diff --git a/CHANGELOG.md b/CHANGELOG.md
index d6d64b24e..33d9d10a4 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -5,6 +5,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ## [Unreleased]
 ### Added
+- Added `pyg::sampler::hetero_neighbor_sample` implementation ([#90](https://github.com/pyg-team/pyg-lib/pull/90))
 - Added `pyg::utils::to_vector` implementation ([#88](https://github.com/pyg-team/pyg-lib/pull/88))
 - Added support for PyTorch 1.12 ([#57](https://github.com/pyg-team/pyg-lib/pull/57), [#58](https://github.com/pyg-team/pyg-lib/pull/58))
 - Added `grouped_matmul` and `segment_matmul` CUDA implementations via `cutlass` ([#51](https://github.com/pyg-team/pyg-lib/pull/51), [#56](https://github.com/pyg-team/pyg-lib/pull/56), [#61](https://github.com/pyg-team/pyg-lib/pull/61), [#64](https://github.com/pyg-team/pyg-lib/pull/64), [#69](https://github.com/pyg-team/pyg-lib/pull/69))
diff --git a/pyg_lib/csrc/sampler/cpu/neighbor_kernel.cpp b/pyg_lib/csrc/sampler/cpu/neighbor_kernel.cpp
index 23cacab72..ba0500d21 100644
--- a/pyg_lib/csrc/sampler/cpu/neighbor_kernel.cpp
+++ b/pyg_lib/csrc/sampler/cpu/neighbor_kernel.cpp
@@ -5,13 +5,16 @@
 #include "pyg_lib/csrc/sampler/cpu/mapper.h"
 #include "pyg_lib/csrc/sampler/subgraph.h"
 #include "pyg_lib/csrc/utils/cpu/convert.h"
+#include "pyg_lib/csrc/utils/types.h"
 
 namespace pyg {
 namespace sampler {
 
 namespace {
 
-// `node_t` is either a scalar or a pair of scalars of (example_id, node_id):
+// Helper classes for bipartite neighbor sampling //////////////////////////////
+
+// `node_t` is either a scalar or a pair of scalars (example_id, node_id):
 template <typename node_t,
           typename scalar_t,
           bool replace,
@@ -191,6 +194,8 @@ class NeighborSampler {
   std::vector<scalar_t> sampled_edge_ids_;
 };
 
+// Homogeneous neighbor sampling ///////////////////////////////////////////////
+
 template <bool replace, bool directed, bool disjoint, bool return_edge_id>
 std::tuple<at::Tensor, at::Tensor, at::Tensor, c10::optional<at::Tensor>>
 sample(const at::Tensor& rowptr,
@@ -312,6 +317,28 @@ neighbor_sample_kernel(const at::Tensor& rowptr,
     return sample<false, false, false, false>(rowptr, col, seed, count, time);
 }
 
+// Heterogeneous neighbor sampling ////////////////////////////////////////////
+
+std::tuple<c10::Dict<rel_t, at::Tensor>,
+           c10::Dict<rel_t, at::Tensor>,
+           c10::Dict<node_t, at::Tensor>,
+           c10::optional<c10::Dict<rel_t, at::Tensor>>>
+hetero_neighbor_sample_kernel(
+    const std::vector<node_t>& node_types,
+    const std::vector<edge_t>& edge_types,
+    const c10::Dict<rel_t, at::Tensor>& rowptr_dict,
+    const c10::Dict<rel_t, at::Tensor>& col_dict,
+    const c10::Dict<node_t, at::Tensor>& seed_dict,
+    const c10::Dict<rel_t, std::vector<int64_t>>& num_neighbors_dict,
+    const c10::optional<c10::Dict<node_t, at::Tensor>>& time_dict,
+    bool replace,
+    bool directed,
+    bool disjoint,
+    bool return_edge_id) {
+  std::cout << "hetero_neighbor_sample_kernel" << std::endl;
+  return std::make_tuple(col_dict, col_dict, col_dict, col_dict);
+}
+
 }  // namespace
 
 TORCH_LIBRARY_IMPL(pyg, CPU, m) {
@@ -319,5 +346,11 @@ TORCH_LIBRARY_IMPL(pyg, CPU, m) {
          TORCH_FN(neighbor_sample_kernel));
 }
 
