docs: Fix a few typos

There are small typos in:
- docs/source/index.rst
- docs/source/notes/introduction.rst
- karateclub/graph_embedding/gl2vec.py
- karateclub/graph_embedding/graph2vec.py
- karateclub/graph_embedding/sf.py
- karateclub/node_embedding/meta/neu.py

Fixes:
- Should read `occurrence` rather than `occurence`.
- Should read `community` rather than `commmunity`.
- Should read `with` rather than `wiht`.
- Should read `higher` rather than `higer`.
- Should read `eigenvalues` rather than `egeinvalues`.

Signed-off-by: Tim Gates <[email protected]>

docs/source/index.rst

@@ -3,7 +3,7 @@
 Karate Club Documentation
 ===============================
 
-*Karate Club* is an unsupervised machine learning extension library for `NetworkX <https://networkx.github.io/>`_. It builds on other open source linear algebra, machine learning, and graph signal processing libraries such as `Numpy <https://numpy.org/>`_, `Scipy <https://www.scipy.org/>`_, `Gensim <https://radimrehurek.com/gensim/>`_, `PyGSP <https://pygsp.readthedocs.io/en/stable/>`_, and `Scikit-Learn <https://scikit-learn.org/stable/>`_. *Karate Club* consists of state-of-the-art methods to do unsupervised learning on graph structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping commmunity detection methods. Implemented methods cover a wide range of network science (`NetSci <https://netscisociety.net/home>`_, `Complenet <https://complenet.weebly.com/>`_), data mining (`ICDM <http://icdm2019.bigke.org/>`_, `CIKM <http://www.cikm2019.net/>`_, `KDD <https://www.kdd.org/kdd2020/>`_), artificial intelligence (`AAAI <http://www.aaai.org/Conferences/conferences.php>`_, `IJCAI <https://www.ijcai.org/>`_) and machine learning (`NeurIPS <https://nips.cc/>`_, `ICML <https://icml.cc/>`_, `ICLR <https://iclr.cc/>`_) conferences, workshops, and pieces from prominent journals. 
+*Karate Club* is an unsupervised machine learning extension library for `NetworkX <https://networkx.github.io/>`_. It builds on other open source linear algebra, machine learning, and graph signal processing libraries such as `Numpy <https://numpy.org/>`_, `Scipy <https://www.scipy.org/>`_, `Gensim <https://radimrehurek.com/gensim/>`_, `PyGSP <https://pygsp.readthedocs.io/en/stable/>`_, and `Scikit-Learn <https://scikit-learn.org/stable/>`_. *Karate Club* consists of state-of-the-art methods to do unsupervised learning on graph structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping community detection methods. Implemented methods cover a wide range of network science (`NetSci <https://netscisociety.net/home>`_, `Complenet <https://complenet.weebly.com/>`_), data mining (`ICDM <http://icdm2019.bigke.org/>`_, `CIKM <http://www.cikm2019.net/>`_, `KDD <https://www.kdd.org/kdd2020/>`_), artificial intelligence (`AAAI <http://www.aaai.org/Conferences/conferences.php>`_, `IJCAI <https://www.ijcai.org/>`_) and machine learning (`NeurIPS <https://nips.cc/>`_, `ICML <https://icml.cc/>`_, `ICLR <https://iclr.cc/>`_) conferences, workshops, and pieces from prominent journals. 
 
 
 .. code-block:: latex

docs/source/notes/introduction.rst

@@ -4,7 +4,7 @@ Introduction by example
 *Karate Club* is an unsupervised machine learning extension library for `NetworkX <https://networkx.github.io/>`_.
 
