diff --git a/recommend/model/operators.py b/recommend/model/operators.py
new file mode 100644
index 0000000..a508966
--- /dev/null
+++ b/recommend/model/operators.py
@@ -0,0 +1,52 @@
+import torch
+from typing import Optional, Tuple
+from torch import nn
+
+def broadcast(src: torch.Tensor, other: torch.Tensor, dim: int):
+    if dim < 0:
+        dim = other.dim() + dim
+    if src.dim() == 1:
+        for _ in range(0, dim):
+            src = src.unsqueeze(0)
+    for _ in range(src.dim(), other.dim()):
+        src = src.unsqueeze(-1)
+    src = src.expand(other.size())
+    return src
+
+def scatter_sum(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
+                out: Optional[torch.Tensor] = None,
+                dim_size: Optional[int] = None) -> torch.Tensor:
+    index = broadcast(index, src, dim)
+    if out is None:
+        size = list(src.size())
+        if dim_size is not None:
+            size[dim] = dim_size
+        elif index.numel() == 0:
+            size[dim] = 0
+        else:
+            size[dim] = int(index.max()) + 1
+        out = torch.zeros(size, dtype=src.dtype, device=src.device)
+        return out.scatter_add_(dim, index, src)
+    else:
+        return out.scatter_add_(dim, index, src)
+
+def scatter_add(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
+                out: Optional[torch.Tensor] = None,
+                dim_size: Optional[int] = None) -> torch.Tensor:
+    return scatter_sum(src, index, dim, out, dim_size)
+
+
+class EdgelistDrop(nn.Module):
+    def __init__(self):
+        super(EdgelistDrop, self).__init__()
+
+    def forward(self, edgeList, keep_rate, return_mask=False):
+        if keep_rate == 1.0:
+            return edgeList, torch.ones(edgeList.size(0)).type(torch.bool)
+        edgeNum = edgeList.size(0)
+        mask = (torch.rand(edgeNum) + keep_rate).floor().type(torch.bool)
+        newEdgeList = edgeList[mask, :]
+        if return_mask:
+            return newEdgeList, mask
+        else:
+            return newEdgeList