+TORCH_LIBRARY_FRAGMENT(pyg, m) {
+  // TODO (matthias) fix automatic dispatching
+  m.def(TORCH_SELECTIVE_NAME("pyg::hetero_neighbor_sample_cpu"),
+        TORCH_FN(hetero_neighbor_sample_kernel));
+}
+
 }  // namespace sampler
 }  // namespace pyg
diff --git a/pyg_lib/csrc/sampler/neighbor.cpp b/pyg_lib/csrc/sampler/neighbor.cpp
index 5dda7407f..c2d647d80 100644
--- a/pyg_lib/csrc/sampler/neighbor.cpp
+++ b/pyg_lib/csrc/sampler/neighbor.cpp
@@ -31,11 +31,43 @@ neighbor_sample(const at::Tensor& rowptr,
                  disjoint, return_edge_id);
 }
 
+std::tuple<c10::Dict<rel_t, at::Tensor>,
+           c10::Dict<rel_t, at::Tensor>,
+           c10::Dict<node_t, at::Tensor>,
+           c10::optional<c10::Dict<rel_t, at::Tensor>>>
+hetero_neighbor_sample(
+    const std::vector<node_t>& node_types,
+    const std::vector<edge_t>& edge_types,
+    const c10::Dict<rel_t, at::Tensor>& rowptr_dict,
+    const c10::Dict<rel_t, at::Tensor>& col_dict,
+    const c10::Dict<node_t, at::Tensor>& seed_dict,
+    const c10::Dict<rel_t, std::vector<int64_t>>& num_neighbors_dict,
+    const c10::optional<c10::Dict<node_t, at::Tensor>>& time_dict,
+    bool replace,
+    bool directed,
+    bool disjoint,
+    bool return_edge_id) {
+  // TODO (matthias) Add TensorArg definitions and type checks.
+  static auto op = c10::Dispatcher::singleton()
+                       .findSchemaOrThrow("pyg::hetero_neighbor_sample_cpu", "")
+                       .typed<decltype(hetero_neighbor_sample)>();
+  return op.call(node_types, edge_types, rowptr_dict, col_dict, seed_dict,
+                 num_neighbors_dict, time_dict, replace, directed, disjoint,
+                 return_edge_id);
+}
+
 TORCH_LIBRARY_FRAGMENT(pyg, m) {
   m.def(TORCH_SELECTIVE_SCHEMA(
       "pyg::neighbor_sample(Tensor rowptr, Tensor col, Tensor seed, int[] "
       "num_neighbors, Tensor? time, bool replace, bool directed, bool "
       "disjoint, bool return_edge_id) -> (Tensor, Tensor, Tensor, Tensor?)"));
+  m.def(TORCH_SELECTIVE_SCHEMA(
+      "pyg::hetero_neighbor_sample(str[] node_types, (str, str, str)[] "
+      "edge_types, Dict(str, Tensor) rowptr_dict, Dict(str, Tensor) col_dict, "
+      "Dict(str, Tensor) seed_dict, Dict(str, int[]) num_neighbors_dict, "
+      "Dict(str, Tensor)? time_dict, bool replace, bool directed, bool "
+      "disjoint, bool return_edge_id) -> (Dict(str, Tensor), Dict(str, "
+      "Tensor), Dict(str, Tensor), Dict(str, Tensor)?)"));
 }
 
 }  // namespace sampler
diff --git a/pyg_lib/csrc/sampler/neighbor.h b/pyg_lib/csrc/sampler/neighbor.h
index 68c27caec..f0e9aa7f1 100644
--- a/pyg_lib/csrc/sampler/neighbor.h
+++ b/pyg_lib/csrc/sampler/neighbor.h
@@ -2,6 +2,7 @@
 
 #include <ATen/ATen.h>
 #include "pyg_lib/csrc/macros.h"
+#include "pyg_lib/csrc/utils/types.h"
 
 namespace pyg {
 namespace sampler {
@@ -21,5 +22,27 @@ neighbor_sample(const at::Tensor& rowptr,
                 bool disjoint = false,
                 bool return_edge_id = true);
 
