Link Prediction on two merged Heterogeneous Graphs #8211

jameswpm · 2023-10-17T16:49:35Z

jameswpm
Oct 17, 2023

I'm working with Heterogeneous Graphs for Link prediction using different customized datasets.

In the current problem/dataset, I want to know if I reached some limitations on the Link pred capabilities or if it's just my lack of knowledge of PyG. I started to work with PyG last year and still consider myself a noob. Any help is welcome.

The problem

Basically, I want to get 2 disjoint feature-less graphs, create a set of links between them, and check the capability of the GNN to predict these links.

The data

My custom graph is built and the GNN is used following the steps:

1 - Get the graph A (8 different node types and 13 different edge types)
2 - Get the graph B (7 different node types and 13 different edge types)
3 - Merge them using update
4 - Set a set of links between nodes of type a (from graph A) and nodes of type b (from graph B)
5 - Run a Link prediction GNN to find out the performance in discovering the links between A(a) and B(b)

After the data creation, I'm running the validate without errors, so I don't have a problem with the data.

What I've tried

My first attempt was to try to adapt the MovieLens example, using the same model and almost the same structure just excluding the feature embedding layers since I don't have node features). My guess is that SageConv layer is the best model to deal with my task, and it's the easy one to find out good examples for Link Prediction.

It gave me the error raise RuntimeError(f'Node {node} target {node.target} references nonexistent attribute 'f'{atom} of {seen_qualname}') when running the to_hetero function. If I got it correctly from this other issue, it is an incompatibility of some layers to work with specific graphs. Am I right here?

My questions

1 - Am I in the right direction trying to use SageConv to deal with this kind of problem?
2 - The error that I'm getting is indeed to the incompatibilities of SageConv and my type of graph?
3 - Any other suggestions 😅

My model code

Pretty the same of MovieLens

class GNN(torch.nn.Module):
    def __init__(self, hidden_channels):
        super().__init__()
        self.conv1 = SAGEConv(hidden_channels, hidden_channels)
        self.conv2 = SAGEConv(hidden_channels, hidden_channels)

    def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
        # A 2-layer GNN computation graph.
        # `ReLU` is the non-lineary function used in-between.
        x = self.conv1(x, edge_index).relu()
        x = self.conv2(x, edge_index)
        return x
        
class Classifier(torch.nn.Module):
    def forward(self, x_i: Tensor, x_j: Tensor) -> Tensor:
        # Convert node embeddings to edge-level representations:
        edge_feat_a = x_i[edge_label_index[0]]
        edge_feat_b = x_j[edge_label_index[1]]

        # Apply dot-product to get a prediction per supervision edge:
        return (edge_feat_a * edge_feat_b ).sum(dim=-1)
        
 class Model(torch.nn.Module):
    def __init__(self, hidden_channels):
        super().__init__()
        # initial embeddings
        self.x_i_emb = torch.nn.Embedding(
            graph["a"].num_nodes, hidden_channels)
        self.x_j_emb = torch.nn.Embedding(
            graph["b"].num_nodes, hidden_channels)

        # Instantiate homogeneous GNN:
        self.gnn = GNN(hidden_channels)

        # Convert GNN model into a heterogeneous variant:
        self.gnn = to_hetero(self.gnn, metadata=graph.metadata())

        self.classifier = Classifier()

    def forward(self, data) -> Tensor:
        x_dict = {
            "a": self.x_i_emb(data["a"].node_id),
            "b": self.x_j_emb(data["b"].node_id),
        }

        # `x_dict` holds feature matrices of all node types
        # `edge_index_dict` holds all edge indices of all edge types
        x_dict = self.gnn(x_dict, data.edge_index_dict)

        pred = self.classifier(
            x_dict["a"],
            x_dict["b"],
            data['a', 'to', 'b'].edge_label_index,
        )

        return pred

Answered by rusty1s

Oct 26, 2023

Ah, got it. It looks like - is not a valid character in node type/edge types names. This should work just when replacing - with _ (or something else).

View full answer

rusty1s · 2023-10-20T15:23:00Z

rusty1s
Oct 20, 2023
Maintainer

I think the issue is within your usage of update, which is different from what you would expect (see the source code in update. In particular, it will just override existing attributes (just like Dict.update() does). If you want to concatenate two data object together, you can do this via Batch.from_data_list([data1, data2]). Alternatively, this function is also present in the LinkNeighborLoader.

