minor modifications

localgraphclustering/ncp.py

@@ -632,6 +632,19 @@ def l1reg(self,
         for func in funcs.keys():
             self.add_random_node_samples(method=func,methodname=funcs[func],ratio=ratio,nthreads=nthreads,timeout=timeout/len(funcs))
         return self
+
+    def l1reg_rand(self,
+              gamma: float = 0.01/0.99,
+              rholist: List[float] = [1.0e-10,1.0e-8,1.0e-7,1.0e-6,1.0e-5,1.0e-4],
+              ratio: float = 0.3,
+              nthreads: int = 4,
+              timeout: float = 1000):
+        alpha = 1.0-1.0/(1.0+gamma)
+        funcs = {partialfunc(spectral_clustering, alpha=alpha,rho=rho,method="l1reg-rand"):'l1reg;rho=%.0e'%(rho)
+                    for rho in rholist}
+        for func in funcs.keys():
+            self.add_random_node_samples(method=func,methodname=funcs[func],ratio=ratio,nthreads=nthreads,timeout=timeout/len(funcs))
+        return self
 
     def crd(self,
             U: int = 3,

localgraphclustering/spectral_clustering.py

@@ -32,7 +32,7 @@ def spectral_clustering(G, ref_nodes,
 
     method: str
         Which method to use for the nodes embedding.
-        Options: "acl", "l1reg", "nibble", "fiedler", "fiedler_local"
+        Options: "acl", "l1reg", "l1reg-rand", "nibble", "fiedler", "fiedler_local"
 
     refine:
 
@@ -94,7 +94,7 @@ def spectral_clustering(G, ref_nodes,
     if G._weighted:
         warnings.warn("The weights of the graph will be discarded. Use approximate_PageRank_weighted instead if you want to keep the edge weights.")
 
-    if method == "acl" or method == "acl_weighted" or method == "l1reg":
+    if method == "acl" or method == "acl_weighted" or method == "l1reg" or method == "l1reg-rand":
         p = approximate_PageRank(G,ref_nodes,timeout = timeout, iterations = iterations, alpha = alpha,
             rho = rho, epsilon = epsilon, method = method, ys = ys)
     elif method == "nibble":