gnn support weight sampling neighbors.

mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/engine/gnn/bindings.cc

@@ -65,9 +65,9 @@ PYBIND_REGISTER(
            })
       .def("get_sampled_neighbors",
            [](gnn::GraphData &g, std::vector<gnn::NodeIdType> node_list, std::vector<gnn::NodeIdType> neighbor_nums,
-              std::vector<gnn::NodeType> neighbor_types) {
+              std::vector<gnn::NodeType> neighbor_types, SamplingStrategy strategy) {
              std::shared_ptr<Tensor> out;
-             THROW_IF_ERROR(g.GetSampledNeighbors(node_list, neighbor_nums, neighbor_types, &out));
+             THROW_IF_ERROR(g.GetSampledNeighbors(node_list, neighbor_nums, neighbor_types, strategy, &out));
              return out;
            })
       .def("get_neg_sampled_neighbors",
@@ -114,8 +114,15 @@ PYBIND_REGISTER(
         return out;
       }))
       .def("stop", [](gnn::GraphDataServer &g) { THROW_IF_ERROR(g.Stop()); })
-      .def("is_stoped", [](gnn::GraphDataServer &g) { return g.IsStoped(); });
+      .def("is_stopped", [](gnn::GraphDataServer &g) { return g.IsStopped(); });
   }));
 
+PYBIND_REGISTER(SamplingStrategy, 0, ([](const py::module *m) {
+                  (void)py::enum_<SamplingStrategy>(*m, "SamplingStrategy", py::arithmetic())
+                    .value("DE_SAMPLING_RANDOM", SamplingStrategy::kRandom)
+                    .value("DE_SAMPLING_EDGE_WEIGHT", SamplingStrategy::kEdgeWeight)
+                    .export_values();
+                }));
+
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/core/constants.h

@@ -71,6 +71,9 @@ enum class NormalizeForm {
   kNfkd,
 };
 
+// Possible values for SamplingStrategy
+enum class SamplingStrategy { kRandom = 0, kEdgeWeight = 1 };
+
 // convenience functions for 32bit int bitmask
 inline bool BitTest(uint32_t bits, uint32_t bitMask) { return (bits & bitMask) == bitMask; }
 

mindspore/ccsrc/minddata/dataset/engine/gnn/edge.h

@@ -35,10 +35,11 @@ class Edge {
   // Constructor
   // @param EdgeIdType id - edge id
   // @param EdgeType type - edge type
+  // @param WeightType weight - edge weight
   // @param std::shared_ptr<Node> src_node - source node
   // @param std::shared_ptr<Node> dst_node - destination node
-  Edge(EdgeIdType id, EdgeType type, std::shared_ptr<Node> src_node, std::shared_ptr<Node> dst_node)
-      : id_(id), type_(type), src_node_(src_node), dst_node_(dst_node) {}
+  Edge(EdgeIdType id, EdgeType type, WeightType weight, std::shared_ptr<Node> src_node, std::shared_ptr<Node> dst_node)
+      : id_(id), type_(type), weight_(weight), src_node_(src_node), dst_node_(dst_node) {}
 
   virtual ~Edge() = default;
 
@@ -48,6 +49,9 @@ class Edge {
   // @return NodeIdType - Returned edge type
   EdgeType type() const { return type_; }
 
+  // @return WeightType - Returned edge weight
+  WeightType weight() const { return weight_; }
+
   // Get the feature of a edge
   // @param FeatureType feature_type - type of feature
   // @param std::shared_ptr<Feature> *out_feature - Returned feature
@@ -77,6 +81,7 @@ class Edge {
  protected:
   EdgeIdType id_;
   EdgeType type_;
+  WeightType weight_;
   std::shared_ptr<Node> src_node_;
   std::shared_ptr<Node> dst_node_;
 };

mindspore/ccsrc/minddata/dataset/engine/gnn/gnn_graph_data.proto

@@ -71,6 +71,7 @@ message GnnGraphDataRequestPb {
   repeated int32 number = 4; // samples number
   TensorPb id_tensor = 5; // input ids ,node id or edge id
   GnnRandomWalkPb random_walk = 6;
+  int32 strategy = 7;
 }
 
 message GnnGraphDataResponsePb {

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_data.h

@@ -76,11 +76,13 @@ class GraphData {
   // @param std::vector<NodeType> node_list - List of nodes
   // @param std::vector<NodeIdType> neighbor_nums - Number of neighbors sampled per hop
   // @param std::vector<NodeType> neighbor_types - Neighbor type sampled per hop
+  // @param std::SamplingStrategy strategy - Sampling strategy
   // @param std::shared_ptr<Tensor> *out - Returned neighbor's id.
   // @return Status The status code returned
   virtual Status GetSampledNeighbors(const std::vector<NodeIdType> &node_list,
                                      const std::vector<NodeIdType> &neighbor_nums,
-                                     const std::vector<NodeType> &neighbor_types, std::shared_ptr<Tensor> *out) = 0;
+                                     const std::vector<NodeType> &neighbor_types, SamplingStrategy strategy,
+                                     std::shared_ptr<Tensor> *out) = 0;
 
   // Get negative sampled neighbors.
   // @param std::vector<NodeType> node_list - List of nodes
@@ -95,7 +97,7 @@ class GraphData {
   // @param std::vector<NodeIdType> node_list - List of nodes
   // @param std::vector<NodeType> meta_path - node type of each step
   // @param float step_home_param - return hyper parameter in node2vec algorithm
-  // @param float step_away_param - inout hyper parameter in node2vec algorithm
+  // @param float step_away_param - in out hyper parameter in node2vec algorithm
   // @param NodeIdType default_node - default node id
   // @param std::shared_ptr<Tensor> *out - Returned nodes id in walk path
   // @return Status The status code returned

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_data_client.cc

@@ -137,7 +137,8 @@ Status GraphDataClient::GetAllNeighbors(const std::vector<NodeIdType> &node_list
 