6 replies

jameswpm Oct 22, 2023
Author

Actually, after I posted the message, I replaced the update step (I was not sure, but I thought it was the only step I was not sure about. Thanks for confirming it; I feel I'm improving my knowledge on PyG or, at least, on how to use it less wrongly😊).

However, I still get the message RuntimeError: Node conv1__SRC_Data target conv1.SRC_Region__rev_regions__SRC_Data references nonexistent attribute SRC_Region__rev_regions__SRC_Data of conv1

Do you know how can I investigate this error further? It's not so easy for me to understand where the problem is.

My (new and without using update) graph creation process is inspired by the one I found on this project since my original data is also an XML file. Still, instead of using the networkx, I'm adding my nodes and edges into Python dictionaries and calling the following function to get my HeteroData.

def get_hetero_graph(self):
        self.data = HeteroData()
        
        # Convert nodes to PyTorch tensors and set HeteroData
        for node_type, node_list in self.nodes.items():
            self.data[node_type].num_nodes = len(node_list)
            self.data[node_type].node_id = torch.Tensor([item['id'] for item in node_list]).long()

        # convert edges to PyTorch tensors
        for edge_type, edge_list in self.edge_index.items():
            src_name, rel_name, tgt_name = edge_type.split('|') # dict keys are in form of 'node_type_1|name_of_relation|node_type_2'
            self.data[src_name, rel_name, tgt_name].edge_index = torch.tensor(edge_list, dtype=torch.long).t().contiguous()
        
        #TODO: Attributes not needed for the first experiments, since the graph nodes will be featureless
        # if consider_attributtes:
        #     # convert attributtes to PyTorch tensors

        return self.data

So, instead of getting the two Graphs (A and B) based on two XML files and merging them, I'm iterating over both XMLs and adding info into the dictionaries (self.nodes and self_edge_index).

Do you see any apparent mistake with this approach?

Thanks for the help

rusty1s Oct 23, 2023
Maintainer

Mh, do you have a minimal example to reproduce? I am not totally sure how to investigate this without it. The error is sadly pretty meaningless :(

jameswpm Oct 24, 2023
Author

Thanks for your response. Indeed, I should have provided a minimal example before.

I tried to omit the data preparation (irrelevant) and copy the node IDs directly to the code (with fewer nodes than my real data).
It's bottled with numbers, but it works and reproduces the mentioned error.
I'm trying to adapt the main MovieLens example in the following code, but I also tried other models and examples. The problem is always raised in the to_hetero function.

#PyG
import torch
from torch_geometric.data import HeteroData
import torch_geometric.transforms as T
from torch_geometric.nn import SAGEConv
from torch import Tensor
from torch_geometric.nn import to_hetero
import torch

class TestPyG():

    def __init__(self):        
        self.data = HeteroData()
        self.nodes = {}
        self.edge_index = {}

    def get_hetero_graph(self):

