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diff --git a/_sources/content/algorithms/assortativity/correlation.ipynb b/_sources/content/algorithms/assortativity/correlation.ipynb
diff --git a/_sources/content/algorithms/assortativity/correlation.md b/_sources/content/algorithms/assortativity/correlation.md
@@ -22,16 +22,29 @@ language_info:
   version: 3.8.5
 ---
 
-# Node assortativity coefficients and correlation measures
+# Node Assortativity Coefficients and Correlation Measures
 
-In this tutorial, we will go through the theory of [assortativity](https://en.wikipedia.org/wiki/Assortativity) and its measures.
+In this tutorial, we will explore the theory of assortativity [^1] and its measures. 
 
-Specifically, we'll focus on assortativity measures available in NetworkX at [algorithms/assortativity/correlation.py](https://github.com/networkx/networkx/blob/main/networkx/algorithms/assortativity/correlation.py):
+We'll focus on assortativity measures available in NetworkX at [`algorithms/assortativity/correlation.py`](https://github.com/networkx/networkx/blob/main/networkx/algorithms/assortativity/correlation.py):
 * Attribute assortativity
 * Numeric assortativity
 * Degree assortativity
 
-as well as mixing matrices, which are closely releated to assortativity measures.
+as well as mixing matrices, which are closely related to assortativity measures.
+
+## Import packages
+
+```{code-cell} ipython3
+import networkx as nx
+import matplotlib.pyplot as plt
+import pickle
+import copy
+import random
+import warnings
+
+%matplotlib inline
+```
 
 ## Assortativity
 
@@ -80,7 +93,7 @@ Pearson correlation coefficient.
 
 Here the property $P(v)$ is a nominal property assigned to each node.
 As defined above we calculate the normalized mixing matrix $e$ and from that we
-define the attribute assortativity coefficient [^1] as below.
+define the attribute assortativity coefficient [^2] as below.
 
 From here onwards we will use subscript notation to denote indexing, for eg. $P_i = P[i]$ and $e_{ij} = e[i][j]$
 
@@ -93,7 +106,7 @@ It is implemented as `attribute_assortativity_coefficient`.
 Here the property $P(v)$ is a numerical property assigned to each
 node and the definition of the normalized mixing
 matrix $e$, $\sigma_a$, and $\sigma_b$ are same as above.
-From these we define numeric assortativity coefficient [^1] as below.
+From these we define numeric assortativity coefficient [^2] as below.
 
 $$ r = \frac{\sum\limits_{i,j}P_i P_j(e_{ij} -a_i b_j)}{\sigma_a\sigma_b} $$
 
@@ -105,7 +118,7 @@ When it comes to measuring degree assortativity for directed networks we have
 more options compared to assortativity w.r.t a property because we have 2 types
 of degrees, namely in-degree and out-degree.
 Based on the 2 types of degrees we can measure $2 \times 2 =4$ different types
-of degree assortativity [^2]:
+of degree assortativity [^3]:
 
 1. r(in,in) : Measures tendency of having a directed edge (u,v) such that, in-degree(u) = in-degree(v).
 2. r(in,out) : Measures tendency of having a directed edge (u,v) such that, in-degree(u) = out-degree(v).
@@ -130,25 +143,14 @@ It is implemented as `degree_assortativity_coefficient` and
 `scipy.stats.pearsonr` to calculate the assortativity coefficient which makes
 it potentally faster.
 
-## Example
+## Assortativity Example
 
-```{code-cell} ipython3
-%matplotlib inline
-import networkx as nx
-import matplotlib.pyplot as plt
-import pickle
-import copy
-import random
-import warnings
-
-warnings.filterwarnings("ignore")
-```
++++
 
 Illustrating how value of assortativity changes
 
 ```{code-cell} ipython3
 gname = "g2"
-# loading the graph
 G = nx.read_graphml(f"data/{gname}.graphml")
 with open(f"data/pos_{gname}", "rb") as fp:
     pos = pickle.load(fp)
@@ -261,6 +263,10 @@ are drawn.
 
 +++
 
-[^1]: M. E. J. Newman, Mixing patterns in networks <https://doi.org/10.1103/PhysRevE.67.026126>
+## References
+
+[^1]: [Wikipedia, Assortativity](https://en.wikipedia.org/wiki/Assortativity)
+
+[^2]: M. E. J. Newman, Mixing patterns in networks <https://doi.org/10.1103/PhysRevE.67.026126>
 
-[^2]: Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M. Edge direction and the structure of networks <https://doi.org/10.1073/pnas.0912671107>
+[^3]: Foster, J.G., Foster, D.V., Grassberger, P. & Paczuski, M. Edge direction and the structure of networks <https://doi.org/10.1073/pnas.0912671107>
diff --git a/_sources/content/algorithms/dag/index.ipynb b/_sources/content/algorithms/dag/index.ipynb
diff --git a/_sources/content/algorithms/dag/index.md b/_sources/content/algorithms/dag/index.md
@@ -25,25 +25,24 @@ language_info:
 # Directed Acyclic Graphs & Topological Sort
 
 In this tutorial, we will explore the algorithms related to a directed acyclic graph
-(or a "dag" as it is sometimes called) implemented in networkx under `networkx/algorithms/dag.py`.
+(or a "DAG" as it is sometimes called) implemented in NetworkX under [`networkx/algorithms/dag.py`](https://github.com/networkx/networkx/blob/main/networkx/algorithms/dag.py).
 
 First of all, we need to understand what a directed graph is.
 
-## Directed Graph
-
-### Example
+## Import packages
 
 ```{code-cell} ipython3
-%matplotlib inline
 import networkx as nx
 import matplotlib.pyplot as plt
-```
+import inspect
 