 Status GraphDataClient::GetSampledNeighbors(const std::vector<NodeIdType> &node_list,
                                             const std::vector<NodeIdType> &neighbor_nums,
-                                            const std::vector<NodeType> &neighbor_types, std::shared_ptr<Tensor> *out) {
+                                            const std::vector<NodeType> &neighbor_types, SamplingStrategy strategy,
+                                            std::shared_ptr<Tensor> *out) {
 #if !defined(_WIN32) && !defined(_WIN64)
   GnnGraphDataRequestPb request;
   GnnGraphDataResponsePb response;
@@ -151,6 +152,7 @@ Status GraphDataClient::GetSampledNeighbors(const std::vector<NodeIdType> &node_
   for (const auto &type : neighbor_types) {
     request.add_type(static_cast<google::protobuf::int32>(type));
   }
+  request.set_strategy(static_cast<google::protobuf::int32>(strategy));
   RETURN_IF_NOT_OK(GetGraphDataTensor(request, &response, out));
 #endif
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_data_client.h

@@ -86,10 +86,12 @@ class GraphDataClient : public GraphData {
   // @param std::vector<NodeType> node_list - List of nodes
   // @param std::vector<NodeIdType> neighbor_nums - Number of neighbors sampled per hop
   // @param std::vector<NodeType> neighbor_types - Neighbor type sampled per hop
+  // @param std::SamplingStrategy strategy - Sampling strategy
   // @param std::shared_ptr<Tensor> *out - Returned neighbor's id.
   // @return Status The status code returned
   Status GetSampledNeighbors(const std::vector<NodeIdType> &node_list, const std::vector<NodeIdType> &neighbor_nums,
-                             const std::vector<NodeType> &neighbor_types, std::shared_ptr<Tensor> *out) override;
+                             const std::vector<NodeType> &neighbor_types, SamplingStrategy strategy,
+                             std::shared_ptr<Tensor> *out) override;
 
   // Get negative sampled neighbors.
   // @param std::vector<NodeType> node_list - List of nodes
@@ -104,7 +106,7 @@ class GraphDataClient : public GraphData {
   // @param std::vector<NodeIdType> node_list - List of nodes
   // @param std::vector<NodeType> meta_path - node type of each step
   // @param float step_home_param - return hyper parameter in node2vec algorithm
-  // @param float step_away_param - inout hyper parameter in node2vec algorithm
+  // @param float step_away_param - in out hyper parameter in node2vec algorithm
   // @param NodeIdType default_node - default node id
   // @param std::shared_ptr<Tensor> *out - Returned nodes id in walk path
   // @return Status The status code returned

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_data_impl.cc

@@ -171,7 +171,8 @@ Status GraphDataImpl::CheckNeighborType(NodeType neighbor_type) {
 
 Status GraphDataImpl::GetSampledNeighbors(const std::vector<NodeIdType> &node_list,
                                           const std::vector<NodeIdType> &neighbor_nums,
-                                          const std::vector<NodeType> &neighbor_types, std::shared_ptr<Tensor> *out) {
+                                          const std::vector<NodeType> &neighbor_types, SamplingStrategy strategy,
+                                          std::shared_ptr<Tensor> *out) {
   CHECK_FAIL_RETURN_UNEXPECTED(!node_list.empty(), "Input node_list is empty.");
   CHECK_FAIL_RETURN_UNEXPECTED(neighbor_nums.size() == neighbor_types.size(),
                                "The sizes of neighbor_nums and neighbor_types are inconsistent.");
@@ -199,7 +200,7 @@ Status GraphDataImpl::GetSampledNeighbors(const std::vector<NodeIdType> &node_li
           std::shared_ptr<Node> node;
           RETURN_IF_NOT_OK(GetNodeByNodeId(node_id, &node));
           std::vector<NodeIdType> out;
-          RETURN_IF_NOT_OK(node->GetSampledNeighbors(neighbor_types[i], neighbor_nums[i], &out));
+          RETURN_IF_NOT_OK(node->GetSampledNeighbors(neighbor_types[i], neighbor_nums[i], strategy, &out));
           neighbors.insert(neighbors.end(), out.begin(), out.end());
         }
       }

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_data_impl.h

@@ -80,10 +80,12 @@ class GraphDataImpl : public GraphData {
   // @param std::vector<NodeType> node_list - List of nodes
   // @param std::vector<NodeIdType> neighbor_nums - Number of neighbors sampled per hop
   // @param std::vector<NodeType> neighbor_types - Neighbor type sampled per hop
+  // @param std::SamplingStrategy strategy - Sampling strategy
   // @param std::shared_ptr<Tensor> *out - Returned neighbor's id.
   // @return Status The status code returned
   Status GetSampledNeighbors(const std::vector<NodeIdType> &node_list, const std::vector<NodeIdType> &neighbor_nums,
-                             const std::vector<NodeType> &neighbor_types, std::shared_ptr<Tensor> *out) override;
+                             const std::vector<NodeType> &neighbor_types, SamplingStrategy strategy,
+                             std::shared_ptr<Tensor> *out) override;
 
   // Get negative sampled neighbors.
   // @param std::vector<NodeType> node_list - List of nodes
@@ -98,7 +100,7 @@ class GraphDataImpl : public GraphData {
   // @param std::vector<NodeIdType> node_list - List of nodes
   // @param std::vector<NodeType> meta_path - node type of each step
   // @param float step_home_param - return hyper parameter in node2vec algorithm
-  // @param float step_away_param - inout hyper parameter in node2vec algorithm
+  // @param float step_away_param - in out hyper parameter in node2vec algorithm
   // @param NodeIdType default_node - default node id
   // @param std::shared_ptr<Tensor> *out - Returned nodes id in walk path
   // @return Status The status code returned
@@ -194,7 +196,7 @@ class GraphDataImpl : public GraphData {
     std::vector<NodeIdType> node_list_;
     std::vector<NodeType> meta_path_;
     float step_home_param_;  // Return hyper parameter. Default is 1.0
-    float step_away_param_;  // Inout hyper parameter. Default is 1.0
+    float step_away_param_;  // In out hyper parameter. Default is 1.0
     NodeIdType default_node_;
 
     int32_t num_walks_;    // Number of walks per source. Default is 1

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_data_server.h

@@ -50,7 +50,7 @@ class GraphDataServer {
 
   enum ServerState state() { return state_; }
 
-  bool IsStoped() {
+  bool IsStopped() {
     if (state_ == kGdsStopped) {
       return true;
     } else {