+// Recursively samples neighbors from all node indices in `seed_dict`
+// in the heterogeneous graph given by `(rowptr_dict, col_dict)`.
+// Returns: (row_dict, col_dict, node_id_dict, edge_id_dict)
+PYG_API
+std::tuple<c10::Dict<rel_t, at::Tensor>,
+           c10::Dict<rel_t, at::Tensor>,
+           c10::Dict<node_t, at::Tensor>,
+           c10::optional<c10::Dict<rel_t, at::Tensor>>>
+hetero_neighbor_sample(
+    const std::vector<node_t>& node_types,
+    const std::vector<edge_t>& edge_types,
+    const c10::Dict<rel_t, at::Tensor>& rowptr_dict,
+    const c10::Dict<rel_t, at::Tensor>& col_dict,
+    const c10::Dict<node_t, at::Tensor>& seed_dict,
+    const c10::Dict<rel_t, std::vector<int64_t>>& num_neighbors_dict,
+    const c10::optional<c10::Dict<node_t, at::Tensor>>& time_dict =
+        c10::nullopt,
+    bool replace = false,
+    bool directed = true,
+    bool disjoint = false,
+    bool return_edge_id = true);
+
 }  // namespace sampler
 }  // namespace pyg
diff --git a/pyg_lib/csrc/utils/types.h b/pyg_lib/csrc/utils/types.h
new file mode 100644
index 000000000..159f4daef
--- /dev/null
+++ b/pyg_lib/csrc/utils/types.h
@@ -0,0 +1,8 @@
+#pragma once
+
+#include <string>
+#include <tuple>
+
+typedef std::string node_t;
+typedef std::string rel_t;
+typedef std::tuple<std::string, std::string, std::string> edge_t;
diff --git a/pyg_lib/sampler/__init__.py b/pyg_lib/sampler/__init__.py
index 7890c6428..61c7fb5cc 100644
--- a/pyg_lib/sampler/__init__.py
+++ b/pyg_lib/sampler/__init__.py
@@ -1,8 +1,12 @@
-from typing import List, Tuple, Optional
+from typing import List, Tuple, Optional, Dict
 
 import torch
 from torch import Tensor
 
+NodeType = str
+RelType = str
+EdgeType = Tuple[str, str, str]
+
 
 def neighbor_sample(
     rowptr: Tensor,
@@ -51,6 +55,70 @@ def neighbor_sample(
                                          return_edge_id)
 
 
+def hetero_neighbor_sample(
+    rowptr_dict: Dict[EdgeType, Tensor],
+    col_dict: Dict[EdgeType, Tensor],
+    seed_dict: Dict[NodeType, Tensor],
+    num_neighbors_dict: Dict[EdgeType, List[int]],
+    time_dict: Optional[Dict[NodeType, Tensor]] = None,
+    replace: bool = False,
+    directed: bool = True,
+    disjoint: bool = False,
+    return_edge_id: bool = True,
+) -> Tuple[Dict[EdgeType, Tensor], Dict[EdgeType, Tensor], Dict[
+        NodeType, Tensor], Optional[Dict[EdgeType, Tensor]]]:
+    r"""Recursively samples neighbors from all node indices in :obj:`seed_dict`
+    in the heterogeneous graph given by :obj:`(rowptr_dict, col_dict)`.
+
+    .. note ::
+        Similar to :meth:`neighbor_sample`, but expects a dictionary of node
+        types (:obj:`str`) and  edge tpyes (:obj:`Tuple[str, str, str]`) for
+        each non-boolean argument.
+
+    Args:
+        kwargs: Arguments of :meth:`neighbor_sample`.
+    """
+    src_node_types = {k[0] for k in rowptr_dict.keys()}
+    dst_node_types = {k[-1] for k in rowptr_dict.keys()}
+    node_types = list(src_node_types | dst_node_types)
+    edge_types = list(rowptr_dict.keys())
+
+    TO_REL_TYPE = {key: '__'.join(key) for key in edge_types}
+    TO_EDGE_TYPE = {'__'.join(key): key for key in edge_types}
+
+    rowptr_dict = {TO_REL_TYPE[k]: v for k, v in rowptr_dict.items()}
+    col_dict = {TO_REL_TYPE[k]: v for k, v in col_dict.items()}
+    num_neighbors_dict = {
+        TO_REL_TYPE[k]: v
+        for k, v in num_neighbors_dict.items()
+    }
+
+    out = torch.ops.pyg.hetero_neighbor_sample(
+        node_types,
+        edge_types,
+        rowptr_dict,
+        col_dict,
+        seed_dict,
+        num_neighbors_dict,
+        time_dict,
+        replace,
+        directed,
+        disjoint,
+        return_edge_id,
+    )
+
+    out_row_dict, out_col_dict, out_node_id_dict, out_edge_id_dict = out
+    out_row_dict = {TO_EDGE_TYPE[k]: v for k, v in out_row_dict.items()}
+    out_col_dict = {TO_EDGE_TYPE[k]: v for k, v in out_col_dict.items()}
+    if out_edge_id_dict is not None:
+        out_edge_id_dict = {
+            TO_EDGE_TYPE[k]: v
+            for k, v in out_edge_id_dict.items()
+        }
+
+    return out_row_dict, out_col_dict, out_node_id_dict, out_edge_id_dict
+
+
 def subgraph(
     rowptr: Tensor,
     col: Tensor,
@@ -103,6 +171,7 @@ def random_walk(rowptr: Tensor, col: Tensor, seed: Tensor, walk_length: int,
 