        self.nodes = {
            'SRC-Data': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}, {'id': 5}, {'id': 6}, {'id': 7}, {'id': 8}], 
            'SRC-Region': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}, {'id': 5}, {'id': 6}, {'id': 7}, {'id': 8}, {'id': 9}, {'id': 10}, {'id': 11}, {'id': 12}, {'id': 13}, {'id': 14}, {'id': 15}, {'id': 16}, {'id': 17}, {'id': 18}, {'id': 19}, {'id': 20}, {'id': 21}, {'id': 22}, {'id': 23}, {'id': 24}, {'id': 25}, {'id': 26}, {'id': 27}, {'id': 28}, {'id': 29}, {'id': 30}, {'id': 31}, {'id': 32}, {'id': 33}, {'id': 34}, {'id': 35}, {'id': 36}, {'id': 37}, {'id': 38}, {'id': 39}, {'id': 40}, {'id': 41}, {'id': 42}, {'id': 43}], 
            'SRC-E': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}, {'id': 5}, {'id': 6}, {'id': 7}, {'id': 8}, {'id': 9}, {'id': 10}, {'id': 11}, {'id': 12}, {'id': 13}, {'id': 14}, {'id': 15}, {'id': 16}, {'id': 17}, {'id': 18}, {'id': 19}, {'id': 20}, {'id': 21}, {'id': 22}, {'id': 23}, {'id': 24}, {'id': 25}, {'id': 26}, {'id': 27}, {'id': 28}, {'id': 29}, {'id': 30}, {'id': 31}, {'id': 32}, {'id': 33}, {'id': 34}, {'id': 35}, {'id': 36}, {'id': 37}, {'id': 38}, {'id': 39}, {'id': 40}, {'id': 41}, {'id': 42}, {'id': 43}], 
            'SRC-A': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}, {'id': 5}, {'id': 6}, {'id': 7}, {'id': 8}, {'id': 9}, {'id': 10}, {'id': 11}, {'id': 12}, {'id': 13}, {'id': 14}, {'id': 15}, {'id': 16}, {'id': 17}, {'id': 18}, {'id': 19}, {'id': 20}, {'id': 21}, {'id': 22}, {'id': 23}, {'id': 24}, {'id': 25}, {'id': 26}, {'id': 27}, {'id': 28}, {'id': 29}, {'id': 30}, {'id': 31}, {'id': 32}, {'id': 33}, {'id': 34}, {'id': 35}, {'id': 36}, {'id': 37}, {'id': 38}, {'id': 39}, {'id': 40}, {'id': 41}, {'id': 42}, {'id': 43}, {'id': 44}, {'id': 45}, {'id': 46}, {'id': 47}, {'id': 48}, {'id': 49}, {'id': 50}, {'id': 51}, {'id': 52}, {'id': 53}, {'id': 54}, {'id': 55}, {'id': 56}, {'id': 57}, {'id': 58}, {'id': 59}, {'id': 60}, {'id': 61}, {'id': 62}, {'id': 63}, {'id': 64}, {'id': 65}, {'id': 66}, {'id': 67}, {'id': 68}, {'id': 69}, {'id': 70}, {'id': 71}, {'id': 72}, {'id': 73}, {'id': 74}, {'id': 75}, {'id': 76}, {'id': 77}, {'id': 78}, {'id': 79}, {'id': 80}, {'id': 81}, {'id': 82}, {'id': 83}, {'id': 84}, {'id': 85}, {'id': 86}, {'id': 87}, {'id': 88}, {'id': 89}, {'id': 90}, {'id': 91}, {'id': 92}, {'id': 93}, {'id': 94}, {'id': 95}, {'id': 96}, {'id': 97}, {'id': 98}, {'id': 99}, {'id': 100}, {'id': 101}, {'id': 102}, {'id': 103}, {'id': 104}, {'id': 105}, {'id': 106}, {'id': 107}, {'id': 108}, {'id': 109}, {'id': 110}, {'id': 111}, {'id': 112}, {'id': 113}, {'id': 114}, {'id': 115}, {'id': 116}, {'id': 117}, {'id': 118}, {'id': 119}, {'id': 120}, {'id': 121}, {'id': 122}, {'id': 123}, {'id': 124}, {'id': 125}, {'id': 126}, {'id': 127}, {'id': 128}, {'id': 129}, {'id': 130}, {'id': 131}, {'id': 132}, {'id': 133}, {'id': 134}, {'id': 135}, {'id': 136}, {'id': 137}, {'id': 138}, {'id': 139}, {'id': 140}, {'id': 141}], 