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_data_service_impl.cc

@@ -78,7 +78,7 @@ grpc::Status GraphDataServiceImpl::ClientRegister(grpc::ServerContext *context,
         }
         break;
       case GraphDataServer::kGdsStopped:
-        response->set_error_msg("Stoped");
+        response->set_error_msg("Stopped");
         break;
     }
   } else {
@@ -222,8 +222,9 @@ Status GraphDataServiceImpl::GetSampledNeighbors(const GnnGraphDataRequestPb *re
   neighbor_types.resize(request->type().size());
   std::transform(request->type().begin(), request->type().end(), neighbor_types.begin(),
                  [](const google::protobuf::int32 type) { return static_cast<NodeType>(type); });
+  SamplingStrategy strategy = static_cast<SamplingStrategy>(request->strategy());
   std::shared_ptr<Tensor> tensor;
-  RETURN_IF_NOT_OK(graph_data_impl_->GetSampledNeighbors(node_list, neighbor_nums, neighbor_types, &tensor));
+  RETURN_IF_NOT_OK(graph_data_impl_->GetSampledNeighbors(node_list, neighbor_nums, neighbor_types, strategy, &tensor));
   TensorPb *result = response->add_result_data();
   RETURN_IF_NOT_OK(TensorToPb(tensor, result));
   return Status::OK();

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_loader.cc

@@ -39,7 +39,9 @@ GraphLoader::GraphLoader(GraphDataImpl *graph_impl, std::string mr_filepath, int
       row_id_(0),
       shard_reader_(nullptr),
       graph_feature_parser_(nullptr),
-      keys_({"first_id", "second_id", "third_id", "attribute", "type", "node_feature_index", "edge_feature_index"}) {}
+      required_key_(
+        {"first_id", "second_id", "third_id", "attribute", "type", "node_feature_index", "edge_feature_index"}),
+      optional_key_({{"weight", false}}) {}
 
 Status GraphLoader::GetNodesAndEdges() {
   NodeIdMap *n_id_map = &graph_impl_->node_id_map_;
@@ -62,7 +64,7 @@ Status GraphLoader::GetNodesAndEdges() {
       CHECK_FAIL_RETURN_UNEXPECTED(src_itr != n_id_map->end(), "invalid src_id:" + std::to_string(src_itr->first));
       CHECK_FAIL_RETURN_UNEXPECTED(dst_itr != n_id_map->end(), "invalid src_id:" + std::to_string(dst_itr->first));
       RETURN_IF_NOT_OK(edge_ptr->SetNode({src_itr->second, dst_itr->second}));
-      RETURN_IF_NOT_OK(src_itr->second->AddNeighbor(dst_itr->second));
+      RETURN_IF_NOT_OK(src_itr->second->AddNeighbor(dst_itr->second, edge_ptr->weight()));
       e_id_map->insert({edge_ptr->id(), edge_ptr});  // add edge to edge_id_map_
       graph_impl_->edge_type_map_[edge_ptr->type()].push_back(edge_ptr->id());
       dq.pop_front();
@@ -95,12 +97,18 @@ Status GraphLoader::InitAndLoad() {
 
   graph_impl_->data_schema_ = (shard_reader_->GetShardHeader()->GetSchemas()[0]->GetSchema());
   mindrecord::json schema = graph_impl_->data_schema_["schema"];
-  for (const std::string &key : keys_) {
+  for (const std::string &key : required_key_) {
     if (schema.find(key) == schema.end()) {
       RETURN_STATUS_UNEXPECTED(key + ":doesn't exist in schema:" + schema.dump());
     }
   }
 
+  for (auto op_key : optional_key_) {
+    if (schema.find(op_key.first) != schema.end()) {
+      optional_key_[op_key.first] = true;
+    }
+  }
+
   if (graph_impl_->server_mode_) {
 #if !defined(_WIN32) && !defined(_WIN64)
     int64_t total_blob_size = 0;
@@ -128,7 +136,11 @@ Status GraphLoader::LoadNode(const std::vector<uint8_t> &col_blob, const mindrec
                              DefaultNodeFeatureMap *default_feature) {
   NodeIdType node_id = col_jsn["first_id"];
   NodeType node_type = static_cast<NodeType>(col_jsn["type"]);
-  (*node) = std::make_shared<LocalNode>(node_id, node_type);
+  WeightType weight = 1;
+  if (optional_key_["weight"]) {
+    weight = col_jsn["weight"];
+  }
+  (*node) = std::make_shared<LocalNode>(node_id, node_type, weight);
   std::vector<int32_t> indices;
   RETURN_IF_NOT_OK(graph_feature_parser_->LoadFeatureIndex("node_feature_index", col_blob, &indices));
   if (graph_impl_->server_mode_) {
@@ -174,9 +186,13 @@ Status GraphLoader::LoadEdge(const std::vector<uint8_t> &col_blob, const mindrec
   EdgeIdType edge_id = col_jsn["first_id"];
   EdgeType edge_type = static_cast<EdgeType>(col_jsn["type"]);
   NodeIdType src_id = col_jsn["second_id"], dst_id = col_jsn["third_id"];
-  std::shared_ptr<Node> src = std::make_shared<LocalNode>(src_id, -1);
-  std::shared_ptr<Node> dst = std::make_shared<LocalNode>(dst_id, -1);
-  (*edge) = std::make_shared<LocalEdge>(edge_id, edge_type, src, dst);
+  WeightType edge_weight = 1;
+  if (optional_key_["weight"]) {
+    edge_weight = col_jsn["weight"];
+  }
+  std::shared_ptr<Node> src = std::make_shared<LocalNode>(src_id, -1, 1);
+  std::shared_ptr<Node> dst = std::make_shared<LocalNode>(dst_id, -1, 1);
+  (*edge) = std::make_shared<LocalEdge>(edge_id, edge_type, edge_weight, src, dst);
   std::vector<int32_t> indices;
   RETURN_IF_NOT_OK(graph_feature_parser_->LoadFeatureIndex("edge_feature_index", col_blob, &indices));
   if (graph_impl_->server_mode_) {

mindspore/ccsrc/minddata/dataset/engine/gnn/graph_loader.h

@@ -110,7 +110,8 @@ class GraphLoader {
   std::vector<EdgeFeatureMap> e_feature_maps_;
   std::vector<DefaultNodeFeatureMap> default_node_feature_maps_;
   std::vector<DefaultEdgeFeatureMap> default_edge_feature_maps_;
-  const std::vector<std::string> keys_;
+  const std::vector<std::string> required_key_;
+  std::unordered_map<std::string, bool> optional_key_;
 };
 }  // namespace gnn
 }  // namespace dataset

mindspore/ccsrc/minddata/dataset/engine/gnn/local_edge.cc

@@ -21,8 +21,9 @@ namespace mindspore {
 namespace dataset {
 namespace gnn {
 
-LocalEdge::LocalEdge(EdgeIdType id, EdgeType type, std::shared_ptr<Node> src_node, std::shared_ptr<Node> dst_node)
-    : Edge(id, type, src_node, dst_node) {}
+LocalEdge::LocalEdge(EdgeIdType id, EdgeType type, WeightType weight, std::shared_ptr<Node> src_node,
+                     std::shared_ptr<Node> dst_node)
+    : Edge(id, type, weight, src_node, dst_node) {}
 
