Difficulties with layer based sampling implementation (LADIES sampler)

I am trying to implement a LADIES layer based sampler using the DGL library.
Essentially, this is done by calculating the probability of two nodes being in the same neighbourhood via the laplacian and then sampling based on this probability.
I implement (a very simplified one layer version) using:

class LADIESSampler(dgl.dataloading.Sampler):
    def __init__(self, nodes_per_layer, laplacian):
        self.fanouts = nodes_per_layer
        self.laplacian = laplacian  
    def get_layer_nodes(self, g, seed_nodes, nodes_per_layer): 
        U = self.laplacian[seed_nodes, :]
        pi = np.array(np.sum(U.multiply(U), axis=0))[0]
        p = pi / np.sum(pi)
        s_num = np.min([np.sum(p > 0), nodes_per_layer])
        selected_nodes = np.random.choice(g.num_nodes(), s_num, p = p, replace = False)
        all_nodes = torch.concat((torch.tensor(selected_nodes), seed_nodes))
        print("selected", selected_nodes, "entire layer", all_nodes, p)
        return all_nodes
    def sample_blocks(self, g, seed_nodes):
        print("seed nodes", seed_nodes)
        blocks = []
        all_nodes = self.get_layer_nodes(g, torch.tensor(seed_nodes), 2) 
        frontier2 = dgl.node_subgraph(g, all_nodes)
        eid = frontier.edata[dgl.EID]
        block = dgl.to_block(frontier, seed_nodes)
        block.edata[dgl.EID] = eid
        seed_nodes = block.srcdata[dgl.NID]
        blocks.insert(0, block)
        return seed_nodes, output_nodes, blocks

But I get the following error:

DGLError                                  Traceback (most recent call last)
Input In [228], in <cell line: 1>()
----> 1 s.sample_blocks(g, [2])

Input In [226], in LADIESSampler.sample_blocks(self, g, seed_nodes)
     20 frontier=frontier2
     21 eid = frontier.edata[dgl.EID]
---> 22 block = dgl.to_block(frontier, seed_nodes)
     23 block.edata[dgl.EID] = eid
     24 seed_nodes = block.srcdata[dgl.NID]

File ~/.conda/envs/nsv4/lib/python3.9/site-packages/dgl/transforms/functional.py:2286, in to_block(g, dst_nodes, include_dst_in_src, src_nodes)
   2282 else:
   2283     # use an empty list to signal we need to generate it
   2284     src_node_ids_nd = []
-> 2286 new_graph_index, src_nodes_ids_nd, induced_edges_nd = _CAPI_DGLToBlock(
   2287     g._graph, dst_node_ids_nd, include_dst_in_src, src_node_ids_nd)
   2289 # The new graph duplicates the original node types to SRC and DST sets.
   2290 new_ntypes = (g.ntypes, g.ntypes)

File dgl/_ffi/_cython/./function.pxi:293, in dgl._ffi._cy3.core.FunctionBase.__call__()

File dgl/_ffi/_cython/./function.pxi:239, in dgl._ffi._cy3.core.FuncCall()

DGLError: [11:26:58] /opt/dgl/src/graph/transform/to_bipartite.cc:119: Check failed: new_dst.Ptr<IdType>()[i] != -1 (-1 vs. -1) : Node 0 does not exist in `rhs_nodes`. Argument `rhs_nodes` must contain all the edge destination nodes.

To recreate this error, run:

import numpy as np
import networkx as nx 
import matplotlib.pyplot as plt
import torch
import dgl 

#create baby graph
edges = [(1, 2), (1,3), (1,7), (2,5), (2,8), (3,11), (3,12), (3,4), 
        (4,1), (4,5), (4,12), (5,4), (5,6), (5,2), (7,13),  (10, 9), 
        (10, 11),  (11, 12)]
src = [e[0]-1 for e in edges]
dst = [e[1]-1 for e in edges]

#visualize the graph 
G = dgl.to_networkx(g)
nx.draw(G, arrows = None, with_labels = True)  # networkx draw()

#make sure its undirected
g = dgl.to_bidirected(dgl.graph((src, dst)))

#initialize sampler
s = LADIESSampler([2], lap_matrix)

#try to sample a block 
s.sample_blocks(g, [2])

Any advice on what I’m doing wrong? I have a feeling that it’s either the graph creation or the process for making it a block.

Thank you in advance!

The destination nodes specified must be a superset of the nodes that have edges connecting to them. For example, the following will raise an error since the destination nodes does not contain node 3, which has an edge connecting to it.

g = dgl.graph(([1, 2], [2, 3]))
dgl.to_block(g, torch.LongTensor([2]))     # error

check more details here: dgl.to_block — DGL 0.9 documentation

Thanks, I see that now! But is there an alternative way to generate a subgraph that contains all these nodes that is also compatible with the message passing paradigm? For example, dgl.node_subgraph(), can this be used in message passing?

How about including sub_g.dstnodes() into seed_nodes? or in reverse, just remove the incoming edges for those nodes which not exists in seed_nodes from sub_g?

I’m not sure I follow what you are suggesting: are you saying that I copy the graph and then remove all edges except the ones that I have selected to be in each layer? Did I understand correctly?

I think my confusion also lies in why the frontier needs to contain all the nodes if for each mini-batch the only representations that should be updated are the ones in the subgraph. For example, when I look at the GraphSage implementation, I don’t think the mini-batches contain all the nodes in the graph.

Thank you for your responses, by the way, I really appreciate your taking them time!


See more details here: Introduction of Neighbor Sampling for GNN Training — DGL 0.9 documentation

Note that some GNN modules, such as SAGEConv, need to use the destination nodes’ features on the previous layer to compute the outputs. Without loss of generality, DGL always includes the destination nodes themselves in the source nodes.
Refer to SAGEConv — DGL 0.9 documentation for more details.

I’m not sure I follow what you are suggesting: are you saying that I copy the graph and then remove all edges except the ones that I have selected to be in each layer? Did I understand correctly?


Ok, this makes sense. But to reiterate the follow up question (after looking at the tutorial you linked): if I follow the process you suggested then the mini-batch contains all the nodes in the graph (rather than just the nodes which are actively relevant for message passing). Did I understand that correctly? And, if so, doesn’t that make the process computationally inefficient for very large graphs since I will have to store all the nodes on GPU?

you mean this subgraph is very large? contains all nodes? It’s supposed to be a subgraph instead of a graph containing all nodes.

Hi @Rsalganik1123 . I think your difficulties are from constructing a bipartite graph from DGLGraph. Currently DGL does not have an alternative to create a bipartite subgraph directly. However, here might be a workaround for layer-wise sampling.

g = dgl.graph(...)
sg = dgl.in_subgraph(g, seed_nodes)
sg = dgl.out_subgraph(sg, selected_nodes)
block = dgl.to_block(sg, seed_nodes)

The complexity of above code is relevant to the number of in edges of the seed nodes.

Also be careful to use

all_nodes = torch.concat((torch.tensor(selected_nodes), seed_nodes))
frontier2 = dgl.node_subgraph(g, all_nodes)

, which may lead to undefined behavior since dgl does not support deduplicate values when inducing subgraph.