From bf7cd0f8c0046b2c5484f1685b6f9d45c96aeeab Mon Sep 17 00:00:00 2001 From: Tim Gates Date: Tue, 19 Jul 2022 21:00:28 +1000 Subject: [PATCH] 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 --- docs/source/index.rst | 2 +- docs/source/notes/introduction.rst | 2 +- karateclub/graph_embedding/gl2vec.py | 2 +- karateclub/graph_embedding/graph2vec.py | 2 +- karateclub/graph_embedding/sf.py | 2 +- karateclub/node_embedding/meta/neu.py | 4 ++-- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index 096c17ec..7583c21b 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,7 +3,7 @@ Karate Club Documentation =============================== -*Karate Club* is an unsupervised machine learning extension library for `NetworkX `_. It builds on other open source linear algebra, machine learning, and graph signal processing libraries such as `Numpy `_, `Scipy `_, `Gensim `_, `PyGSP `_, and `Scikit-Learn `_. *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 `_, `Complenet `_), data mining (`ICDM `_, `CIKM `_, `KDD `_), artificial intelligence (`AAAI `_, `IJCAI `_) and machine learning (`NeurIPS `_, `ICML `_, `ICLR `_) conferences, workshops, and pieces from prominent journals. +*Karate Club* is an unsupervised machine learning extension library for `NetworkX `_. It builds on other open source linear algebra, machine learning, and graph signal processing libraries such as `Numpy `_, `Scipy `_, `Gensim `_, `PyGSP `_, and `Scikit-Learn `_. *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 `_, `Complenet `_), data mining (`ICDM `_, `CIKM `_, `KDD `_), artificial intelligence (`AAAI `_, `IJCAI `_) and machine learning (`NeurIPS `_, `ICML `_, `ICLR `_) conferences, workshops, and pieces from prominent journals. .. code-block:: latex diff --git a/docs/source/notes/introduction.rst b/docs/source/notes/introduction.rst index 152515ba..fb32c926 100644 --- a/docs/source/notes/introduction.rst +++ b/docs/source/notes/introduction.rst @@ -4,7 +4,7 @@ Introduction by example *Karate Club* is an unsupervised machine learning extension library for `NetworkX `_. -*Karate Club* is an unsupervised machine learning extension library for `NetworkX `_. It builds on other open source linear algebra, machine learning, and graph signal processing libraries such as `Numpy `_, `Scipy `_, `Gensim `_, `PyGSP `_, and `Scikit-Learn `_. *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 `_, `Complenet `_), data mining (`ICDM `_, `CIKM `_, `KDD `_), artificial intelligence (`AAAI `_, `IJCAI `_) and machine learning (`NeurIPS `_, `ICML `_, `ICLR `_) conferences, workshops, and pieces from prominent journals. +*Karate Club* is an unsupervised machine learning extension library for `NetworkX `_. It builds on other open source linear algebra, machine learning, and graph signal processing libraries such as `Numpy `_, `Scipy `_, `Gensim `_, `PyGSP `_, and `Scikit-Learn `_. *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 `_, `Complenet `_), data mining (`ICDM `_, `CIKM `_, `KDD `_), artificial intelligence (`AAAI `_, `IJCAI `_) and machine learning (`NeurIPS `_, `ICML `_, `ICLR `_) conferences, workshops, and pieces from prominent journals. -------------------------------------------------------------------------------- diff --git a/karateclub/graph_embedding/gl2vec.py b/karateclub/graph_embedding/gl2vec.py index f23b216c..ec5f9038 100644 --- a/karateclub/graph_embedding/gl2vec.py +++ b/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 diff --git a/karateclub/graph_embedding/graph2vec.py b/karateclub/graph_embedding/graph2vec.py index 2171c1fe..5db98fb7 100644 --- a/karateclub/graph_embedding/graph2vec.py +++ b/karateclub/graph_embedding/graph2vec.py @@ -10,7 +10,7 @@ class Graph2Vec(Estimator): r"""An implementation of `"Graph2Vec" `_ 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 diff --git a/karateclub/graph_embedding/sf.py b/karateclub/graph_embedding/sf.py index 97cfeee1..22035d58 100644 --- a/karateclub/graph_embedding/sf.py +++ b/karateclub/graph_embedding/sf.py @@ -8,7 +8,7 @@ class SF(Estimator): r"""An implementation of `"SF" `_ 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: diff --git a/karateclub/node_embedding/meta/neu.py b/karateclub/node_embedding/meta/neu.py index 23468ecc..3f460adc 100644 --- a/karateclub/node_embedding/meta/neu.py +++ b/karateclub/node_embedding/meta/neu.py @@ -7,12 +7,12 @@ class NEU(Estimator): r"""An implementation of `"NEU" `_ 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. """