 Status LocalEdge::GetFeatures(FeatureType feature_type, std::shared_ptr<Feature> *out_feature) {
   auto itr = features_.find(feature_type);

mindspore/ccsrc/minddata/dataset/engine/gnn/local_edge.h

@@ -34,9 +34,11 @@ class LocalEdge : public Edge {
   // Constructor
   // @param EdgeIdType id - edge id
   // @param EdgeType type - edge type
+  // @param WeightType weight - edge weight
   // @param std::shared_ptr<Node> src_node - source node
   // @param std::shared_ptr<Node> dst_node - destination node
-  LocalEdge(EdgeIdType id, EdgeType type, std::shared_ptr<Node> src_node, std::shared_ptr<Node> dst_node);
+  LocalEdge(EdgeIdType id, EdgeType type, WeightType weight, std::shared_ptr<Node> src_node,
+            std::shared_ptr<Node> dst_node);
 
   ~LocalEdge() = default;
 

mindspore/ccsrc/minddata/dataset/engine/gnn/local_node.cc

@@ -16,6 +16,7 @@
 #include "minddata/dataset/engine/gnn/local_node.h"
 
 #include <algorithm>
+#include <random>
 #include <string>
 #include <utility>
 
@@ -26,7 +27,10 @@ namespace mindspore {
 namespace dataset {
 namespace gnn {
 
-LocalNode::LocalNode(NodeIdType id, NodeType type) : Node(id, type), rnd_(GetRandomDevice()) { rnd_.seed(GetSeed()); }
+LocalNode::LocalNode(NodeIdType id, NodeType type, WeightType weight)
+    : Node(id, type, weight), rnd_(GetRandomDevice()) {
+  rnd_.seed(GetSeed());
+}
 
 Status LocalNode::GetFeatures(FeatureType feature_type, std::shared_ptr<Feature> *out_feature) {
   auto itr = features_.find(feature_type);
@@ -44,13 +48,13 @@ Status LocalNode::GetAllNeighbors(NodeType neighbor_type, std::vector<NodeIdType
   auto itr = neighbor_nodes_.find(neighbor_type);
   if (itr != neighbor_nodes_.end()) {
     if (exclude_itself) {
-      neighbors.resize(itr->second.size());
-      std::transform(itr->second.begin(), itr->second.end(), neighbors.begin(),
+      neighbors.resize(itr->second.first.size());
+      std::transform(itr->second.first.begin(), itr->second.first.end(), neighbors.begin(),
                      [](const std::shared_ptr<Node> node) { return node->id(); });
     } else {
-      neighbors.resize(itr->second.size() + 1);
+      neighbors.resize(itr->second.first.size() + 1);
       neighbors[0] = id_;
-      std::transform(itr->second.begin(), itr->second.end(), neighbors.begin() + 1,
+      std::transform(itr->second.first.begin(), itr->second.first.end(), neighbors.begin() + 1,
                      [](const std::shared_ptr<Node> node) { return node->id(); });
     }
   } else {
@@ -63,8 +67,8 @@ Status LocalNode::GetAllNeighbors(NodeType neighbor_type, std::vector<NodeIdType
   return Status::OK();
 }
 
-Status LocalNode::GetSampledNeighbors(const std::vector<std::shared_ptr<Node>> &neighbors, int32_t samples_num,
-                                      std::vector<NodeIdType> *out) {
+Status LocalNode::GetRandomSampledNeighbors(const std::vector<std::shared_ptr<Node>> &neighbors, int32_t samples_num,
+                                            std::vector<NodeIdType> *out) {
   std::vector<NodeIdType> shuffled_id(neighbors.size());
   std::iota(shuffled_id.begin(), shuffled_id.end(), 0);
   std::shuffle(shuffled_id.begin(), shuffled_id.end(), rnd_);
@@ -75,14 +79,33 @@ Status LocalNode::GetSampledNeighbors(const std::vector<std::shared_ptr<Node>> &
   return Status::OK();
 }
 
-Status LocalNode::GetSampledNeighbors(NodeType neighbor_type, int32_t samples_num,
+Status LocalNode::GetWeightSampledNeighbors(const std::vector<std::shared_ptr<Node>> &neighbors,
+                                            const std::vector<WeightType> &weights, int32_t samples_num,
+                                            std::vector<NodeIdType> *out) {
+  CHECK_FAIL_RETURN_UNEXPECTED(neighbors.size() == weights.size(),
+                               "The number of neighbors does not match the weight.");
+  std::discrete_distribution<NodeIdType> discrete_dist(weights.begin(), weights.end());
+  for (int32_t i = 0; i < samples_num; ++i) {
+    NodeIdType index = discrete_dist(rnd_);
+    out->emplace_back(neighbors[index]->id());
+  }
+  return Status::OK();
+}
+
+Status LocalNode::GetSampledNeighbors(NodeType neighbor_type, int32_t samples_num, SamplingStrategy strategy,
                                       std::vector<NodeIdType> *out_neighbors) {
   std::vector<NodeIdType> neighbors;
   neighbors.reserve(samples_num);
   auto itr = neighbor_nodes_.find(neighbor_type);
   if (itr != neighbor_nodes_.end()) {
-    while (neighbors.size() < samples_num) {
-      RETURN_IF_NOT_OK(GetSampledNeighbors(itr->second, samples_num - neighbors.size(), &neighbors));
+    if (strategy == SamplingStrategy::kRandom) {
+      while (neighbors.size() < samples_num) {
+        RETURN_IF_NOT_OK(GetRandomSampledNeighbors(itr->second.first, samples_num - neighbors.size(), &neighbors));
+      }
+    } else if (strategy == SamplingStrategy::kEdgeWeight) {
+      RETURN_IF_NOT_OK(GetWeightSampledNeighbors(itr->second.first, itr->second.second, samples_num, &neighbors));
+    } else {
+      RETURN_STATUS_UNEXPECTED("Invalid strategy");
     }
   } else {
     MS_LOG(DEBUG) << "There are no neighbors. node_id:" << id_ << " neighbor_type:" << neighbor_type;
@@ -95,12 +118,15 @@ Status LocalNode::GetSampledNeighbors(NodeType neighbor_type, int32_t samples_nu
   return Status::OK();
 }
 
