[Misc] Replace \xxx with @XXX in structured comment. (#4822)

* param

* brief

* note

* return

* tparam

* brief2

* file

* return2

* return

* blabla

* all
Co-authored-by: Steve <ubuntu@ip-172-31-34-29.ap-northeast-1.compute.internal>

src/graph/shared_mem_manager.h

 /*!
  *  Copyright (c) 2018 by Contributors
- * \file graph/shared_mem_manager.cc
- * \brief DGL shared mem manager APIs
+ * @file graph/shared_mem_manager.cc
+ * @brief DGL shared mem manager APIs
  */
 
 #ifndef DGL_GRAPH_SHARED_MEM_MANAGER_H_

src/graph/subgraph.cc

 /*!
  *  Copyright (c) 2020 by Contributors
- * \file graph/subgraph.cc
- * \brief Functions for extracting subgraphs.
+ * @file graph/subgraph.cc
+ * @brief Functions for extracting subgraphs.
  */
 #include "./heterograph.h"
 using namespace dgl::runtime;

src/graph/transform/compact.cc

@@ -13,8 +13,8 @@
  *  See the License for the specific language governing permissions and
  *  limitations under the License.
  *
- * \file graph/transform/compact.cc
- * \brief Compact graph implementation
+ * @file graph/transform/compact.cc
+ * @brief Compact graph implementation
  */
 
 #include "compact.h"

src/graph/transform/compact.h

@@ -13,8 +13,8 @@
  *  See the License for the specific language governing permissions and
  *  limitations under the License.
  *
- * \file graph/transform/compact.h
- * \brief Functions to find and eliminate the common isolated nodes across
+ * @file graph/transform/compact.h
+ * @brief Functions to find and eliminate the common isolated nodes across
  * all given graphs with the same set of nodes.
  */
 

src/graph/transform/cpu/kdtree_ndarray_adapter.h

 /*!
  *  Copyright (c) 2021 by Contributors
- * \file graph/transform/cpu/kdtree_ndarray_adapter.h
- * \brief NDArray adapter for nanoflann, without
+ * @file graph/transform/cpu/kdtree_ndarray_adapter.h
+ * @brief NDArray adapter for nanoflann, without
  *        duplicating the storage
  */
 #ifndef DGL_GRAPH_TRANSFORM_CPU_KDTREE_NDARRAY_ADAPTER_H_
@@ -19,19 +19,19 @@ namespace transform {
 namespace knn_utils {
 
 /*!
- * \brief A simple 2D NDArray adapter for nanoflann, without duplicating the
+ * @brief A simple 2D NDArray adapter for nanoflann, without duplicating the
  *        storage.
  *
- * \tparam FloatType: The type of the point coordinates (typically, double or
+ * @tparam FloatType: The type of the point coordinates (typically, double or
  *         float).
- * \tparam IdType: The type for indices in the KD-tree index (typically,
+ * @tparam IdType: The type for indices in the KD-tree index (typically,
  *         size_t of int)
- * \tparam FeatureDim: If set to > 0, it specifies a compile-time fixed
+ * @tparam FeatureDim: If set to > 0, it specifies a compile-time fixed
  *         dimensionality for the points in the data set, allowing more compiler
  *         optimizations.
- * \tparam Dist: The distance metric to use: nanoflann::metric_L1,
+ * @tparam Dist: The distance metric to use: nanoflann::metric_L1,
            nanoflann::metric_L2, nanoflann::metric_L2_Simple, etc.
- * \note The spelling of dgl's adapter ("adapter") is different from naneflann
+ * @note The spelling of dgl's adapter ("adapter") is different from naneflann
  *       ("adaptor")
  */
 template <
@@ -66,7 +66,7 @@ class KDTreeNDArrayAdapter {
   index_type* GetIndex() { return index_; }
 
   /*!
-   * \brief Query for the \a num_closest points to a given point
+   * @brief Query for the \a num_closest points to a given point
    *  Note that this is a short-cut method for GetIndex()->findNeighbors().
    */
   void query(
@@ -77,20 +77,20 @@ class KDTreeNDArrayAdapter {
     index_->findNeighbors(resultSet, query_pt, nanoflann::SearchParams());
   }
 
