Merge pull request #142 from tigergraph/GML-1574

[GML-1574] fix(files): sync files with algos repository;

GDBMS_ALGO/topological_link_prediction/common_neighbors.gsql

@@ -0,0 +1,63 @@
+CREATE TEMPLATE QUERY GDBMS_ALGO.topological_link_prediction.common_neighbors(VERTEX v_source, VERTEX v_target, SET<STRING> e_type_set, BOOL print_results = TRUE)  SYNTAX V1 { 
+
+    /*
+    First Author: <First Author Name>
+    First Commit Date:  <First Commit Date>
+
+    Recent Author: <Recent Commit Author Name>
+    Recent Commit Date: <Recent Commit Date>
+
+
+    Repository:
+        https://github.com/tigergraph/gsql-graph-algorithms/tree/master/algorithms/Topological%20Link%20Prediction
+
+    Maturity:
+        production
+
+    Description: 
+        This query calculates the number of common neighbors between two vertices.
+        The higher the number, the closer two vertices are.
+
+    Publications:
+        NA
+
+    TigerGraph Documentation:
+        https://docs.tigergraph.com/graph-ml/current/link-prediction/common-neighbors
+
+    Parameters:
+        v_source:
+            Input vertex one
+        v_target:
+            Input vertex two
+        e_type_set:
+            edge types to traverse. If all edge types are desired, pass in "ALL" to the set.
+        print_results:
+            if True, print result (True by default)
+    */
+
+    avs = {v_source};
+    bvs = {v_target};
+
+    IF "ALL" NOT IN e_type_set THEN  # Specific edge types defined
+        # Get neighbors of source vertices
+        na = SELECT n 
+             FROM avs -(e_type_set)- :n; 
+
+        nb = SELECT n 
+             FROM bvs -(e_type_set)- :n; 
+
+    ELSE  # Use any edge types
+        # Get neighbors of source vertices
+        na = SELECT n 
+             FROM avs -()- :n; 
+
+        nb = SELECT n 
+             FROM bvs -()- :n; 
+    END;
+    # Get neighbors in common
+    u = na INTERSECT nb; 
+
+    IF print_results THEN
+        PRINT u.size() as closeness; 
+    END;
+}

GDBMS_ALGO/topological_link_prediction/preferential_attachment.gsql

@@ -0,0 +1,62 @@
+CREATE TEMPLATE QUERY GDBMS_ALGO.topological_link_prediction.preferential_attachment(VERTEX v_source, VERTEX v_target, SET<STRING> e_type_set, BOOL print_results = TRUE)  SYNTAX V1 { 
+
+    /*
+    First Author: <First Author Name>
+    First Commit Date:  <First Commit Date>
+
+    Recent Author: <Recent Commit Author Name>
+    Recent Commit Date: <Recent Commit Date>
+
+
+    Repository:
+        https://github.com/tigergraph/gsql-graph-algorithms/tree/master/algorithms/Topological%20Link%20Prediction
+
+    Maturity:
+        production
+
+    Description: 
+        This query calculates the preferential attachment value between two vertices.
+        The higher the number, the closer two vertices are.
+
+        Preferential attachment is calculated by multiplying the number of each input vertices neighbors together.
+
+    Publications:
+        NA
+
+    TigerGraph Documentation:
+        https://docs.tigergraph.com/graph-ml/current/link-prediction/preferential-attachment
+
+    Parameters:
+        v_source:
+            Input vertex one
+        v_target:
+            Input vertex two
+        e_type_set:
+            edge types to traverse. If all edge types are desired, pass in "ALL" to the set.
+        print_results:
+            if True, print result (True by default)
+    */
+
+    avs = {v_source};
+    bvs = {v_target};
+
+    # See if user specified edge types to traverse
+    IF "ALL" NOT IN e_type_set THEN
+        na = SELECT n 
+             FROM avs -(e_type_set)- :n;  # Get neighbors of vertex A
+
+        nb = SELECT n 
+             FROM bvs -(e_type_set)- :n;  // Get neighbors of vertex B
+
+    ELSE  // traverse all edge types
+        na = SELECT n 
+             FROM avs -()- :n;  // Get neighbors of vertex A
+
+        nb = SELECT n 
+             FROM bvs -()- :n;  // Get neighbors of vertex B
+    END;
+
+    IF print_results THEN
+        PRINT na.size()*nb.size() as closeness;  // calculate and return closeness value
+    END;
+}