-Status LocalNode::AddNeighbor(const std::shared_ptr<Node> &node) {
+Status LocalNode::AddNeighbor(const std::shared_ptr<Node> &node, const WeightType &weight) {
   auto itr = neighbor_nodes_.find(node->type());
   if (itr != neighbor_nodes_.end()) {
-    itr->second.push_back(node);
+    itr->second.first.push_back(node);
+    itr->second.second.push_back(weight);
   } else {
-    neighbor_nodes_[node->type()] = {node};
+    std::vector<std::shared_ptr<Node>> nodes = {node};
+    std::vector<WeightType> weights = {weight};
+    neighbor_nodes_[node->type()] = std::make_pair(std::move(nodes), std::move(weights));
   }
   return Status::OK();
 }

mindspore/ccsrc/minddata/dataset/engine/gnn/local_node.h

@@ -18,6 +18,7 @@
 
 #include <memory>
 #include <unordered_map>
+#include <utility>
 #include <vector>
 
 #include "minddata/dataset/engine/gnn/node.h"
@@ -33,7 +34,7 @@ class LocalNode : public Node {
   // Constructor
   // @param NodeIdType id - node id
   // @param NodeType type - node type
-  LocalNode(NodeIdType id, NodeType type);
+  LocalNode(NodeIdType id, NodeType type, WeightType weight);
 
   ~LocalNode() = default;
 
@@ -53,15 +54,16 @@ class LocalNode : public Node {
   // Get the sampled neighbors of a node
   // @param NodeType neighbor_type - type of neighbor
   // @param int32_t samples_num - Number of neighbors to be acquired
+  // @param SamplingStrategy strategy - Sampling strategy
   // @param std::vector<NodeIdType> *out_neighbors - Returned neighbors id
   // @return Status The status code returned
-  Status GetSampledNeighbors(NodeType neighbor_type, int32_t samples_num,
+  Status GetSampledNeighbors(NodeType neighbor_type, int32_t samples_num, SamplingStrategy strategy,
                              std::vector<NodeIdType> *out_neighbors) override;
 
   // Add neighbor of node
   // @param std::shared_ptr<Node> node -
   // @return Status The status code returned
-  Status AddNeighbor(const std::shared_ptr<Node> &node) override;
+  Status AddNeighbor(const std::shared_ptr<Node> &node, const WeightType &) override;
 
   // Update feature of node
   // @param std::shared_ptr<Feature> feature -
@@ -69,12 +71,16 @@ class LocalNode : public Node {
   Status UpdateFeature(const std::shared_ptr<Feature> &feature) override;
 
  private:
-  Status GetSampledNeighbors(const std::vector<std::shared_ptr<Node>> &neighbors, int32_t samples_num,
-                             std::vector<NodeIdType> *out);
+  Status GetRandomSampledNeighbors(const std::vector<std::shared_ptr<Node>> &neighbors, int32_t samples_num,
+                                   std::vector<NodeIdType> *out);
+
+  Status GetWeightSampledNeighbors(const std::vector<std::shared_ptr<Node>> &neighbors,
+                                   const std::vector<WeightType> &weights, int32_t samples_num,
+                                   std::vector<NodeIdType> *out);
 
   std::mt19937 rnd_;
   std::unordered_map<FeatureType, std::shared_ptr<Feature>> features_;
-  std::unordered_map<NodeType, std::vector<std::shared_ptr<Node>>> neighbor_nodes_;
+  std::unordered_map<NodeType, std::pair<std::vector<std::shared_ptr<Node>>, std::vector<WeightType>>> neighbor_nodes_;
 };
 }  // namespace gnn
 }  // namespace dataset

mindspore/ccsrc/minddata/dataset/engine/gnn/node.h

@@ -28,6 +28,7 @@ namespace dataset {
 namespace gnn {
 using NodeType = int8_t;
 using NodeIdType = int32_t;
+using WeightType = float;
 
 constexpr NodeIdType kDefaultNodeId = -1;
 
@@ -36,7 +37,8 @@ class Node {
   // Constructor
   // @param NodeIdType id - node id
   // @param NodeType type - node type
-  Node(NodeIdType id, NodeType type) : id_(id), type_(type) {}
+  // @param WeightType type - node weight
+  Node(NodeIdType id, NodeType type, WeightType weight) : id_(id), type_(type), weight_(weight) {}
 
   virtual ~Node() = default;
 
@@ -46,6 +48,9 @@ class Node {
   // @return NodeIdType - Returned node type
   NodeType type() const { return type_; }
 
+  // @return WeightType - Returned node weight
+  WeightType weight() const { return weight_; }
+
   // Get the feature of a node
   // @param FeatureType feature_type - type of feature
   // @param std::shared_ptr<Feature> *out_feature - Returned feature
@@ -62,15 +67,16 @@ class Node {
   // Get the sampled neighbors of a node
   // @param NodeType neighbor_type - type of neighbor
   // @param int32_t samples_num - Number of neighbors to be acquired
+  // @param SamplingStrategy strategy - Sampling strategy
   // @param std::vector<NodeIdType> *out_neighbors - Returned neighbors id
   // @return Status The status code returned
-  virtual Status GetSampledNeighbors(NodeType neighbor_type, int32_t samples_num,
+  virtual Status GetSampledNeighbors(NodeType neighbor_type, int32_t samples_num, SamplingStrategy strategy,
                                      std::vector<NodeIdType> *out_neighbors) = 0;
 
   // Add neighbor of node
   // @param std::shared_ptr<Node> node -
   // @return Status The status code returned
-  virtual Status AddNeighbor(const std::shared_ptr<Node> &node) = 0;
+  virtual Status AddNeighbor(const std::shared_ptr<Node> &node, const WeightType &weight) = 0;
 