            'SRC-C': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}, {'id': 5}, {'id': 6}, {'id': 7}, {'id': 8}, {'id': 9}, {'id': 10}, {'id': 11}, {'id': 12}, {'id': 13}, {'id': 14}, {'id': 15}, {'id': 16}, {'id': 17}, {'id': 18}, {'id': 19}, {'id': 20}, {'id': 21}, {'id': 22}, {'id': 23}, {'id': 24}, {'id': 25}, {'id': 26}, {'id': 27}, {'id': 28}, {'id': 29}, {'id': 30}, {'id': 31}, {'id': 32}, {'id': 33}, {'id': 34}, {'id': 35}, {'id': 36}, {'id': 37}, {'id': 38}, {'id': 39}, {'id': 40}, {'id': 41}, {'id': 42}, {'id': 43}, {'id': 44}, {'id': 45}, {'id': 46}, {'id': 47}, {'id': 48}, {'id': 49}, {'id': 50}, {'id': 51}, {'id': 52}, {'id': 53}, {'id': 54}, {'id': 55}, {'id': 56}, {'id': 57}, {'id': 58}, {'id': 59}, {'id': 60}, {'id': 61}, {'id': 62}, {'id': 63}, {'id': 64}, {'id': 65}, {'id': 66}, {'id': 67}, {'id': 68}, {'id': 69}, {'id': 70}, {'id': 71}, {'id': 72}, {'id': 73}, {'id': 74}, {'id': 75}, {'id': 76}, {'id': 77}, {'id': 78}, {'id': 79}, {'id': 80}, {'id': 81}, {'id': 82}, {'id': 83}, {'id': 84}, {'id': 85}, {'id': 86}, {'id': 87}, {'id': 88}, {'id': 89}, {'id': 90}, {'id': 91}, {'id': 92}, {'id': 93}, {'id': 94}, {'id': 95}, {'id': 96}, {'id': 97}, {'id': 98}, {'id': 99}, {'id': 100}, {'id': 101}], 
            'SRC-T': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}, {'id': 5}, {'id': 6}, {'id': 7}, {'id': 8}, {'id': 9}, {'id': 10}, {'id': 11}, {'id': 12}, {'id': 13}, {'id': 14}, {'id': 15}, {'id': 16}, {'id': 17}, {'id': 18}, {'id': 19}, {'id': 20}, {'id': 21}, {'id': 22}, {'id': 23}, {'id': 24}, {'id': 25}, {'id': 26}, {'id': 27}, {'id': 28}, {'id': 29}, {'id': 30}, {'id': 31}, {'id': 32}, {'id': 33}, {'id': 34}, {'id': 35}, {'id': 36}, {'id': 37}, {'id': 38}, {'id': 39}, {'id': 40}, {'id': 41}, {'id': 42}, {'id': 43}, {'id': 44}, {'id': 45}, {'id': 46}, {'id': 47}, {'id': 48}, {'id': 49}, {'id': 50}, {'id': 51}, {'id': 52}, {'id': 53}, {'id': 54}, {'id': 55}, {'id': 56}, {'id': 57}, {'id': 58}, {'id': 59}, {'id': 60}, {'id': 61}, {'id': 62}, {'id': 63}, {'id': 64}, {'id': 65}, {'id': 66}, {'id': 67}, {'id': 68}, {'id': 69}, {'id': 70}, {'id': 71}, {'id': 72}, {'id': 73}, {'id': 74}, {'id': 75}, {'id': 76}, {'id': 77}, {'id': 78}, {'id': 79}, {'id': 80}, {'id': 81}, 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            'TGT-T': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}, {'id': 5}, {'id': 6}, {'id': 7}, {'id': 8}, {'id': 9}, {'id': 10}, {'id': 11}, {'id': 12}, {'id': 13}, {'id': 14}, {'id': 15}, {'id': 16}, {'id': 17}, {'id': 18}, {'id': 19}, {'id': 20}, {'id': 21}, {'id': 22}, {'id': 23}, {'id': 24}, {'id': 25}, {'id': 26}, {'id': 27}, {'id': 28}, {'id': 29}, {'id': 30}, {'id': 31}, {'id': 32}, {'id': 33}, {'id': 34}, {'id': 35}, {'id': 36}, {'id': 37}, {'id': 