 
-*Karate Club* is an unsupervised machine learning extension library for `NetworkX <https://networkx.github.io/>`_. It builds on other open source linear algebra, machine learning, and graph signal processing libraries such as `Numpy <https://numpy.org/>`_, `Scipy <https://www.scipy.org/>`_, `Gensim <https://radimrehurek.com/gensim/>`_, `PyGSP <https://pygsp.readthedocs.io/en/stable/>`_, and `Scikit-Learn <https://scikit-learn.org/stable/>`_. *Karate Club* consists of state-of-the-art methods to do unsupervised learning on graph structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping commmunity detection methods. Implemented methods cover a wide range of network science (`NetSci <https://netscisociety.net/home>`_, `Complenet <https://complenet.weebly.com/>`_), data mining (`ICDM <http://icdm2019.bigke.org/>`_, `CIKM <http://www.cikm2019.net/>`_, `KDD <https://www.kdd.org/kdd2020/>`_), artificial intelligence (`AAAI <http://www.aaai.org/Conferences/conferences.php>`_, `IJCAI <https://www.ijcai.org/>`_) and machine learning (`NeurIPS <https://nips.cc/>`_, `ICML <https://icml.cc/>`_, `ICLR <https://iclr.cc/>`_) conferences, workshops, and pieces from prominent journals. 
+*Karate Club* is an unsupervised machine learning extension library for `NetworkX <https://networkx.github.io/>`_. It builds on other open source linear algebra, machine learning, and graph signal processing libraries such as `Numpy <https://numpy.org/>`_, `Scipy <https://www.scipy.org/>`_, `Gensim <https://radimrehurek.com/gensim/>`_, `PyGSP <https://pygsp.readthedocs.io/en/stable/>`_, and `Scikit-Learn <https://scikit-learn.org/stable/>`_. *Karate Club* consists of state-of-the-art methods to do unsupervised learning on graph structured data. To put it simply it is a Swiss Army knife for small-scale graph mining research. First, it provides network embedding techniques at the node and graph level. Second, it includes a variety of overlapping and non-overlapping community detection methods. Implemented methods cover a wide range of network science (`NetSci <https://netscisociety.net/home>`_, `Complenet <https://complenet.weebly.com/>`_), data mining (`ICDM <http://icdm2019.bigke.org/>`_, `CIKM <http://www.cikm2019.net/>`_, `KDD <https://www.kdd.org/kdd2020/>`_), artificial intelligence (`AAAI <http://www.aaai.org/Conferences/conferences.php>`_, `IJCAI <https://www.ijcai.org/>`_) and machine learning (`NeurIPS <https://nips.cc/>`_, `ICML <https://icml.cc/>`_, `ICLR <https://iclr.cc/>`_) conferences, workshops, and pieces from prominent journals. 
 
 --------------------------------------------------------------------------------
 

karateclub/graph_embedding/gl2vec.py

@@ -11,7 +11,7 @@ class GL2Vec(Estimator):
     from the ICONIP '19 paper "GL2vec: Graph Embedding Enriched by Line Graphs with Edge Features".
     First, the algorithm creates the line graph of each graph in the graph dataset.
     The procedure creates Weisfeiler-Lehman tree features for nodes in graphs. Using
-    these features a document (graph) - feature co-occurence matrix is decomposed in order
+    these features a document (graph) - feature co-occurrence matrix is decomposed in order
     to generate representations for the graphs.
 
     The procedure assumes that nodes have no string feature present and the WL-hashing

karateclub/graph_embedding/graph2vec.py

@@ -10,7 +10,7 @@ class Graph2Vec(Estimator):
     r"""An implementation of `"Graph2Vec" <https://arxiv.org/abs/1707.05005>`_
     from the MLGWorkshop '17 paper "Graph2Vec: Learning Distributed Representations of Graphs".
     The procedure creates Weisfeiler-Lehman tree features for nodes in graphs. Using
-    these features a document (graph) - feature co-occurence matrix is decomposed in order
+    these features a document (graph) - feature co-occurrence matrix is decomposed in order
     to generate representations for the graphs.
 
     The procedure assumes that nodes have no string feature present and the WL-hashing

karateclub/graph_embedding/sf.py

@@ -8,7 +8,7 @@
 class SF(Estimator):
     r"""An implementation of `"SF" <https://arxiv.org/abs/1810.09155>`_
     from the NeurIPS Relational Representation Learning Workshop '18 paper "A Simple Baseline Algorithm for Graph Classification".
-    The procedure calculates the k lowest egeinvalues of the normalized Laplacian.
+    The procedure calculates the k lowest eigenvalues of the normalized Laplacian.
     If the graph has a lower number of eigenvalues than k the representation is padded with zeros.
 
     Args:

karateclub/node_embedding/meta/neu.py

@@ -7,12 +7,12 @@
 class NEU(Estimator):
     r"""An implementation of `"NEU" <https://www.ijcai.org/Proceedings/2017/0544.pdf>`_
     from the IJCAI 17 paper "Fast Network Embedding Enhancement via High Order Proximity Approximation".
-    The procedure uses an arbitrary embedding and augments it by higher order proximities wiht a recursive
+    The procedure uses an arbitrary embedding and augments it by higher order proximities with a recursive
     meta learning algorithm.
 
     Args:
         L1 (float): Weight of lower order proximities. Defauls is 0.5
-        L2 (float): Weight of higer order proximities. Default is 0.25.
+        L2 (float): Weight of higher order proximities. Default is 0.25.
         T (int): Number of iterations. Default is 1.
         seed (int): Random seed value. Default is 42.
     """