-  /*! \brief Interface expected by KDTreeSingleIndexAdaptor */
+  /*! @brief Interface expected by KDTreeSingleIndexAdaptor */
   const self_type& derived() const { return *this; }
 
-  /*! \brief Interface expected by KDTreeSingleIndexAdaptor */
+  /*! @brief Interface expected by KDTreeSingleIndexAdaptor */
   self_type& derived() { return *this; }
 
   /*!
-   * \brief Interface expected by KDTreeSingleIndexAdaptor,
+   * @brief Interface expected by KDTreeSingleIndexAdaptor,
    *  return the number of data points
    */
   size_t kdtree_get_point_count() const { return data_->shape[0]; }
 
   /*!
-   * \brief Interface expected by KDTreeSingleIndexAdaptor,
+   * @brief Interface expected by KDTreeSingleIndexAdaptor,
    *  return the dim'th component of the idx'th point
    */
   FloatType kdtree_get_pt(const size_t idx, const size_t dim) const {
@@ -98,7 +98,7 @@ class KDTreeNDArrayAdapter {
   }
 
   /*!
-   * \brief Interface expected by KDTreeSingleIndexAdaptor.
+   * @brief Interface expected by KDTreeSingleIndexAdaptor.
    *  Optional bounding-box computation: return false to
    *  default to a standard bbox computation loop.
    *

src/graph/transform/cpu/knn.cc

 /*!
  *  Copyright (c) 2019 by Contributors
- * \file graph/transform/cpu/knn.cc
- * \brief k-nearest-neighbor (KNN) implementation
+ * @file graph/transform/cpu/knn.cc
+ * @brief k-nearest-neighbor (KNN) implementation
  */
 
 #include "../knn.h"
@@ -28,7 +28,7 @@ namespace impl {
 static constexpr int NN_DESCENT_BLOCK_SIZE = 16384;
 
 /*!
- * \brief Compute Euclidean distance between two vectors, return positive
+ * @brief Compute Euclidean distance between two vectors, return positive
  *  infinite value if the intermediate distance is greater than the worst
  *  distance.
  */
@@ -54,7 +54,7 @@ FloatType EuclideanDistWithCheck(
   }
 }
 
-/*! \brief Compute Euclidean distance between two vectors */
+/*! @brief Compute Euclidean distance between two vectors */
 template <typename FloatType, typename IdType>
 FloatType EuclideanDist(
     const FloatType* vec1, const FloatType* vec2, int64_t dim) {
@@ -67,7 +67,7 @@ FloatType EuclideanDist(
   return dist;
 }
 
-/*! \brief Insert a new element into a heap */
+/*! @brief Insert a new element into a heap */
 template <typename FloatType, typename IdType>
 void HeapInsert(
     IdType* out, FloatType* dist, IdType new_id, FloatType new_dist, int k,
@@ -104,7 +104,7 @@ void HeapInsert(
   }
 }
 
-/*! \brief Insert a new element and its flag into heap, return 1 if insert
+/*! @brief Insert a new element and its flag into heap, return 1 if insert
  * successfully */
 template <typename FloatType, typename IdType>
 int FlaggedHeapInsert(
@@ -144,7 +144,7 @@ int FlaggedHeapInsert(
   return 1;
 }
 
-/*! \brief Build heap for each point. Used by NN-descent */
+/*! @brief Build heap for each point. Used by NN-descent */
 template <typename FloatType, typename IdType>
 void BuildHeap(IdType* index, FloatType* dist, int k) {
   for (int i = k / 2 - 1; i >= 0; --i) {
@@ -171,7 +171,7 @@ void BuildHeap(IdType* index, FloatType* dist, int k) {
 }
 
 /*!
- * \brief Neighbor update process in NN-descent. The distance between
+ * @brief Neighbor update process in NN-descent. The distance between
  *  two points are computed. If this new distance is less than any worst
  *  distance of these two points, we update the neighborhood of that point.
  */
@@ -208,7 +208,7 @@ int UpdateNeighbors(
   return num_updates;
 }
 