38}, {'id': 39}, {'id': 40}, {'id': 41}, {'id': 42}, {'id': 43}, {'id': 44}, {'id': 45}, {'id': 46}, {'id': 47}, {'id': 48}, {'id': 49}, {'id': 50}, {'id': 51}, {'id': 52}, {'id': 53}, {'id': 54}, {'id': 55}, {'id': 56}, {'id': 57}, {'id': 58}, {'id': 59}, {'id': 60}, {'id': 61}, {'id': 62}, {'id': 63}, {'id': 64}, {'id': 65}, {'id': 66}, {'id': 67}, {'id': 68}, {'id': 69}, {'id': 70}, {'id': 71}, {'id': 72}, {'id': 73}, {'id': 74}, {'id': 75}, {'id': 76}, {'id': 77}, {'id': 78}, {'id': 79}, {'id': 80}, {'id': 81}, 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{'id': 468}, {'id': 469}, {'id': 470}, {'id': 471}, {'id': 472}, {'id': 473}, {'id': 474}, {'id': 475}, {'id': 476}, {'id': 477}, {'id': 478}, {'id': 479}, {'id': 480}, {'id': 481}, {'id': 482}, {'id': 483}, {'id': 484}, {'id': 485}, {'id': 486}, {'id': 487}, {'id': 488}, {'id': 489}, {'id': 490}, {'id': 491}, {'id': 492}, {'id': 493}, {'id': 494}, {'id': 495}, {'id': 496}, {'id': 497}, {'id': 498}, {'id': 499}, {'id': 500}, {'id': 501}, {'id': 502}, {'id': 503}, {'id': 504}, {'id': 505}, {'id': 506}, {'id': 507}, {'id': 508}, {'id': 509}, {'id': 510}, {'id': 511}, {'id': 512}, {'id': 513}, {'id': 514}, {'id': 515}, {'id': 516}, {'id': 517}, {'id': 518}, {'id': 519}, {'id': 520}, {'id': 521}, {'id': 522}, {'id': 523}, {'id': 524}, {'id': 525}, {'id': 526}, {'id': 527}, {'id': 528}, {'id': 529}, {'id': 530}, {'id': 531}, {'id': 532}, {'id': 533}, {'id': 534}, {'id': 535}, {'id': 536}, {'id': 537}, {'id': 538}, {'id': 539}, {'id': 540}, {'id': 541}, {'id': 542}, {'id': 543}, {'id': 544}, {'id': 545}, {'id': 546}, {'id': 547}, {'id': 548}, {'id': 549}, {'id': 550}, {'id': 551}, {'id': 552}, {'id': 553}, {'id': 554}, {'id': 555}, {'id': 556}, {'id': 557}, {'id': 558}, {'id': 559}, {'id': 560}, {'id': 561}, {'id': 562}, {'id': 563}, {'id': 564}, {'id': 565}, {'id': 566}, {'id': 567}, {'id': 568}], 
            'TGT-B': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}, {'id': 5}, {'id': 6}, {'id': 7}, {'id': 8}, {'id': 9}, {'id': 10}, {'id': 11}, {'id': 12}, {'id': 13}, {'id': 14}, {'id': 15}, {'id': 16}, {'id': 17}, {'id': 18}, {'id': 19}, {'id': 20}, {'id': 21}, {'id': 22}, {'id': 23}, {'id': 24}, {'id': 25}, {'id': 26}, {'id': 27}, {'id': 28}, {'id': 29}, {'id': 30}, {'id': 31}, {'id': 32}, {'id': 33}, {'id': 34}, {'id': 35}, {'id': 36}, {'id': 37}, {'id': 38}, {'id': 39}, {'id': 40}, {'id': 41}, {'id': 42}, {'id': 43}, {'id': 44}, {'id': 45}, {'id': 46}, {'id': 47}, {'id': 48}, {'id': 49}, {'id': 50}, {'id': 51}, {'id': 52}, {'id': 53}, {'id': 54}, {'id': 55}, {'id': 56}, {'id': 57}, {'id': 58}, {'id': 59}, {'id': 60}, {'id': 61}, {'id': 62}, {'id': 63}, {'id': 64}, {'id': 65}, {'id': 66}, {'id': 67}, {'id': 68}, {'id': 69}, {'id': 70}, {'id': 71}, {'id': 72}, {'id': 73}, {'id': 74}, {'id': 75}, {'id': 76}, {'id': 77}, {'id': 78}, {'id': 79}, {'id': 80}, {'id': 