 __all__ = [
     'neighbor_sample',
+    'hetero_neighbor_sample',
     'subgraph',
     'random_walk',
 ]
diff --git a/test/csrc/sampler/test_neighbor.cpp b/test/csrc/sampler/test_neighbor.cpp
index 065cee604..f9d3a6978 100644
--- a/test/csrc/sampler/test_neighbor.cpp
+++ b/test/csrc/sampler/test_neighbor.cpp
@@ -2,6 +2,7 @@
 #include <gtest/gtest.h>
 
 #include "pyg_lib/csrc/sampler/neighbor.h"
+#include "pyg_lib/csrc/utils/types.h"
 #include "test/csrc/graph.h"
 
 TEST(NeighborTest, BasicAssertions) {
@@ -25,7 +26,7 @@ TEST(NeighborTest, BasicAssertions) {
   EXPECT_TRUE(at::equal(std::get<3>(out).value(), expected_edges));
 }
 
-TEST(NeighborDisjointTest, BasicAssertions) {
+TEST(DisjointNeighborTest, BasicAssertions) {
   auto options = at::TensorOptions().dtype(at::kLong);
 
   auto graph = cycle_graph(/*num_nodes=*/6, options);
@@ -48,3 +49,24 @@ TEST(NeighborDisjointTest, BasicAssertions) {
       at::tensor({4, 5, 6, 7, 2, 3, 6, 7, 4, 5, 8, 9}, options);
   EXPECT_TRUE(at::equal(std::get<3>(out).value(), expected_edges));
 }
+
+TEST(HeteroNeighborTest, BasicAssertions) {
+  auto options = at::TensorOptions().dtype(at::kLong);
+
+  auto graph = cycle_graph(/*num_nodes=*/6, options);
+  std::vector<node_t> node_types = {"paper"};
+  std::vector<edge_t> edge_types = {{"paper", "to", "paper"}};
+  c10::Dict<rel_t, at::Tensor> rowptr_dict;
+  rowptr_dict.insert("paper__to__paper", std::get<0>(graph));
+  c10::Dict<rel_t, at::Tensor> col_dict;
+  col_dict.insert("paper__to__paper", std::get<1>(graph));
+  c10::Dict<node_t, at::Tensor> seed_dict;
+  seed_dict.insert("paper", at::arange(2, 4, options));
+  std::vector<int64_t> num_neighbors = {2, 2};
+  c10::Dict<rel_t, std::vector<int64_t>> num_neighbors_dict;
+  num_neighbors_dict.insert("paper__to__paper", num_neighbors);
+
+  auto out = pyg::sampler::hetero_neighbor_sample(
+      node_types, edge_types, rowptr_dict, col_dict, seed_dict,
+      num_neighbors_dict);
+}