-/*! \brief The kd-tree implementation of K-Nearest Neighbors */
+/*! @brief The kd-tree implementation of K-Nearest Neighbors */
 template <typename FloatType, typename IdType>
 void KdTreeKNN(
     const NDArray& data_points, const IdArray& data_offsets,

src/graph/transform/cuda/cuda_compact_graph.cu

@@ -13,8 +13,8 @@
  *  See the License for the specific language governing permissions and
  *  limitations under the License.
  *
- * \file graph/transform/cuda/cuda_compact_graph.cu
- * \brief Functions to find and eliminate the common isolated nodes across
+ * @file graph/transform/cuda/cuda_compact_graph.cu
+ * @brief Functions to find and eliminate the common isolated nodes across
  * all given graphs with the same set of nodes.
  */
 
@@ -41,15 +41,15 @@ namespace transform {
 namespace {
 
 /**
- * \brief This function builds node maps for each node type, preserving the
+ * @brief This function builds node maps for each node type, preserving the
  * order of the input nodes. Here it is assumed the nodes are not unique,
  * and thus a unique list is generated.
  *
- * \param input_nodes The set of input nodes.
- * \param node_maps The node maps to be constructed.
- * \param count_unique_device The number of unique nodes (on the GPU).
- * \param unique_nodes_device The unique nodes (on the GPU).
- * \param stream The stream to operate on.
+ * @param input_nodes The set of input nodes.
+ * @param node_maps The node maps to be constructed.
+ * @param count_unique_device The number of unique nodes (on the GPU).
+ * @param unique_nodes_device The unique nodes (on the GPU).
+ * @param stream The stream to operate on.
  */
 template <typename IdType>
 void BuildNodeMaps(

src/graph/transform/cuda/cuda_map_edges.cuh

@@ -13,8 +13,8 @@
  *  See the License for the specific language governing permissions and
  *  limitations under the License.
  *
- * \file graph/transform/cuda/cuda_map_edges.cuh
- * \brief Device level functions for mapping edges.
+ * @file graph/transform/cuda/cuda_map_edges.cuh
+ * @brief Device level functions for mapping edges.
  */
 
 #ifndef DGL_GRAPH_TRANSFORM_CUDA_CUDA_MAP_EDGES_CUH_
@@ -59,20 +59,20 @@ __device__ void map_vertex_ids(
 }
 
 /**
- * \brief Generate mapped edge endpoint ids.
+ * @brief Generate mapped edge endpoint ids.
  *
- * \tparam IdType The type of id.
- * \tparam BLOCK_SIZE The size of each thread block.
- * \tparam TILE_SIZE The number of edges to process per thread block.
- * \param global_srcs_device The source ids to map.
- * \param new_global_srcs_device The mapped source ids (output).
- * \param global_dsts_device The destination ids to map.
- * \param new_global_dsts_device The mapped destination ids (output).
- * \param num_edges The number of edges to map.
- * \param src_mapping The mapping of sources ids.
- * \param src_hash_size The the size of source id hash table/mapping.
- * \param dst_mapping The mapping of destination ids.
- * \param dst_hash_size The the size of destination id hash table/mapping.
+ * @tparam IdType The type of id.
+ * @tparam BLOCK_SIZE The size of each thread block.
+ * @tparam TILE_SIZE The number of edges to process per thread block.
+ * @param global_srcs_device The source ids to map.
+ * @param new_global_srcs_device The mapped source ids (output).
+ * @param global_dsts_device The destination ids to map.
+ * @param new_global_dsts_device The mapped destination ids (output).
+ * @param num_edges The number of edges to map.
+ * @param src_mapping The mapping of sources ids.
+ * @param src_hash_size The the size of source id hash table/mapping.
+ * @param dst_mapping The mapping of destination ids.
+ * @param dst_hash_size The the size of destination id hash table/mapping.
  */
 template <typename IdType, int BLOCK_SIZE, IdType TILE_SIZE>
 __global__ void map_edge_ids(
@@ -95,14 +95,14 @@ __global__ void map_edge_ids(
 }
 