81}, {'id': 82}, {'id': 83}, {'id': 84}, {'id': 85}, {'id': 86}, {'id': 87}, {'id': 88}, {'id': 89}, {'id': 90}, {'id': 91}, {'id': 92}, {'id': 93}, {'id': 94}, {'id': 95}, {'id': 96}, {'id': 97}, {'id': 98}, {'id': 99}, {'id': 100}, {'id': 101}, {'id': 102}, {'id': 103}, {'id': 104}, {'id': 105}, {'id': 106}, {'id': 107}, {'id': 108}, {'id': 109}, {'id': 110}, {'id': 111}, {'id': 112}, {'id': 113}, {'id': 114}, {'id': 115}, {'id': 116}, {'id': 117}, {'id': 118}, {'id': 119}, {'id': 120}, {'id': 121}, {'id': 122}, {'id': 123}, {'id': 124}, {'id': 125}, {'id': 126}, {'id': 127}, {'id': 128}, {'id': 129}, {'id': 130}, {'id': 131}, {'id': 132}, {'id': 133}, {'id': 134}, {'id': 135}, {'id': 136}, {'id': 137}, {'id': 138}, {'id': 139}, {'id': 140}, {'id': 141}, {'id': 142}, {'id': 143}, {'id': 144}, {'id': 145}, {'id': 146}, {'id': 147}, {'id': 148}, {'id': 149}, {'id': 150}, {'id': 151}, {'id': 152}, {'id': 153}, {'id': 154}, {'id': 155}, {'id': 156}, {'id': 157}, {'id': 158}, {'id': 159}, {'id': 160}, {'id': 161}, {'id': 162}, {'id': 163}, {'id': 164}, {'id': 165}, {'id': 166}, {'id': 167}, {'id': 168}, {'id': 169}, {'id': 170}, {'id': 171}, {'id': 172}, {'id': 173}, {'id': 174}, {'id': 175}, {'id': 176}, {'id': 177}, {'id': 178}, {'id': 179}, {'id': 180}, {'id': 181}, {'id': 182}, {'id': 183}, {'id': 184}, {'id': 185}, {'id': 186}, {'id': 187}, {'id': 188}, {'id': 189}, {'id': 190}, {'id': 191}, {'id': 192}, {'id': 193}, {'id': 194}, {'id': 195}, {'id': 196}, {'id': 197}, {'id': 198}, {'id': 199}, {'id': 200}, {'id': 201}, {'id': 202}, {'id': 203}, {'id': 204}, {'id': 205}, {'id': 206}, {'id': 207}, {'id': 208}, {'id': 209}, {'id': 210}, {'id': 211}, {'id': 212}, {'id': 213}, {'id': 214}, {'id': 215}, {'id': 216}, {'id': 217}, {'id': 218}, {'id': 219}, {'id': 220}, {'id': 221}, {'id': 222}, {'id': 223}, {'id': 224}, {'id': 225}, {'id': 226}, {'id': 227}, {'id': 228}, {'id': 229}, {'id': 230}, {'id': 231}, {'id': 232}, {'id': 233}, {'id': 234}, {'id': 235}, {'id': 236}, {'id': 237}, {'id': 238}, {'id': 239}, {'id': 240}, {'id': 241}, {'id': 242}, {'id': 243}, {'id': 244}, {'id': 245}, {'id': 246}, {'id': 247}, {'id': 248}, {'id': 249}, {'id': 250}, {'id': 251}, {'id': 252}, {'id': 253}, {'id': 254}, {'id': 255}, {'id': 256}, {'id': 257}, {'id': 258}, {'id': 259}, {'id': 260}, {'id': 261}, {'id': 262}, {'id': 263}, {'id': 264}, {'id': 265}, {'id': 266}, {'id': 267}, {'id': 268}, {'id': 269}, {'id': 270}, {'id': 271}, {'id': 272}, {'id': 273}, {'id': 274}, {'id': 275}, {'id': 276}, {'id': 277}, {'id': 278}, {'id': 279}, {'id': 280}, {'id': 281}, {'id': 282}, {'id': 283}, {'id': 284}, {'id': 285}, {'id': 286}, {'id': 287}, {'id': 288}, {'id': 289}], 
            'TGT-Start': [{'id': 0}, {'id': 1}, {'id': 2}, {'id': 3}, {'id': 4}, {'id': 5}, {'id': 6}, {'id': 7}, {'id': 8}], 
            'SRC-Exit': [{'id': 0}, {'id': 1}]
        }

