     c = H.nodes.clique_eigenvector_centrality.asnumpy()
     assert norm(c[1:] - c[1]) < 1e-4
-    assert abs(c[0] / c[1] - ratio(5, 7, kind="CEC")) < 1e-4
+    assert abs(c[0] / c[1] - _ratio(5, 7, kind="CEC")) < 1e-4
 
 
 @pytest.mark.slow
-def test_h_eigenvector_centrality():
+def test_uniform_h_eigenvector_centrality():
     # test empty hypergraph
     H = xgi.Hypergraph()
-    c = xgi.h_eigenvector_centrality(H)
+    c = xgi.uniform_h_eigenvector_centrality(H)
     assert c == dict()
 
     # Test no edges
     H.add_nodes_from([0, 1, 2])
-    hec = xgi.h_eigenvector_centrality(H)
+    hec = xgi.uniform_h_eigenvector_centrality(H)
     for i in hec:
         assert np.isnan(hec[i])
 
     # test disconnected
     H.add_edge([0, 1])
-    hec = xgi.h_eigenvector_centrality(H)
+    hec = xgi.uniform_h_eigenvector_centrality(H)
     assert set(hec) == {0, 1, 2}
     for i in hec:
         assert np.isnan(hec[i])
 
     H = xgi.sunflower(3, 1, 5)
-    c = H.nodes.h_eigenvector_centrality(max_iter=1000).asnumpy()
+    c = xgi.uniform_h_eigenvector_centrality(H, max_iter=1000)
+    c = np.array(list(c.values()))
     assert norm(c[1:] - c[1]) < 1e-4
-    assert abs(c[0] / c[1] - ratio(3, 5, kind="HEC")) < 1e-4
+    assert abs(c[0] / c[1] - _ratio(3, 5, kind="HEC")) < 1e-4
 
     H = xgi.sunflower(5, 1, 7)
-    c = H.nodes.h_eigenvector_centrality(max_iter=1000).asnumpy()
+    c = xgi.uniform_h_eigenvector_centrality(H, max_iter=1000)
+    c = np.array(list(c.values()))
     assert norm(c[1:] - c[1]) < 1e-4
-    assert abs(c[0] / c[1] - ratio(5, 7, kind="HEC")) < 1e-4
+    assert abs(c[0] / c[1] - _ratio(5, 7, kind="HEC")) < 1e-4
 
     with pytest.raises(XGIError):
         H = xgi.Hypergraph([[1, 2], [2, 3, 4]])
-        H.nodes.h_eigenvector_centrality.asnumpy()
+        xgi.uniform_h_eigenvector_centrality(H)
+
+    # non-convergence
+    with pytest.raises(XGIError):
+        H = xgi.Hypergraph([[1, 2], [2, 3, 4]])
+        xgi.uniform_h_eigenvector_centrality(H, max_iter=2)
 
 
 def test_node_edge_centrality():
@@ -105,6 +112,11 @@ def test_node_edge_centrality():
     c = H.edges.node_edge_centrality.asnumpy()
     assert abs(c[0] - c[1]) < 1e-6
 
+    H = xgi.load_xgi_data("email-enron").cleanup()
+    c = xgi.node_edge_centrality(H)
+    assert len(c[0]) == H.num_nodes
+    assert len(c[1]) == H.num_edges
+
 
 def test_line_vector_centrality():
     H = xgi.Hypergraph()
@@ -128,36 +140,6 @@ def test_line_vector_centrality():
         xgi.line_vector_centrality(H)
 