   // Update feature of node
   // @param std::shared_ptr<Feature> feature -
@@ -80,6 +86,7 @@ class Node {
  protected:
   NodeIdType id_;
   NodeType type_;
+  WeightType weight_;
 };
 }  // namespace gnn
 }  // namespace dataset

mindspore/ccsrc/minddata/dataset/include/constants.h

@@ -71,6 +71,9 @@ enum class NormalizeForm {
   kNfkd,
 };
 
+// Possible values for SamplingStrategy
+enum class SamplingStrategy { kRandom = 0, kEdgeWeight = 1 };
+
 // convenience functions for 32bit int bitmask
 inline bool BitTest(uint32_t bits, uint32_t bitMask) { return (bits & bitMask) == bitMask; }
 

mindspore/dataset/engine/__init__.py

@@ -25,7 +25,7 @@ operations for users to preprocess data: shuffle, batch, repeat, map, and zip.
 from ..core import config
 from .cache_client import DatasetCache
 from .datasets import *
-from .graphdata import GraphData
+from .graphdata import GraphData, SamplingStrategy
 from .iterators import *
 from .samplers import *
 from .serializer_deserializer import compare, deserialize, serialize, show

mindspore/dataset/engine/graphdata.py

@@ -18,10 +18,12 @@ and provides operations related to graph data.
 """
 import atexit
 import time
+from enum import IntEnum
 import numpy as np
 from mindspore._c_dataengine import GraphDataClient
 from mindspore._c_dataengine import GraphDataServer
 from mindspore._c_dataengine import Tensor
+from mindspore._c_dataengine import SamplingStrategy as Sampling
 
 from .validators import check_gnn_graphdata, check_gnn_get_all_nodes, check_gnn_get_all_edges, \
     check_gnn_get_nodes_from_edges, check_gnn_get_all_neighbors, check_gnn_get_sampled_neighbors, \
@@ -29,6 +31,17 @@ from .validators import check_gnn_graphdata, check_gnn_get_all_nodes, check_gnn_
     check_gnn_random_walk
 
 
+class SamplingStrategy(IntEnum):
+    RANDOM = 0
+    EDGE_WEIGHT = 1
+
+
+DE_C_INTER_SAMPLING_STRATEGY = {
+    SamplingStrategy.RANDOM: Sampling.DE_SAMPLING_RANDOM,
+    SamplingStrategy.EDGE_WEIGHT: Sampling.DE_SAMPLING_EDGE_WEIGHT,
+}
+
+
 class GraphData:
     """
     Reads the graph dataset used for GNN training from the shared file and database.
@@ -86,7 +99,7 @@ class GraphData:
                 dataset_file, num_parallel_workers, hostname, port, num_client, auto_shutdown)
             atexit.register(stop)
             try:
-                while self._graph_data.is_stoped() is not True:
+                while self._graph_data.is_stopped() is not True:
                     time.sleep(1)
             except KeyboardInterrupt:
                 raise Exception("Graph data server receives KeyboardInterrupt.")
@@ -185,7 +198,7 @@ class GraphData:
         return self._graph_data.get_all_neighbors(node_list, neighbor_type).as_array()
 
     @check_gnn_get_sampled_neighbors
-    def get_sampled_neighbors(self, node_list, neighbor_nums, neighbor_types):
+    def get_sampled_neighbors(self, node_list, neighbor_nums, neighbor_types, strategy=SamplingStrategy.RANDOM):
         """
         Get sampled neighbor information.
 
@@ -199,6 +212,11 @@ class GraphData:
             node_list (Union[list, numpy.ndarray]): The given list of nodes.
             neighbor_nums (Union[list, numpy.ndarray]): Number of neighbors sampled per hop.
             neighbor_types (Union[list, numpy.ndarray]): Neighbor type sampled per hop.
+            strategy (SamplingStrategy, optional): Sampling strategy (default=SamplingStrategy.RANDOM).
+                It can be any of [SamplingStrategy.RANDOM, SamplingStrategy.EDGE_WEIGHT].
+
+                - SamplingStrategy.RANDOM, random sampling with replacement.
+                - SamplingStrategy.EDGE_WEIGHT, sampling with edge weight as probability.
 
         Returns:
             numpy.ndarray, array of neighbors.
@@ -215,10 +233,12 @@ class GraphData:
             TypeError: If `neighbor_nums` is not list or ndarray.
             TypeError: If `neighbor_types` is not list or ndarray.
         """
+        if not isinstance(strategy, SamplingStrategy):
+            raise TypeError("Wrong input type for strategy, should be enum of 'SamplingStrategy'.")
         if self._working_mode == 'server':
             raise Exception("This method is not supported when working mode is server.")
         return self._graph_data.get_sampled_neighbors(
-            node_list, neighbor_nums, neighbor_types).as_array()
+            node_list, neighbor_nums, neighbor_types, DE_C_INTER_SAMPLING_STRATEGY[strategy]).as_array()
 
     @check_gnn_get_neg_sampled_neighbors
     def get_neg_sampled_neighbors(self, node_list, neg_neighbor_num, neg_neighbor_type):
@@ -342,7 +362,7 @@ class GraphData:
             target_nodes (list[int]): Start node list in random walk
             meta_path (list[int]): node type for each walk step
             step_home_param (float, optional): return hyper parameter in node2vec algorithm (Default = 1.0).
-            step_away_param (float, optional): inout hyper parameter in node2vec algorithm (Default = 1.0).
+            step_away_param (float, optional): in out hyper parameter in node2vec algorithm (Default = 1.0).
             default_node (int, optional): default node if no more neighbors found (Default = -1).
                 A default value of -1 indicates that no node is given.
 

mindspore/dataset/engine/validators.py

@@ -1114,7 +1114,7 @@ def check_gnn_get_sampled_neighbors(method):
 
     @wraps(method)
     def new_method(self, *args, **kwargs):
-        [node_list, neighbor_nums, neighbor_types], _ = parse_user_args(method, *args, **kwargs)
+        [node_list, neighbor_nums, neighbor_types, _], _ = parse_user_args(method, *args, **kwargs)
 
         check_gnn_list_or_ndarray(node_list, 'node_list')
 

model_zoo/utils/graph_to_mindrecord/graph_map_schema.py

@@ -37,6 +37,7 @@ class GraphMapSchema:
             "second_id": {"type": "int64"},
             "third_id": {"type": "int64"},
             "type": {"type": "int32"},
+            "weight": {"type": "float32"},
             "attribute": {"type": "string"},  # 'n' for ndoe, 'e' for edge
             "node_feature_index": {"type": "int32", "shape": [-1]},
             "edge_feature_index": {"type": "int32", "shape": [-1]}
@@ -91,8 +92,11 @@ class GraphMapSchema:
             logger.info("node cannot be None.")
             raise ValueError("node cannot be None.")
 