 /**
- * \brief Device level node maps for each node type.
+ * @brief Device level node maps for each node type.
  *
- * \param num_nodes Number of nodes per type.
- * \param offset When offset is set to 0, LhsHashTable is identical to
+ * @param num_nodes Number of nodes per type.
+ * @param offset When offset is set to 0, LhsHashTable is identical to
  *        RhsHashTable. Or set to num_nodes.size()/2 to use seperated
  *        LhsHashTable and RhsHashTable.
- * \param ctx The DGL context.
- * \param stream The stream to operate on.
+ * @param ctx The DGL context.
+ * @param stream The stream to operate on.
  */
 template <typename IdType>
 class DeviceNodeMap {

src/graph/transform/cuda/cuda_to_block.cu

@@ -13,8 +13,8 @@
  *  See the License for the specific language governing permissions and
  *  limitations under the License.
  *
- * \file graph/transform/cuda/cuda_to_block.cu
- * \brief Functions to convert a set of edges into a graph block with local
+ * @file graph/transform/cuda/cuda_to_block.cu
+ * @brief Functions to convert a set of edges into a graph block with local
  * ids.
  */
 
@@ -50,16 +50,16 @@ class DeviceNodeMapMaker {
   }
 
   /**
-   * \brief This function builds node maps for each node type, preserving the
+   * @brief This function builds node maps for each node type, preserving the
    * order of the input nodes. Here it is assumed the lhs_nodes are not unique,
    * and thus a unique list is generated.
    *
-   * \param lhs_nodes The set of source input nodes.
-   * \param rhs_nodes The set of destination input nodes.
-   * \param node_maps The node maps to be constructed.
-   * \param count_lhs_device The number of unique source nodes (on the GPU).
-   * \param lhs_device The unique source nodes (on the GPU).
-   * \param stream The stream to operate on.
+   * @param lhs_nodes The set of source input nodes.
+   * @param rhs_nodes The set of destination input nodes.
+   * @param node_maps The node maps to be constructed.
+   * @param count_lhs_device The number of unique source nodes (on the GPU).
+   * @param lhs_device The unique source nodes (on the GPU).
+   * @param stream The stream to operate on.
    */
   void Make(
       const std::vector<IdArray>& lhs_nodes,
@@ -96,14 +96,14 @@ class DeviceNodeMapMaker {
   }
 
   /**
-   * \brief This function builds node maps for each node type, preserving the
+   * @brief This function builds node maps for each node type, preserving the
    * order of the input nodes. Here it is assumed both lhs_nodes and rhs_nodes
    * are unique.
    *
-   * \param lhs_nodes The set of source input nodes.
-   * \param rhs_nodes The set of destination input nodes.
-   * \param node_maps The node maps to be constructed.
-   * \param stream The stream to operate on.
+   * @param lhs_nodes The set of source input nodes.
+   * @param rhs_nodes The set of destination input nodes.
+   * @param node_maps The node maps to be constructed.
+   * @param stream The stream to operate on.
    */
   void Make(
       const std::vector<IdArray>& lhs_nodes,

src/graph/transform/cuda/knn.cu

 /*!
  *  Copyright (c) 2020 by Contributors
- * \file graph/transform/cuda/knn.cu
- * \brief k-nearest-neighbor (KNN) implementation (cuda)
+ * @file graph/transform/cuda/knn.cu
+ * @brief k-nearest-neighbor (KNN) implementation (cuda)
  */
 
 #include <curand_kernel.h>
@@ -23,7 +23,7 @@ namespace dgl {
 namespace transform {
 namespace impl {
 /*!
- * \brief Utility class used to avoid linker errors with extern
+ * @brief Utility class used to avoid linker errors with extern
  *  unsized shared memory arrays with templated type
  */
 template <typename Type>
@@ -54,7 +54,7 @@ struct SharedMemory<double> {
   }
 };
 