 
-def ratio(r, m, kind="CEC"):
-    """Generate the ratio between largest and second largest centralities
-    for the sunflower hypergraph with one core node.
-
-    Parameters
-    ----------
-    r : int
-        Number of petals
-    m : int
-        Size of edges
-    kind : str, default: "CEC"
-        "CEC" or "HEC"
-
-    Returns
-    -------
-    float
-        Ratio
-
-    References
-    ----------
-    Three Hypergraph Eigenvector Centralities,
-    Austin R. Benson,
-    https://doi.org/10.1137/18M1203031
-    """
-    if kind == "CEC":
-        return 2 * r * (m - 1) / (np.sqrt(m**2 + 4 * (m - 1) * (r - 1)) + m - 2)
-    elif kind == "HEC":
-        return r ** (1.0 / m)
-
-
 def test_katz_centrality(edgelist1, edgelist8):
     # test hypergraph with no edge
     H = xgi.Hypergraph()
@@ -195,3 +177,130 @@ def test_katz_centrality(edgelist1, edgelist8):
     }
     for n in c:
         assert np.allclose(c[n], expected_c[n])
+
+
+@pytest.mark.slow
+def test_h_eigenvector_centrality():
+    # test empty hypergraph
+    H = xgi.Hypergraph()
+    c = xgi.h_eigenvector_centrality(H)
+    assert c == dict()
+
+    # Test no edges
+    H.add_nodes_from([0, 1, 2])
+    hec = xgi.h_eigenvector_centrality(H)
+    for i in hec:
+        assert np.isnan(hec[i])
+
+    # test disconnected
+    H.add_edge([0, 1])
+    hec = xgi.h_eigenvector_centrality(H)
+    assert set(hec) == {0, 1, 2}
+    for i in hec:
+        assert np.isnan(hec[i])
+
+    H = xgi.sunflower(3, 1, 5)
+    c = xgi.h_eigenvector_centrality(H, max_iter=1000)
+    assert (
+        max([abs(c[0] / c[i + 1] - _ratio(3, 5, kind="HEC")) for i in range(12)]) < 1e-4
+    )
+
+    H = xgi.sunflower(5, 1, 7)
+    print(H.num_nodes)
+    c = xgi.h_eigenvector_centrality(H, max_iter=1000)
+    assert (
+        max([abs(c[0] / c[i + 1] - _ratio(5, 7, kind="HEC")) for i in range(29)]) < 1e-4
+    )
+
+    H = xgi.Hypergraph([[1, 2], [2, 3, 4]])
+    c = xgi.h_eigenvector_centrality(H)
+    true_c = {
+        1: 0.24458437592396465,
+        2: 0.3014043407819482,
+        3: 0.22700561916516002,
+        4: 0.22700566412892714,
+    }
+    for i in c:
+        assert np.allclose(c[i], true_c[i])
+
+    H = xgi.load_xgi_data("email-enron")
+    H.cleanup(relabel=False)
+    c = xgi.h_eigenvector_centrality(H)
+    assert sorted(c) == sorted(H.nodes)
+
+
+@pytest.mark.slow
+def test_z_eigenvector_centrality():
+    # test empty hypergraph
+    H = xgi.Hypergraph()
+    c = xgi.z_eigenvector_centrality(H)
+    assert c == dict()
+
+    # Test no edges
+    H.add_nodes_from([0, 1, 2])
+    hec = xgi.z_eigenvector_centrality(H)
+    for i in hec:
+        assert np.isnan(hec[i])
+
+    # test disconnected
+    H.add_edge([0, 1])
+    hec = xgi.z_eigenvector_centrality(H)
+    assert set(hec) == {0, 1, 2}
+    for i in hec:
+        assert np.isnan(hec[i])
+
+    H = xgi.sunflower(3, 1, 5)
+    c = H.nodes.z_eigenvector_centrality(max_iter=1000).asdict()
+    assert (
+        max([abs(c[0] / c[i + 1] - _ratio(3, 5, kind="ZEC")) for i in range(12)]) < 1e-4
+    )
+
+    H = xgi.sunflower(5, 1, 7)
+    print(H.num_nodes)
+    c = xgi.z_eigenvector_centrality(H, max_iter=1000)
+    assert (
+        max([abs(c[0] / c[i + 1] - _ratio(5, 7, kind="ZEC")) for i in range(29)]) < 1e-4
+    )
+
+    H = xgi.Hypergraph([[1, 2], [2, 3, 4]])
+    c = xgi.z_eigenvector_centrality(H, max_iter=10000)
+    true_c = {
+        1: 0.45497398635982933,
+        2: 0.45900452108663403,
+        3: 0.04301074627676834,
+        4: 0.04301074627676829,
+    }
+    for i in c:
+        assert np.allclose(c[i], true_c[i])
+
+
+def _ratio(r, m, kind="CEC"):
+    """Generate the _ratio between largest and second largest centralities
+    for the sunflower hypergraph with one core node.
+
+    Parameters
+    ----------
+    r : int
+        Number of petals
+    m : int
+        Size of edges
+    kind : str, default: "CEC"
+        "CEC" or "HEC"
+
+    Returns
+    -------
+    float
+        Ratio
+
+    References
+    ----------
+    Three Hypergraph Eigenvector Centralities,
+    Austin R. Benson,
+    https://doi.org/10.1137/18M1203031
+    """
+    if kind == "CEC":
+        return 2 * r * (m - 1) / (np.sqrt(m**2 + 4 * (m - 1) * (r - 1)) + m - 2)
+    elif kind == "HEC":
+        return r ** (1.0 / m)
+    elif kind == "ZEC":
+        return r**0.5