-        node_graph = {"first_id": node["id"], "second_id": 0, "third_id": 0, "attribute": 'n', "type": node["type"],
-                      "node_feature_index": []}
+        node_graph = {"first_id": node["id"], "second_id": 0, "third_id": 0, "weight": 1.0, "attribute": 'n',
+                      "type": node["type"], "node_feature_index": []}
+        if "weight" in node:
+            node_graph["weight"] = node["weight"]
+
         for i in range(self.num_node_features):
             k = i + 1
             node_field_key = 'feature_' + str(k)
@@ -129,8 +133,11 @@ class GraphMapSchema:
             logger.info("edge cannot be None.")
             raise ValueError("edge cannot be None.")
 
-        edge_graph = {"first_id": edge["id"], "second_id": edge["src_id"], "third_id": edge["dst_id"], "attribute": 'e',
-                      "type": edge["type"], "edge_feature_index": []}
+        edge_graph = {"first_id": edge["id"], "second_id": edge["src_id"], "third_id": edge["dst_id"], "weight": 1.0,
+                      "attribute": 'e', "type": edge["type"], "edge_feature_index": []}
+
+        if "weight" in edge:
+            edge_graph["weight"] = edge["weight"]
 
         for i in range(self.num_edge_features):
             k = i + 1

tests/ut/cpp/dataset/gnn_graph_test.cc

@@ -15,6 +15,7 @@
  */
 #include <algorithm>
 #include <string>
+#include <map>
 #include <memory>
 #include <unordered_set>
 
@@ -38,6 +39,60 @@ using namespace mindspore::dataset::gnn;
 class MindDataTestGNNGraph : public UT::Common {
  protected:
   MindDataTestGNNGraph() = default;
+
+  using NumNeighborsMap = std::map<NodeIdType, uint32_t>;
+  using NodeNeighborsMap = std::map<NodeIdType, NumNeighborsMap>;
+  void ParsingNeighbors(const std::shared_ptr<Tensor> &neighbors, NodeNeighborsMap &node_neighbors) {
+    auto shape_vec = neighbors->shape().AsVector();
+    uint32_t num_members = 1;
+    for (size_t i = 1; i < shape_vec.size(); ++i) {
+      num_members *= shape_vec[i];
+    }
+    uint32_t index = 0;
+    NodeIdType src_node = 0;
+    for (auto node_itr = neighbors->begin<NodeIdType>(); node_itr != neighbors->end<NodeIdType>();
+         ++node_itr, ++index) {
+      if (index % num_members == 0) {
+        src_node = *node_itr;
+        continue;
+      }
+      auto src_node_itr = node_neighbors.find(src_node);
+      if (src_node_itr == node_neighbors.end()) {
+        node_neighbors[src_node] = {{*node_itr, 1}};
+      } else {
+        auto nei_itr = src_node_itr->second.find(*node_itr);
+        if (nei_itr == src_node_itr->second.end()) {
+          src_node_itr->second[*node_itr] = 1;
+        } else {
+          src_node_itr->second[*node_itr] += 1;
+        }
+      }
+    }
+  }
+
+  void CheckNeighborsRatio(const NumNeighborsMap &number_neighbors, const std::vector<WeightType> &weights,
+                           float deviation_ratio = 0.1) {
+    EXPECT_EQ(number_neighbors.size(), weights.size());
+    int index = 0;
+    uint32_t pre_num = 0;
+    WeightType pre_weight = 1;
+    for (auto neighbor : number_neighbors) {
+      if (pre_num != 0) {
+        float target_ratio = static_cast<float>(pre_weight) / static_cast<float>(weights[index]);
+        float current_ratio = static_cast<float>(pre_num) / static_cast<float>(neighbor.second);
+        float target_upper = target_ratio * (1 + deviation_ratio);
+        float target_lower = target_ratio * (1 - deviation_ratio);
+        MS_LOG(INFO) << "current_ratio:" << std::to_string(current_ratio)
+                     << " target_upper:" << std::to_string(target_upper)
+                     << " target_lower:" << std::to_string(target_lower);
+        EXPECT_LE(current_ratio, target_upper);
+        EXPECT_GE(current_ratio, target_lower);
+      }
+      pre_num = neighbor.second;
+      pre_weight = weights[index];
+      ++index;
+    }
+  }
 };
 
 TEST_F(MindDataTestGNNGraph, TestGetAllNeighbors) {
@@ -131,44 +186,75 @@ TEST_F(MindDataTestGNNGraph, TestGetSampledNeighbors) {
   std::transform(node_set.begin(), node_set.end(), node_list.begin(), [](const NodeIdType node) { return node; });
 
   std::shared_ptr<Tensor> neighbors;
-  s = graph.GetSampledNeighbors(node_list, {10}, {meta_info.node_type[1]}, &neighbors);
-  EXPECT_TRUE(s.IsOk());
-  EXPECT_TRUE(neighbors->shape().ToString() == "<5,11>");
+  {
+    MS_LOG(INFO) << "Test random sampling.";
+    NodeNeighborsMap number_neighbors;
+    int count = 0;
+    while (count < 1000) {
+      neighbors.reset();
+      s = graph.GetSampledNeighbors(node_list, {10}, {meta_info.node_type[1]}, SamplingStrategy::kRandom, &neighbors);
+      EXPECT_TRUE(s.IsOk());
+      EXPECT_TRUE(neighbors->shape().ToString() == "<5,11>");
+      ParsingNeighbors(neighbors, number_neighbors);
+      ++count;
+    }
+    CheckNeighborsRatio(number_neighbors[103], {1, 1, 1, 1, 1});
+  }
+
+  {
+    MS_LOG(INFO) << "Test edge weight sampling.";
+    NodeNeighborsMap number_neighbors;
+    int count = 0;
+    while (count < 1000) {
+      neighbors.reset();
+      s =
+        graph.GetSampledNeighbors(node_list, {10}, {meta_info.node_type[1]}, SamplingStrategy::kEdgeWeight, &neighbors);
+      EXPECT_TRUE(s.IsOk());
+      EXPECT_TRUE(neighbors->shape().ToString() == "<5,11>");
+      ParsingNeighbors(neighbors, number_neighbors);
+      ++count;
+    }
+    CheckNeighborsRatio(number_neighbors[103], {3, 5, 6, 7, 8});
+  }
 