        self.edge_index = {
            'SRC-Data|regions|SRC-Region': [[0, 0], [1, 1], [2, 5], [3, 8], [4, 13], [5, 16], [6, 24], [7, 28], [8, 36]], 
            'SRC-Region|react|SRC-E': [[0, 0], [1, 1], [2, 2], [4, 3], [3, 4], [5, 5], [6, 6], [7, 7], [8, 8], [9, 9], [10, 10], [11, 11], [12, 12], [13, 13], [14, 14], [15, 15], [16, 16], [17, 17], [18, 18], [19, 19], [20, 20], [21, 21], [22, 22], [23, 23], [24, 24], [25, 25], [27, 26], [26, 27], [28, 28], [29, 29], [31, 30], [32, 31], [33, 32], [34, 33], [35, 34], [30, 35], [36, 36], [37, 37], [38, 38], [39, 39], [40, 40], [41, 41], [42, 42], [43, 43]], 
            'SRC-Region|react|SRC-A': [[0, 0], [0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [1, 11], [1, 12], [2, 13], [2, 14], [2, 15], [2, 16], [2, 17], [4, 18], [4, 19], [3, 20], [3, 21], [3, 22], [3, 23], [3, 24], [3, 25], [3, 26], [5, 27], [5, 28], [6, 29], [6, 30], [6, 31], [6, 32], [6, 33], [6, 34], [7, 35], [7, 36], [7, 37], [7, 38], [8, 39], [8, 40], [8, 41], [8, 42], [8, 43], [8, 44], [8, 45], [8, 46], [8, 47], [8, 48], [8, 49], [8, 50], [8, 51], [9, 52], [10, 53], [11, 54], [12, 55], [13, 56], [13, 57], [14, 58], [14, 59], [14, 60], [14, 61], [14, 62], [15, 63], [15, 64], [15, 65], [15, 66], [15, 67], [15, 68], [16, 69], [16, 70], [17, 71], [17, 72], [17, 73], [17, 74], [18, 75], [18, 76], [18, 77], [18, 78], [18, 79], [19, 80], [19, 81], [20, 82], [21, 83], [22, 84], [23, 85], [24, 86], [24, 87], [25, 88], [25, 89], [25, 90], [25, 91], [25, 92], [25, 93], [27, 94], [27, 95], [26, 96], [26, 97], [26, 98], [26, 99], [28, 100], [28, 101], [29, 102], [29, 103], [29, 104], [29, 105], [29, 106], [31, 107], [31, 108], [32, 109], [32, 110], [32, 111], [33, 112], [33, 113], [33, 114], [34, 115], [34, 116], [34, 117], [35, 118], [35, 119], [35, 120], [30, 121], [30, 122], [30, 123], [30, 124], [36, 125], [36, 126], [36, 127], [36, 128], [36, 129], [36, 130], [37, 131], [38, 132], [39, 133], [40, 134], [41, 135], [41, 136], [41, 137], [42, 139], [42, 138], [43, 140], [43, 141]], 
            'SRC-Region|react|SRC-C': [[0, 0], [0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [0, 11], [0, 12], [0, 13], [0, 14], [0, 15], [2, 16], [2, 17], [2, 18], [2, 19], [2, 20], [2, 21], [2, 22], [2, 23], [3, 24], [3, 25], [3, 26], [3, 27], [3, 28], [3, 29], [3, 30], [6, 31], [6, 32], [6, 33], [6, 34], [6, 35], [6, 36], [6, 37], [6, 38], [7, 39], [7, 40], [7, 41], [7, 42], [7, 43], [7, 44], [7, 45], [7, 46], [8, 47], [8, 48], [9, 49], [9, 50], [10, 51], [10, 52], [11, 53], [11, 54], [12, 55], [12, 56], [14, 57], [14, 58], [14, 59], [14, 60], [14, 61], [14, 62], [15, 63], [15, 64], [15, 65], [15, 66], [15, 67], [15, 68], [15, 69], [15, 70], [15, 71], [17, 72], [17, 73], [17, 74], [17, 75], [17, 76], [19, 77], [20, 78], [20, 79], [21, 80], [21, 81], [22, 82], [22, 83], [23, 84], [23, 85], [25, 86], [25, 87], [25, 88], [25, 89], [25, 90], [25, 91], [25, 92], [25, 93], [27, 94], [26, 95], [26, 96], [26, 97], [26, 98], [26, 99], [31, 100], [30, 101]], 
            'SRC-E|noinclude|SRC-T': [[0, 0], [1, 68], [2, 71], [3, 101], [4, 119], [5, 129], [6, 132], [7, 172], [8, 204], [9, 241], [10, 248], [11, 255], [12, 262], [13, 269], [14, 272], [15, 299], [16, 326], [17, 343], [18, 346], [19, 356], [20, 361], [21, 364], [22, 372], [23, 380], [24, 386], [25, 388], [26, 430], [27, 445], [28, 446], [29, 449], [30, 461], [31, 462], [32, 471], [33, 480], [34, 489], [35, 498], [36, 511], [37, 521], [38, 527], [39, 533], [40, 539], [41, 551], [42, 557], [43, 565]], 
