diff --git a/networkz/algorithms/approximation/tests/test_coalition_formation.py b/networkz/algorithms/approximation/tests/test_coalition_formation.py
deleted file mode 100644
index cf39fd1..0000000
--- a/networkz/algorithms/approximation/tests/test_coalition_formation.py
+++ /dev/null
@@ -1,168 +0,0 @@
-"""
-Testing the :mod:`networkx.algorithms.approximation.coalition_formation` module.
-
-Which is the implementation of the Social Aware Assignment of Passengers in Ridesharing
-The social aware assignment problem belongs to the field of coalition formation, which is an important research branch 
-within multiagent systems. It analyses the outcome that results when a set of agents is partitioned into coalitions.
-Actually, Match_And_Merge model is a special case of simple Additively Separable Hedonic Games (ASHGs).
-
-Which was described in the article:
-Levinger C., Hazon N., Azaria A. Social Aware Assignment of Passengers in Ridesharing. - 2022, https://github.com/VictoKu1/ResearchAlgorithmsCourse1/raw/main/Article/2022%2C%20Chaya%20Amos%20Noam%2C%20Socially%20aware%20assignment%20of%20passengers%20in%20ride%20sharing.pdf.
-
-The match_and_merge algorithm is based on the pseudocode from the article
-which is written (as well as the tests) by Victor Kushnir.
-"""
-import math
-import random
-
-import pytest
-
-import networkx as nx
-from networkz.algorithms.approximation.coalition_formation import match_and_merge
-
-
-def small_chain_graph():
-    G = nx.Graph()
-    list_of_edges = [(1, 2), (2, 3), (3, 4), (4, 5), (4, 6)]
-    G.add_edges_from(list_of_edges)
-    return G
-
-
-def clique_graph_of_size_3():
-    G = nx.Graph()
-    list_of_edges = [(1, 2), (2, 3), (3, 1)]
-    G.add_edges_from(list_of_edges)
-    return G
-
-
-class Test_coalition_formation:
-    def test_empty_graph_returns_empty_list(self):
-        G_empty = nx.Graph()
-        assert match_and_merge(G_empty, k=0) == []
-
-    def test_small_chain_graph_with_k_4_returns_correct_partition(self):
-        G_1 = small_chain_graph()
-        assert match_and_merge(G_1, k=4) == [[1, 2], [3, 4, 5, 6]]
-
-    def test_small_chain_graph_with_k_3_returns_correct_partition(self):
-        G_1 = small_chain_graph()
-        assert match_and_merge(G_1, k=3) == [[1, 2], [3, 4, 6], [5]]
-
-    def test_small_chain_graph_with_k_2_returns_correct_partition(self):
-        G_1 = small_chain_graph()
-        assert match_and_merge(G_1, k=2) == [[1, 2], [3, 4], [5], [6]]
-
-    def test_clique_graph_of_size_3_with_k_3_returns_correct_partition(self):
-        G_clique_3 = clique_graph_of_size_3()
-        assert match_and_merge(G_clique_3, k=3) == [[1, 2, 3]]
-
-    def test_approximation_ratio(self):
-        # For each n between 5 and 15, generate a clique graph with n nodes and check for 5<k≤15
-        for n in range(5, 69):
-            G = nx.complete_graph(n)
-            P = match_and_merge(G, 2)
-            value_of_P = sum(
-                len([(u, v) for u, v in G.edges() if u in S and v in S]) for S in P
-            )
-            optimal_value = len(nx.algorithms.max_weight_matching(G, weight=1))
-            approximation_ratio = value_of_P / optimal_value
-            assert approximation_ratio >= 0.99999
-
-    def test_clique_graph_with_k_in_range_every_node_in_exactly_one_partition(self):
-        # Check that every node is in exactly one partition
-        for n in range(5, 15):
-            G = nx.complete_graph(n)
-            for k in range(5, 15):
-                if k <= n:
-                    P = match_and_merge(G, k)
-                    assert [len([p for p in P if n in p]) == 1 for n in G.nodes()]
-
-    def test_clique_graph_with_k_in_range_number_of_partitions_at_most_ceil_n_2(self):
-        # Check that the number of partitions is at most ceil(n/2)
-        for n in range(5, 15):
-            G = nx.complete_graph(n)
-            for k in range(5, 15):
-                if k <= n:
-                    P = match_and_merge(G, k)
-                    assert len(P) <= math.ceil(G.number_of_nodes() / 2)
-
-    def test_k_greater_than_n_raises_error(self):
-        # Check that it raises an error when k>n
-        for n in range(5, 15):
-            G = nx.complete_graph(n)
-            for k in range(5, 15):
-                if k > n:
-                    with pytest.raises(nx.NetworkXError):
-                        match_and_merge(G, k)
-
-    def test_random_graph_with_k_in_range_returns_correct_partition(self):
-        # For each n between 5 and 15 (inclusive), generate a random graph with n nodes and check for 5<k≤15
-        for n in range(5, 15):
-            p = 0.5
-            G = nx.gnp_random_graph(n, p)
-            for k in range(5, 15):
-                if k <= n:
