change in the documentation

doc/source/graph_generator.rst

@@ -13,8 +13,11 @@ For example, if you want to generate a graph following the stochastic block mode
     from lib.random import stochastic_block_model_graph
     G = stochastic_block_model_graph(nb_nodes=300,nb_edges=1200,nb_com=5,percentage_edge_betw=0.01)
 
+Graph Models
+------------
+
 Stochastic Block Model
-----------------------
+^^^^^^^^^^^^^^^^^^^^^^
 This model partitions :math:`n` vertices in :math:`k` blocks, and places edges between pairs of nodes with a probability that depends on the vertices membership. The probability of a pair of nodes is computed based four parameter, the number of vertices :math:`|V|` and edges :math:`|E|`, the number of blocks :math:`|B|` and finally, the percentage of interlinks :math:`per_{betw}`, i.e number of selected pairs of nodes that belongs to different blocks.
 
 First, we assign randomly a block :math:`b_i \in B` for each node :math:`v \in V`. In this model, all blocks are associated with the same number of nodes :math:`\frac{|V|}{|B|}`. Second, to compute the probability of a pair of nodes to be connected, we use the function :math:`f(u,v)` defined as follows:
@@ -46,7 +49,7 @@ To compute the :math:`p_{in}` and :math:`p_{out}`, we do the following:
 
 
 Spatial Model
--------------
+^^^^^^^^^^^^^
 This model generate a graph with $n$ vertices and $e$ edges selected randomly. Edges are selected using the deterrence function defined in the following formula:
 
 .. math::
@@ -57,18 +60,20 @@ where :math:`u` and :math:`v` are two vertices and :math:`p_u` and :math:`p_v` c
 Nodes coordinates can be generated randomly or randomly selected from existing places (here countries centroids).
 
 ER Random Graph
----------------
+^^^^^^^^^^^^^^^
 The model returns a Erdős-Rényi graph or a binomial graph. Basically,
 the model generates a graph :math:`G_{n,m}` where :math:`n` corresponds to the number of vertices
 and :math:`m` the number of edges in :math:`G_{n,m}`. In this model, each pair of nodes has the same
 probability to be connected.
 
 Configuration model
--------------------
+^^^^^^^^^^^^^^^^^^^
 
 The configuration model generates a graph (graph with parallel edges and self loops) by randomly assigning edges to match a given degree distribution. In our case, generated graph degree distribution follows a powerlaw. We use the Molloy-Reed approach [molloy1995critical]_  to generate the graph.
 
 .. [molloy1995critical] Molloy, M., & Reed, B. (1995). A critical point for random graphs with a given degree sequence. Random structures & algorithms, 6(2‐3), 161-180.
+
+
 Generate a graph dataset based on different models and configurations
 ---------------------------------------------------------------------
 