-/*! \brief Compute Euclidean distance between two vectors in a cuda kernel */
+/*! @brief Compute Euclidean distance between two vectors in a cuda kernel */
 template <typename FloatType, typename IdType>
 __device__ FloatType
 EuclideanDist(const FloatType* vec1, const FloatType* vec2, const int64_t dim) {
@@ -78,7 +78,7 @@ EuclideanDist(const FloatType* vec1, const FloatType* vec2, const int64_t dim) {
 }
 
 /*!
- * \brief Compute Euclidean distance between two vectors in a cuda kernel,
+ * @brief Compute Euclidean distance between two vectors in a cuda kernel,
  *  return positive infinite value if the intermediate distance is greater
  *  than the worst distance.
  */
@@ -239,7 +239,7 @@ __device__ bool FlaggedHeapInsert(
 }
 
 /*!
- * \brief Brute force kNN kernel. Compute distance for each pair of input points
+ * @brief Brute force kNN kernel. Compute distance for each pair of input points
  * and get the result directly (without a distance matrix).
  */
 template <typename FloatType, typename IdType>
@@ -279,7 +279,7 @@ __global__ void BruteforceKnnKernel(
 }
 
 /*!
- * \brief Same as BruteforceKnnKernel, but use shared memory as buffer.
+ * @brief Same as BruteforceKnnKernel, but use shared memory as buffer.
  *  This kernel divides query points and data points into blocks. For each
  *  query block, it will make a loop over all data blocks and compute distances.
  *  This kernel is faster when the dimension of input points is not large.
@@ -400,7 +400,7 @@ __global__ void BruteforceKnnShareKernel(
   }
 }
 
-/*! \brief determine the number of blocks for each segment */
+/*! @brief determine the number of blocks for each segment */
 template <typename IdType>
 __global__ void GetNumBlockPerSegment(
     const IdType* offsets, IdType* out, const int64_t batch_size,
@@ -411,7 +411,7 @@ __global__ void GetNumBlockPerSegment(
   }
 }
 
-/*! \brief Get the batch index and local index in segment for each block */
+/*! @brief Get the batch index and local index in segment for each block */
 template <typename IdType>
 __global__ void GetBlockInfo(
     const IdType* num_block_prefixsum, IdType* block_batch_id,
@@ -430,17 +430,17 @@ __global__ void GetBlockInfo(
 }
 
 /*!
- * \brief Brute force kNN. Compute distance for each pair of input points and
+ * @brief Brute force kNN. Compute distance for each pair of input points and
  * get the result directly (without a distance matrix).
  *
- * \tparam FloatType The type of input points.
- * \tparam IdType The type of id.
- * \param data_points NDArray of dataset points.
- * \param data_offsets offsets of point index in data points.
- * \param query_points NDArray of query points
- * \param query_offsets offsets of point index in query points.
- * \param k the number of nearest points
- * \param result output array
+ * @tparam FloatType The type of input points.
+ * @tparam IdType The type of id.
+ * @param data_points NDArray of dataset points.
+ * @param data_offsets offsets of point index in data points.
+ * @param query_points NDArray of query points
+ * @param query_offsets offsets of point index in query points.
+ * @param k the number of nearest points
+ * @param result output array
  */
 template <typename FloatType, typename IdType>
 void BruteForceKNNCuda(
@@ -473,19 +473,19 @@ void BruteForceKNNCuda(
 }
 
 /*!
- * \brief Brute force kNN with shared memory.
+ * @brief Brute force kNN with shared memory.
  *  This function divides query points and data points into blocks. For each
  *  query block, it will make a loop over all data blocks and compute distances.
  *  It will be faster when the dimension of input points is not large.
  *
- * \tparam FloatType The type of input points.
- * \tparam IdType The type of id.
- * \param data_points NDArray of dataset points.
- * \param data_offsets offsets of point index in data points.
- * \param query_points NDArray of query points
- * \param query_offsets offsets of point index in query points.
- * \param k the number of nearest points
- * \param result output array
+ * @tparam FloatType The type of input points.
+ * @tparam IdType The type of id.
+ * @param data_points NDArray of dataset points.
+ * @param data_offsets offsets of point index in data points.
+ * @param query_points NDArray of query points
+ * @param query_offsets offsets of point index in query points.
+ * @param k the number of nearest points
+ * @param result output array
  */
 template <typename FloatType, typename IdType>
 void BruteForceKNNSharedCuda(
@@ -575,7 +575,7 @@ void BruteForceKNNSharedCuda(
   device->FreeWorkspace(ctx, block_batch_id);
 }
 