-                    P = match_and_merge(G, k)
-                    assert [len(p) <= k for p in P]
-
-    def test_random_graph_with_k_in_range_every_node_in_exactly_one_partition(self):
-        # Check that every node is in exactly one partition
-        for n in range(5, 15):
-            p = 0.5
-            G = nx.gnp_random_graph(n, p)
-            for k in range(5, 15):
-                if k <= n:
-                    P = match_and_merge(G, k)
-                    assert [len([p for p in P if n in p]) == 1 for n in G.nodes()]
-
-    def test_maximum_matching(self):
-        # Check that the maximum matching is a partition
-        for n in range(5, 15):
-            p = 0.5
-            G = nx.gnp_random_graph(n, p)
-            P = [tuple(p) for p in match_and_merge(G, 2) if len(p) > 1]
-            G_test = nx.Graph()
-            G_test.add_nodes_from(sorted((G.nodes()), reverse=True))
-            G_test.add_edges_from(sorted((G.edges()), reverse=True))
-            assert len(P) == len(
-                [
-                    tuple(sorted(p))
-                    for p in sorted(nx.max_weight_matching(G_test, weight=1))
-                ]
-            )
-
-    def test_partition_is_a_social_aware_assignment_check_size_of_subset(self):
-        for n in range(5, 15):
-            p = 0.5
-            G = nx.gnp_random_graph(n, p)
-            for k in range(5, n):
-                P = match_and_merge(G, k)
-                # Check that every partition has at most k nodes
-                assert [len(p) <= k for p in P]
-
-    def test_partition_is_a_social_aware_assignment_V_P(self):
-        for n in range(5, 15):
-            p = 0.5
-            G = nx.gnp_random_graph(n, p)
-            P = match_and_merge(G, 2)
-            # Check that the value of V_P is maximized
-            V_P = 0
-            for p in P:
-                for i in p:
-                    for j in p:
-                        if G.has_edge(i, j):
-                            V_P += 1
-            assert V_P >= len(
-                [tuple(sorted(p)) for p in sorted(nx.max_weight_matching(G, weight=1))]
-            )
-
-    def test_disconnected_components_weighted_graph_with_k_5_returns_correct_partition(
-        self,
-    ):
-        # Check that the partition of graph G is correct for a disconnected graph
-        G = small_chain_graph()
-        G.add_edges_from([(7, 8), (8, 9), (9, 7)])
-        # Add random weights to the edges
-        for u, v in G.edges():
-            G[u][v]["weight"] = random.randint(1, 205)
-        assert match_and_merge(G, k=5) == [[1, 2], [3, 4, 5, 6], [7, 8, 9]]
diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/__init__.py b/networkz/algorithms/approximation/tests/test_firefighter_problem/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/networkz/algorithms/approximation/tests/__init__.py b/networkz/algorithms/tests/test_firefighter_problem/__init__.py
similarity index 100%
rename from networkz/algorithms/approximation/tests/__init__.py
rename to networkz/algorithms/tests/test_firefighter_problem/__init__.py
diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py
similarity index 100%
rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py
rename to networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_dirlaynet_minbudget.py
diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py
similarity index 100%
rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_non_spreading_minbudget.py
rename to networkz/algorithms/tests/test_firefighter_problem/test_non_spreading_minbudget.py
diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py b/networkz/algorithms/tests/test_firefighter_problem/test_parsing.py
similarity index 100%
rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_parsing.py
rename to networkz/algorithms/tests/test_firefighter_problem/test_parsing.py
diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py
similarity index 100%
rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_maxsave.py
rename to networkz/algorithms/tests/test_firefighter_problem/test_spreading_maxsave.py
diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py b/networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py
similarity index 100%
rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_spreading_minbudget.py
rename to networkz/algorithms/tests/test_firefighter_problem/test_spreading_minbudget.py
diff --git a/networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py b/networkz/algorithms/tests/test_firefighter_problem/test_utils.py
similarity index 100%
rename from networkz/algorithms/approximation/tests/test_firefighter_problem/test_utils.py
rename to networkz/algorithms/tests/test_firefighter_problem/test_utils.py