doc/source/link_pred_eval.rst

 Link Prediction Evaluation
 ==========================
+
+Link Prediction with state of the art methods
+---------------------------------------------
+
+In order to evaluate link predictions methods on a graph dataset, we use the `run_eval.py` script. Here
+a graph dataset is a dirctory that contains a file for each graph. For now, the accepted graph format are
+: edgelist (txt), gephi (.gexf) and graphml (.gml).
+
+To launch the evaluation, just run the following command in your terminal:
+
+.. code-block::shell
+
+    python run_eval.py <dataset_dir_path> <output_filename> [-f graph_format] [-t edge fraction used] [-v verbose]
+
+
+Select which link prediction methods to evaluate
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+You can change the methods evaluated in the `evalNE_script.py` file by changing different variables.
+To change the heuristics evaluated, change the following :
+
+.. code-block:: python
+
+    methods_heuristics = ['random_prediction',
+        'common_neighbours',
+        'jaccard_coefficient',
+        "adamic_adar_index",
+        "preferential_attachment",
+        "resource_allocation_index",
+        "stochastic_block_model",
+        "stochastic_block_model_degree_corrected",
+        "spatial_link_prediction"
+               ]
+
+The string corresponds to the name of the function available in `evalne.methods.similarity` modules.
+
+Thanks to **OpenNE**, we can also evaluated methods based on networks embedding. To remove or add such methods
+modify the following variables in the same file :
+
+.. code-block:: python
+
+    methods_ne = ["node2vec" ,"hope-opne", "gf", "sdne", "deepWalk", "line", "grarep"]
+    commands = [
+        "python -m openne --method node2vec --graph-format edgelist --epochs 100 --number-walks 10 --walk-length 80 --window-size 10",
+        "python -m openne --method hope --epochs 100",
+        "python -m openne --method gf --epochs 100",
+        "python -m openne --method sdne --epochs 100 --encoder-list [1024,128] --beta 5 --bs 500",
+        "python -m openne --method deepWalk --graph-format edgelist --epochs 100 --number-walks 10 --walk-length 80 --window-size 10",
+        "python -m openne --method line --graph-format edgelist --epochs 10",
+        "python -m openne --method grarep --epochs 100"
+        ]
+
+More specifically, in `methods` you add the name of the one method you want to use; and in `commands`, you add the openne
+command required to compute the embedding using this method.
+
+Link prediction based on the erosion model
+------------------------------------------
+
+To run the evaluation of our link prediction method based on erosion, you must use the `eval_mixed_model.py` script.

evalNE_script.py

@@ -58,7 +58,7 @@ log("Dataset Built !")
 scoresheet = Scoresheet()
 
 # Set the baselines
-methods = ['random_prediction',
+methods_heuristics = ['random_prediction',
     'common_neighbours',
     'jaccard_coefficient',
     "adamic_adar_index",
@@ -70,7 +70,7 @@ methods = ['random_prediction',
            ]
 
 # Evaluate heristics
-pbar = tqdm(methods,disable= (not args.verbose))
+pbar = tqdm(methods_heuristics,disable= (not args.verbose))
 for method in pbar:
     pbar.set_description("Evaluate "+method)
     result = nee.evaluate_baseline(method=method,)
@@ -81,7 +81,7 @@ if args.network_embedding:
         # Check if OpenNE is installed
         import openne
         # Set embedding methods from OpenNE
-        methods = "node2vec hope-opne gf sdne deepWalk line grarep".split() #lap-opne
+        methods_ne = ["node2vec" ,"hope-opne", "gf", "sdne", "deepWalk", "line", "grarep"]
         commands = [
             "python -m openne --method node2vec --graph-format edgelist --epochs 100 --number-walks 10 --walk-length 80 --window-size 10",
             "python -m openne --method hope --epochs 100",
@@ -90,19 +90,18 @@ if args.network_embedding:
             "python -m openne --method deepWalk --graph-format edgelist --epochs 100 --number-walks 10 --walk-length 80 --window-size 10",
             "python -m openne --method line --graph-format edgelist --epochs 10",
             "python -m openne --method grarep --epochs 100"
-            # "python -m openne --method lap --epochs 100",
             ]
         edge_emb = [ 'hadamard'] #'average',
 
         # Evaluate embedding methods
-        pbar = tqdm(enumerate(methods), disable=(not args.verbose))
+        pbar = tqdm(enumerate(methods_ne), disable=(not args.verbose))
         for i,method in pbar:
             pbar.set_description("Evaluate "+method)
             is_weighted = nx.is_weighted(G)
             command = commands[i] + " --input {} --output {} --representation-size {}"
             if is_weighted:
                 command = command + " --weighted"
-            results = nee.evaluate_cmd(method_name=methods[i], method_type='ne', command=command,
+            results = nee.evaluate_cmd(method_name=methods_ne[i], method_type='ne', command=command,
                                        edge_embedding_methods=edge_emb, input_delim=' ', output_delim=' ',  verbose=args.verbose,write_weights=is_weighted)
             scoresheet.log_results(results)