-/*! \brief Setup rng state for nn-descent */
+/*! @brief Setup rng state for nn-descent */
 __global__ void SetupRngKernel(
     curandState* states, const uint64_t seed, const size_t n) {
   size_t id = blockIdx.x * blockDim.x + threadIdx.x;
@@ -585,7 +585,7 @@ __global__ void SetupRngKernel(
 }
 
 /*!
- * \brief Randomly initialize neighbors (sampling without replacement)
+ * @brief Randomly initialize neighbors (sampling without replacement)
  * for each nodes
  */
 template <typename FloatType, typename IdType>
@@ -637,7 +637,7 @@ __global__ void RandomInitNeighborsKernel(
 }
 
 /*!
- * \brief Randomly select candidates from current knn and reverse-knn graph for
+ * @brief Randomly select candidates from current knn and reverse-knn graph for
  *        nn-descent.
  */
 template <typename IdType>
@@ -735,7 +735,7 @@ __global__ void FindCandidatesKernel(
   }
 }
 
-/*! \brief Update knn graph according to selected candidates for nn-descent */
+/*! @brief Update knn graph according to selected candidates for nn-descent */
 template <typename FloatType, typename IdType>
 __global__ void UpdateNeighborsKernel(
     const FloatType* points, const IdType* offsets, IdType* neighbors,

src/graph/transform/knn.cc

 /*!
  *  Copyright (c) 2019 by Contributors
- * \file graph/transform/knn.cc
- * \brief k-nearest-neighbor (KNN) interface
+ * @file graph/transform/knn.cc
+ * @brief k-nearest-neighbor (KNN) interface
  */
 
 #include "knn.h"

src/graph/transform/knn.h

 /*!
  *  Copyright (c) 2021 by Contributors
- * \file graph/transform/knn.h
- * \brief k-nearest-neighbor (KNN) implementation
+ * @file graph/transform/knn.h
+ * @brief k-nearest-neighbor (KNN) implementation
  */
 
 #ifndef DGL_GRAPH_TRANSFORM_KNN_H_
@@ -15,19 +15,19 @@ namespace dgl {
 namespace transform {
 
 /*!
- * \brief For each point in each segment in \a query_points, find \a k nearest
+ * @brief For each point in each segment in \a query_points, find \a k nearest
  *        points in the same segment in \a data_points. \a data_offsets and \a
  *        query_offsets determine the start index of each segment in \a
  *        data_points and \a query_points.
  *
- * \param data_points dataset points.
- * \param data_offsets offsets of point index in \a data_points.
- * \param query_points query points.
- * \param query_offsets offsets of point index in \a query_points.
- * \param k the number of nearest points.
- * \param result output array. A 2D tensor indicating the index  relation
+ * @param data_points dataset points.
+ * @param data_offsets offsets of point index in \a data_points.
+ * @param query_points query points.
+ * @param query_offsets offsets of point index in \a query_points.
+ * @param k the number of nearest points.
+ * @param result output array. A 2D tensor indicating the index  relation
  *        between \a query_points and \a data_points.
- * \param algorithm algorithm used to compute the k-nearest neighbors.
+ * @param algorithm algorithm used to compute the k-nearest neighbors.
  */
 template <DGLDeviceType XPU, typename FloatType, typename IdType>
 void KNN(
@@ -36,18 +36,18 @@ void KNN(
     IdArray result, const std::string& algorithm);
 
