added normalized cut implementation

test/test_graclus.py

-import pytest
 import torch
 from torch_cluster import graclus_cluster
 
@@ -7,5 +6,6 @@ def test_graclus():
     edge_index = torch.LongTensor([[0, 0, 0, 1, 2, 3, 3, 3, 4, 5, 5, 5, 6, 6],
                                    [2, 3, 6, 5, 0, 0, 4, 5, 3, 1, 3, 6, 0, 3]])
     edge_attr = torch.Tensor([2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2])
+    rid = torch.LongTensor([0, 1, 2, 3, 4, 5, 6])
 
-    graclus_cluster(edge_index, edge_attr)
+    graclus_cluster(edge_index, edge_attr=edge_attr, rid=rid)

torch_cluster/functions/degree.py

+import torch
+
+
+def node_degree(edge_index, num_nodes, out=None):
+    zero = torch.zeros(num_nodes, out=out)
+    one = torch.ones(edge_index.size(1), out=zero.new())
+    return zero.scatter_add_(0, edge_index[0], one)

torch_cluster/functions/graclus.py

-def graclus_cluster(edge_index, edge_attr=None, batch=None):
-    pass
+from __future__ import division
+
+import torch
+
+from .degree import node_degree
+
+
+def graclus_cluster(edge_index,
+                    num_nodes=None,
+                    edge_attr=None,
+                    batch=None,
+                    rid=None):
+
+    num_nodes = edge_index.max() + 1 if num_nodes is None else num_nodes
+    rid = torch.randperm(num_nodes) if rid is None else rid
+
+    cut = normalized_cut(edge_index, num_nodes, edge_attr)
+
+    print(cut)
+
+
+def normalized_cut(edge_index, num_nodes, edge_attr=None):
+    row, col = edge_index
+
+    out = edge_attr.new() if edge_attr is not None else torch.Tensor()
+    cut = node_degree(edge_index, num_nodes, out=out)
+    cut = 1 / cut
+    cut = cut[row] + cut[col]
+
+    if edge_attr is None:
+        return cut
+    else:
+        if edge_attr.dim() > 1 and edge_attr.size(1) > 1:
+            edge_attr = torch.norm(edge_attr, 2, 1)
+        return edge_attr.squeeze() * cut