Update Labor sampler docs, add NeurIPS acceptance

python/dgl/dataloading/labor_sampler.py

@@ -28,8 +28,8 @@
 
 class LaborSampler(BlockSampler):
     """Sampler that builds computational dependency of node representations via
-    labor sampling for multilayer GNN from
-    `(LA)yer-neigh(BOR) Sampling: Defusing Neighborhood Explosion in GNNs
+    labor sampling for multilayer GNN from the NeurIPS 2023 paper
+    `Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
     <https://arxiv.org/abs/2210.13339>`__
 
     This sampler will make every node gather messages from a fixed number of

python/dgl/graphbolt/impl/csc_sampling_graph.py

@@ -437,8 +437,9 @@ def sample_layer_neighbors(
         probs_name: Optional[str] = None,
     ) -> SampledSubgraphImpl:
         """Sample neighboring edges of the given nodes and return the induced
-        subgraph via layer-neighbor sampling from arXiv:2210.13339:
-        "Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs"
+        subgraph via layer-neighbor sampling from the NeurIPS 2023 paper
+        `Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
+        <https://arxiv.org/abs/2210.13339>`__
 
         Parameters
         ----------

python/dgl/graphbolt/impl/neighbor_sampler.py

@@ -122,6 +122,11 @@ def _sample_subgraphs(self, seeds):
 @functional_datapipe("sample_layer_neighbor")
 class LayerNeighborSampler(NeighborSampler):
     """
+    Sampler that builds computational dependency of node representations via
+    labor sampling for multilayer GNN from the NeurIPS 2023 paper
+    `Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
+    <https://arxiv.org/abs/2210.13339>`__
+
     Layer-Neighbor sampler is responsible for sampling a subgraph from given
     data. It returns an induced subgraph along with compacted information. In
     the context of a node classification task, the neighbor sampler directly
@@ -131,10 +136,13 @@ class LayerNeighborSampler(NeighborSampler):
     positive and negative node pairs, and employs these nodes as the seed nodes
     for subsequent steps.
 
-    Implements the approach described in https://arxiv.org/abs/2210.13339,
-    Appendix A.3. Similar to dgl.dataloading.LaborSampler but this uses
-    sequential poisson sampling instead of poisson sampling to keep the count
-    of sampled edges per vertex deterministic.
+    Implements the approach described in Appendix A.3 of the paper. Similar to
+    dgl.dataloading.LaborSampler but this uses sequential poisson sampling
+    instead of poisson sampling to keep the count of sampled edges per vertex
+    deterministic like NeighborSampler. Thus, it is a drop-in replacement for
+    NeighborSampler. However, unlike NeighborSampler, it samples fewer vertices
+    and edges for multilayer GNN scenario without harming convergence speed with
+    respect to training iterations.
     """
 
     def __init__(

python/dgl/sampling/labor.py

@@ -44,9 +44,9 @@ def sample_labors(
     output_device=None,
 ):
     """Sampler that builds computational dependency of node representations via
-    labor sampling for multilayer GNN from
-    `(LA)yer-neigh(BOR) Sampling: Defusing Neighborhood Explosion in GNNs
-    <https://arxiv.org/abs/2210.13339>`
+    labor sampling for multilayer GNN from the NeurIPS 2023 paper
+    `Layer-Neighbor Sampling -- Defusing Neighborhood Explosion in GNNs
+    <https://arxiv.org/abs/2210.13339>`__
 
     This sampler will make every node gather messages from a fixed number of neighbors
     per edge type. The neighbors are picked uniformly with default parameters. For every vertex t