 /*!
- * \brief For each input point, find \a k approximate nearest points in the same
+ * @brief For each input point, find \a k approximate nearest points in the same
  *        segment using NN-descent algorithm.
  *
- * \param points input points.
- * \param offsets offsets of point index.
- * \param result output array. A 2D tensor indicating the index relation between
+ * @param points input points.
+ * @param offsets offsets of point index.
+ * @param result output array. A 2D tensor indicating the index relation between
  *        points.
- * \param k the number of nearest points.
- * \param num_iters The maximum number of NN-descent iterations to perform.
- * \param num_candidates The maximum number of candidates to be considered
+ * @param k the number of nearest points.
+ * @param num_iters The maximum number of NN-descent iterations to perform.
+ * @param num_candidates The maximum number of candidates to be considered
  *        during one iteration.
- * \param delta A value controls the early abort.
+ * @param delta A value controls the early abort.
  */
 template <DGLDeviceType XPU, typename FloatType, typename IdType>
 void NNDescent(

src/graph/transform/line_graph.cc

 /*!
  *  Copyright (c) 2020 by Contributors
- * \file graph/transform/line_graph.cc
- * \brief Line graph implementation
+ * @file graph/transform/line_graph.cc
+ * @brief Line graph implementation
  */
 
 #include <dgl/array.h>
@@ -23,11 +23,11 @@ using namespace dgl::aten;
 namespace transform {
 
 /*!
- * \brief Create Line Graph.
- * \param hg Graph.
- * \param backtracking whether the pair of (v, u) (u, v) edges are treated as
+ * @brief Create Line Graph.
+ * @param hg Graph.
+ * @param backtracking whether the pair of (v, u) (u, v) edges are treated as
  *        linked.
- * \return The Line Graph.
+ * @return The Line Graph.
  */
 HeteroGraphPtr CreateLineGraph(HeteroGraphPtr hg, bool backtracking) {
   const auto hgp = std::dynamic_pointer_cast<HeteroGraph>(hg);

src/graph/transform/metis_partition_hetero.cc

 /*!
  *  Copyright (c) 2020 by Contributors
- * \file graph/metis_partition.cc
- * \brief Call Metis partitioning
+ * @file graph/metis_partition.cc
+ * @brief Call Metis partitioning
  */
 
 #include <dgl/base_heterograph.h>

src/graph/transform/partition_hetero.cc

 /*!
  *  Copyright (c) 2020 by Contributors
- * \file graph/metis_partition.cc
- * \brief Call Metis partitioning
+ * @file graph/metis_partition.cc
+ * @brief Call Metis partitioning
  */
 
 #include <dgl/base_heterograph.h>

src/graph/transform/remove_edges.cc

 /*!
  *  Copyright (c) 2019 by Contributors
- * \file graph/transform/remove_edges.cc
- * \brief Remove edges.
+ * @file graph/transform/remove_edges.cc
+ * @brief Remove edges.
  */
 
 #include <dgl/array.h>

src/graph/transform/to_bipartite.cc

@@ -13,8 +13,8 @@
  *  See the License for the specific language governing permissions and
  *  limitations under the License.
  *
- * \file graph/transform/to_bipartite.cc
- * \brief Convert a graph to a bipartite-structured graph.
+ * @file graph/transform/to_bipartite.cc
+ * @brief Convert a graph to a bipartite-structured graph.
  */
 
 #include "to_bipartite.h"

src/graph/transform/to_bipartite.h

@@ -13,8 +13,8 @@
  *  See the License for the specific language governing permissions and
  *  limitations under the License.
  *
- * \file graph/transform/to_bipartite.h
- * \brief Functions to convert a set of edges into a graph block with local
+ * @file graph/transform/to_bipartite.h
+ * @brief Functions to convert a set of edges into a graph block with local
  * ids.
  */
 

src/graph/transform/to_simple.cc

 /*!
  *  Copyright (c) 2019 by Contributors
- * \file graph/transform/to_simple.cc
- * \brief Convert multigraphs to simple graphs
+ * @file graph/transform/to_simple.cc
+ * @brief Convert multigraphs to simple graphs
  */
 
 #include <dgl/array.h>

src/graph/transform/union_partition.cc

 /*!
  *  Copyright (c) 2020 by Contributors
- * \file graph/transform/union_partition.cc
- * \brief Functions for partition, union multiple graphs.
+ * @file graph/transform/union_partition.cc
+ * @brief Functions for partition, union multiple graphs.
  */
 #include "../heterograph.h"
 using namespace dgl::runtime;