-```{code-cell} ipython3
-triangle_graph = nx.from_edgelist([(1, 2), (2, 3), (3, 1)], create_using=nx.DiGraph)
+%matplotlib inline
 ```
 
+## Example: Directed Graphs
+
 ```{code-cell} ipython3
+triangle_graph = nx.DiGraph([(1, 2), (2, 3), (3, 1)])
 nx.draw_planar(
     triangle_graph,
     with_labels=True,
@@ -75,7 +74,6 @@ clothing_graph = nx.read_graphml(f"data/clothing_graph.graphml")
 
 ```{code-cell} ipython3
 plt.figure(figsize=(12, 12), dpi=150)
-
 nx.draw_planar(
     clothing_graph,
     arrowsize=12,
@@ -164,7 +162,7 @@ Then, a topological sort gives an order in which to perform the jobs.
 
 A closely related application of topological sorting algorithms
 was first studied in the early 1960s in the context of the
-[PERT technique](https://en.wikipedia.org/wiki/Program_evaluation_and_review_technique)
+PERT technique [^1]
 for scheduling in project management.
 In this application, the vertices of a graph represent the milestones of a project,
 and the edges represent tasks that must be performed between one milestone and another.
@@ -343,8 +341,6 @@ We need to check this while the `while` loop is running.
 Combining all of the above gives the current implementation of the `topological_generations()` function in NetworkX.
 
 ```{code-cell} ipython3
-import inspect
-
 print(inspect.getsource(nx.topological_generations))
 ```
 
@@ -353,3 +349,7 @@ Let's finally see what the result will be on the `clothing_graph`.
 ```{code-cell} ipython3
 list(nx.topological_generations(clothing_graph))
 ```
+
+## References
+
+[^1]: [Wikipedia, PERT Technique](https://en.wikipedia.org/wiki/Program_evaluation_and_review_technique)
diff --git a/_sources/content/algorithms/euler/euler.ipynb b/_sources/content/algorithms/euler/euler.ipynb
diff --git a/_sources/content/algorithms/euler/euler.md b/_sources/content/algorithms/euler/euler.md
@@ -14,7 +14,15 @@ kernelspec:
 
 # Euler's Algorithm
 
-In this tutorial, we will explore the Euler's algorithm and its implementation in NetworkX under `networkx/algorithms/euler.py`.
++++
+
+In this tutorial, we will explore Euler's algorithm and its implementation in NetworkX under [`networkx/algorithms/euler.py`](https://github.com/networkx/networkx/blob/main/networkx/algorithms/euler.py).
+
+## Import package
+
+```{code-cell}
+import networkx as nx
+```
 
 ## Seven Bridges of Königsberg
 
@@ -37,8 +45,7 @@ In order to have a clear look, we should first simplify the map a little.
 Euler observed that the choice of route inside each land mass is irrelevant. The only thing that matters is the sequence of bridges to be crossed. This observation allows us to abstract the problem even more. In the graph below, blue vertices represent the land masses and edges represent the bridges that connect them.
 
 ```{code-cell}
-import networkx as nx
-
+# Create graph
 G = nx.DiGraph()
 G.add_edge("A", "B", label="a")
 G.add_edge("B", "A", label="b")
@@ -50,6 +57,7 @@ G.add_edge("C", "D", label="g")
 
 positions = {"A": (0, 0), "B": (1, -2), "C": (1, 2), "D": (2, 0)}
 
+# Visualize graph
 nx.draw_networkx_nodes(G, pos=positions, node_size=500)
 nx.draw_networkx_edges(
     G, pos=positions, edgelist=[("A", "D"), ("B", "D"), ("C", "D")], arrowstyle="-"
@@ -73,9 +81,10 @@ Note that every Euler Circuit is also an Euler Path.
 
 ### Euler's Method
 
-Euler[^2] denoted land masses of the town by capital letters $A$, $B$, $C$ and $D$ and bridges by lowercase $a$, $b$, $c$, $d$, $e$, $f$ and $g$. Let's draw the graph based on this node and edge labels.
+Euler [^2] denoted land masses of the town by capital letters $A$, $B$, $C$ and $D$ and bridges by lowercase $a$, $b$, $c$, $d$, $e$, $f$ and $g$. Let's draw the graph based on this node and edge labels.
 
 ```{code-cell}
+# Design and draw graph
 edge_labels = nx.get_edge_attributes(G, "label")
 
 nx.draw_networkx_nodes(G, pos=positions, node_size=500)
@@ -117,7 +126,7 @@ Euler generalized the method he applied for Königsberg problem as follows:
 - If there are two vertices with odd degree, then they are the starting and ending vertices.
 - If there are no vertices with odd degree, any vertex can be starting or ending vertex and the graph has also an Euler Circuit.
 
-## NetworkX Implementation of Euler's Algorithm
+## Euler's Algorithm in NetworkX
 
 NetworkX implements several methods using the Euler's algorithm. These are:
 - **is_eulerian**      : Whether the graph has an Eulerian circuit
@@ -282,5 +291,6 @@ Euler's algorithm is essential for anyone or anything that uses paths. Some exam
 
 ## References
 
-[^1]: <https://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg>
-[^2]: Euler, Leonhard, ‘Solutio problematis ad geometriam situs pertinentis’ (1741), Eneström 53, MAA Euler Archive.
+[^1]:  [Wikipedia, Seven Bridge of Konigsberg](https://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg)
+
+[^2]:  Euler, Leonhard, ‘Solutio problematis ad geometriam situs pertinentis’ (1741), Eneström 53, MAA Euler Archive.