GDBMS_ALGO/topological_link_prediction/total_neighbors.gsql

@@ -0,0 +1,59 @@
+CREATE TEMPLATE QUERY GDBMS_ALGO.topological_link_prediction.total_neighbors(VERTEX v_source, VERTEX v_target, SET<STRING> e_type_set, BOOL print_results = TRUE)  SYNTAX V1 { 
+
+    /*
+    First Author: <First Author Name>
+    First Commit Date:  <First Commit Date>
+
+    Recent Author: <Recent Commit Author Name>
+    Recent Commit Date: <Recent Commit Date>
+
+
+    Repository:
+        https://github.com/tigergraph/gsql-graph-algorithms/tree/master/algorithms/Topological%20Link%20Prediction
+
+    Maturity:
+        production
+
+    Description: 
+        This query calculates the number of total neighbors of two vertices.
+        The higher the number, the closer two vertices are.
+
+    Publications:
+        NA
+
+    TigerGraph Documentation:
+        https://docs.tigergraph.com/graph-ml/current/link-prediction/total-neighbors
+
+    Parameters:
+        v_source:
+            Input vertex one
+        v_target:
+            Input vertex two
+        e_type_set:
+            edge types to traverse. If all edge types are desired, pass in "ALL" to the set.
+        print_results:
+            if True, print result (True by default)
+    */
+
+    avs = {v_source};
+    bvs = {v_target};
+
+    IF "ALL" NOT IN e_type_set THEN  # Specific edge types defined as parameters
+        na = SELECT n 
+             FROM avs -(e_type_set)- :n;  # Get vertex A's neighbors
+
+        nb = SELECT n 
+             FROM bvs -(e_type_set)- :n;  # Get vertex B's neighbors
+
+    ELSE  # Use all edge types
+        na = SELECT n 
+             FROM avs -()- :n;  # Get vertex A's neighbors
+
+        nb = SELECT n 
+             FROM bvs -()- :n;  # Get vertex B's neighbors
+    END;
+    u = na UNION nb;  # Get all neighbors
+    IF print_results THEN
+        PRINT u.size() as closeness; 
+    END;
+}

algorithms/Centrality/betweenness/tg_betweenness_cent.gsql

@@ -1,4 +1,4 @@
-CREATE QUERY tg_betweenness_cent(SET<STRING> v_type_set, SET<STRING> e_type_set, STRING reverse_e_type,INT max_hops = 10,
+CREATE QUERY tg_betweenness_cent(SET<STRING> v_type_set, SET<STRING> e_type_set, SET<STRING> reverse_e_type,INT max_hops = 10,
     INT top_k = 100, BOOL print_results = True, STRING result_attribute = "",
     STRING file_path = "", BOOL display_edges = FALSE) SYNTAX V1 { 
 

algorithms/Centrality/closeness/exact/tg_closeness_cent.gsql

@@ -1,4 +1,4 @@
-CREATE QUERY tg_closeness_cent(SET<STRING> v_type_set, SET<STRING> e_type_set, STRING reverse_e_type,INT max_hops = 10,
+CREATE QUERY tg_closeness_cent(SET<STRING> v_type_set, SET<STRING> e_type_set, SET<STRING> reverse_e_type,INT max_hops = 10,
   INT top_k = 100, BOOL wf = TRUE, BOOL print_results = True, STRING result_attribute = "",
   STRING file_path = "", BOOL display_edges = FALSE) SYNTAX V1 {
 

algorithms/Community/k_means/tg_kmeans_sub.gsql

@@ -73,7 +73,7 @@ CREATE QUERY tg_kmeans_sub(int k, float max_change = 1.0, string v_type, string
   IF random == TRUE THEN
       FOREACH i IN range[0,k-1] DO
           FOREACH j IN range[0,99] DO
-              @@centroids_array[i][j] = rand_int(-2,5)-0.123*0.4;
+              @@centroids_array[i][j] = tg_rand_int(-2,5)-0.123*0.4;
           END;
       END;
   END;