   neighbors.reset();
-  s = graph.GetSampledNeighbors(node_list, {2, 3}, {meta_info.node_type[1], meta_info.node_type[0]}, &neighbors);
+  s = graph.GetSampledNeighbors(node_list, {2, 3}, {meta_info.node_type[1], meta_info.node_type[0]},
+                                SamplingStrategy::kRandom, &neighbors);
   EXPECT_TRUE(s.IsOk());
   EXPECT_TRUE(neighbors->shape().ToString() == "<5,9>");
 
   neighbors.reset();
   s = graph.GetSampledNeighbors(node_list, {2, 3, 4},
-                                {meta_info.node_type[1], meta_info.node_type[0], meta_info.node_type[1]}, &neighbors);
+                                {meta_info.node_type[1], meta_info.node_type[0], meta_info.node_type[1]},
+                                SamplingStrategy::kRandom, &neighbors);
   EXPECT_TRUE(s.IsOk());
   EXPECT_TRUE(neighbors->shape().ToString() == "<5,33>");
 
   neighbors.reset();
-  s = graph.GetSampledNeighbors({}, {10}, {meta_info.node_type[1]}, &neighbors);
+  s = graph.GetSampledNeighbors({}, {10}, {meta_info.node_type[1]}, SamplingStrategy::kRandom, &neighbors);
   EXPECT_TRUE(s.ToString().find("Input node_list is empty.") != std::string::npos);
 
   neighbors.reset();
-  s = graph.GetSampledNeighbors({-1, 1}, {10}, {meta_info.node_type[1]}, &neighbors);
+  s = graph.GetSampledNeighbors({-1, 1}, {10}, {meta_info.node_type[1]}, SamplingStrategy::kRandom, &neighbors);
   EXPECT_TRUE(s.ToString().find("Invalid node id") != std::string::npos);
 
   neighbors.reset();
-  s = graph.GetSampledNeighbors(node_list, {2, 50}, {meta_info.node_type[0], meta_info.node_type[1]}, &neighbors);
+  s = graph.GetSampledNeighbors(node_list, {2, 50}, {meta_info.node_type[0], meta_info.node_type[1]},
+                                SamplingStrategy::kRandom, &neighbors);
   EXPECT_TRUE(s.ToString().find("Wrong samples number") != std::string::npos);
 
   neighbors.reset();
-  s = graph.GetSampledNeighbors(node_list, {2}, {5}, &neighbors);
+  s = graph.GetSampledNeighbors(node_list, {2}, {5}, SamplingStrategy::kRandom, &neighbors);
   EXPECT_TRUE(s.ToString().find("Invalid neighbor type") != std::string::npos);
 
   neighbors.reset();
-  s = graph.GetSampledNeighbors(node_list, {2, 3, 4}, {meta_info.node_type[1], meta_info.node_type[0]}, &neighbors);
+  s = graph.GetSampledNeighbors(node_list, {2, 3, 4}, {meta_info.node_type[1], meta_info.node_type[0]},
+                                SamplingStrategy::kRandom, &neighbors);
   EXPECT_TRUE(s.ToString().find("The sizes of neighbor_nums and neighbor_types are inconsistent.") !=
               std::string::npos);
 
   neighbors.reset();
-  s = graph.GetSampledNeighbors({301}, {10}, {meta_info.node_type[1]}, &neighbors);
+  s = graph.GetSampledNeighbors({301}, {10}, {meta_info.node_type[1]}, SamplingStrategy::kRandom, &neighbors);
   EXPECT_TRUE(s.ToString().find("Invalid node id:301") != std::string::npos);
 }
 

tests/ut/python/dataset/test_graphdata.py

@@ -17,6 +17,7 @@ import pytest
 import numpy as np
 import mindspore.dataset as ds
 from mindspore import log as logger
+from mindspore.dataset.engine import SamplingStrategy
 
 DATASET_FILE = "../data/mindrecord/testGraphData/testdata"
 SOCIAL_DATA_FILE = "../data/mindrecord/testGraphData/sns"
@@ -97,7 +98,10 @@ def test_graphdata_getsampledneighbors():
     nodes = g.get_nodes_from_edges(edges)
     assert len(nodes) == 40
     neighbor = g.get_sampled_neighbors(
-        np.unique(nodes[0:21, 0]), [2, 3], [2, 1])
+        np.unique(nodes[0:21, 0]), [2, 3], [2, 1], SamplingStrategy.RANDOM)
+    assert neighbor.shape == (10, 9)
+    neighbor = g.get_sampled_neighbors(
+        np.unique(nodes[0:21, 0]), [2, 3], [2, 1], SamplingStrategy.EDGE_WEIGHT)
     assert neighbor.shape == (10, 9)
 
 

tests/ut/python/dataset/test_graphdata_distributed.py

@@ -20,6 +20,7 @@ from multiprocessing import Process
 import numpy as np
 import mindspore.dataset as ds
 from mindspore import log as logger
+from mindspore.dataset.engine import SamplingStrategy
 
 DATASET_FILE = "../data/mindrecord/testGraphData/testdata"
 
@@ -68,9 +69,9 @@ class GNNGraphDataset():
         neg_nodes = self.g.get_neg_sampled_neighbors(
             node_list=nodes, neg_neighbor_num=3, neg_neighbor_type=1)
         nodes_neighbors = self.g.get_sampled_neighbors(node_list=nodes, neighbor_nums=[
-            2, 2], neighbor_types=[2, 1])
-        neg_nodes_neighbors = self.g.get_sampled_neighbors(
-            node_list=neg_nodes[:, 1:].reshape(-1), neighbor_nums=[2, 2], neighbor_types=[2, 2])
+            2, 2], neighbor_types=[2, 1], strategy=SamplingStrategy.RANDOM)
+        neg_nodes_neighbors = self.g.get_sampled_neighbors(node_list=neg_nodes[:, 1:].reshape(-1), neighbor_nums=[2, 2],
+                                                           neighbor_types=[2, 1], strategy=SamplingStrategy.EDGE_WEIGHT)
         nodes_neighbors_features = self.g.get_node_feature(
             node_list=nodes_neighbors, feature_types=[2, 3])
         neg_neighbors_features = self.g.get_node_feature(