            'SRC-A|include|SRC-T': [[0, 0], [0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [0, 11], [0, 12], [1, 13], [1, 15], [1, 16], [1, 17], [1, 18], [2, 21], [2, 22], [2, 23], [2, 24], [2, 25], [3, 27], [3, 29], [3, 30], [3, 31], [3, 32], [4, 34], [4, 36], [4, 37], [4, 38], [4, 39], [5, 43], [5, 45], [5, 46], [6, 44], [6, 47], [6, 48], [7, 50], [7, 51], [7, 52], [7, 53], [7, 54], [8, 49], [8, 57], [8, 58], [8, 59], [8, 60], [9, 63], [10, 64], [11, 67], [11, 68], [12, 69], [13, 70], [13, 71], [14, 72], [14, 73], [14, 74], [14, 75], [14, 76], [15, 78], [15, 80], [15, 81], [15, 82], [15, 83], [16, 84], [16, 87], [16, 88], [16, 89], [16, 90], [17, 93], [17, 94], [17, 95], [17, 96], [17, 97], [18, 100], [18, 101], [19, 102], [20, 103], [20, 104], [20, 105], [20, 106], [20, 107], [21, 111], [21, 112], [21, 113], [21, 114], [21, 115], [22, 121], [23, 124], [24, 119], [25, 109], [26, 116], [27, 129], [27, 130], [28, 131], [29, 132], [29, 133], [29, 134], [29, 135], [30, 136], [30, 137], [30, 138], [30, 139], [30, 140], [30, 141], [31, 142], [31, 145], [31, 146], [31, 147], [31, 148], [31, 149], [32, 151], [32, 153], [32, 154], [32, 155], [32, 156], [32, 157], [33, 159], [33, 161], [33, 162], [33, 163], [33, 164], [33, 165], [34, 166], [34, 169], [34, 170], [34, 171], [35, 173], [35, 175], [35, 176], [35, 177], [35, 178], [35, 179], [35, 180], [36, 174], [36, 183], [36, 184], [36, 185], [36, 186], [36, 187], [36, 188], [36, 189], [37, 196], [37, 197], [37, 198], [37, 199], [38, 195], [38, 200], [38, 201], [38, 202], [38, 203], [39, 204], [39, 205], [39, 206], [39, 207], [39, 208], [39, 209], [39, 210], [39, 211], [39, 212], [39, 213], [40, 214], [40, 216], [41, 217], [41, 218], [42, 219], [42, 220], [43, 221], [43, 222], [44, 215], [44, 223], [44, 224], [44, 225], [44, 226], [45, 230], [45, 231], [45, 232], [45, 233], [46, 228], [47, 235], [48, 229], [48, 237], [49, 238], [50, 236], [50, 239], [51, 240], [52, 241], [52, 242], [52, 243], [52, 244], [52, 245], [53, 248], [53, 249], [53, 250], [53, 251], [53, 252], [54, 255], [54, 256], [54, 257], [54, 258], [54, 259], [55, 262], [55, 263], [55, 264], [55, 265], [55, 266], [56, 269], [56, 270], [57, 271], [58, 272], [58, 273], [59, 274], [59, 276], [59, 277], [59, 278], [59, 279], [60, 282], [60, 283], [60, 284], [60, 285], [60, 286], [61, 290], [61, 291], [61, 292], [62, 287], [62, 293], [62, 294], [62, 295], [62, 296], [63, 300], [63, 301], [63, 302], [63, 303], [63, 304], [63, 305], [64, 308], [64, 309], [64, 310], [64, 311], [64, 312], [64, 313], [65, 316], [66, 317], [67, 322], [67, 323], [68, 324], [68, 325], [69, 327], [70, 326], [70, 328], [71, 329], [71, 330], [71, 331], [71, 332], [72, 336], [72, 337], [72, 338], [72, 339], [73, 344], [73, 345], [74, 333], [74, 342], [75, 346], [75, 347], [76, 348], [77, 349], [78, 350], [79, 351], [80, 352], [80, 353], [81, 354], [81, 355], [82, 357], [82, 358], [82, 359], [82, 360], [82, 361], [83, 364], [83, 365], [83, 366], [83, 367], [83, 368], [84, 371], [84, 372], [84, 373], [84, 374], [84, 375], [85, 378], [85, 379], [85, 380], [85, 381], [85, 382], [86, 385], [86, 386], [87, 387], [88, 388], [88, 389], [89, 390], [89, 391], [90, 392], [90, 393], [90, 394], [90, 395], [90, 396], [90, 397], [91, 399], [91, 401], [91, 402], [91, 403], [91, 404], [91, 405], [92, 408], [92, 409], [92, 410], [92, 411], [92, 412], [92, 413], [93, 414], [93, 417], [93, 418], [93, 419], [93, 420], [93, 421], [94, 424], [94, 425], [94, 426], [95, 427], [95, 428], [95, 429], [96, 431], [96, 432], [96, 433], [96, 434], [97, 437], [97, 438], [97, 439], [97, 440], [98, 443], [99, 444], [100, 446], [100, 447], [101, 448], [102, 449], [102, 450], [103, 451], [104, 452], [105, 453], [106, 454], [107, 455], [107, 456], [107, 457], [108, 458], [108, 459], [108, 460], [109, 462], [109, 463], [109, 464], [109, 465], [109, 466], [110, 467], [110, 469], [111, 468], [111, 470], [112, 471], [112, 472], [112, 473], [112, 474], [112, 475], 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534], [277, 535], [277, 536], [279, 538], [279, 539], [279, 540], [279, 541], [279, 542], [279, 543], [280, 546], [280, 547], [280, 549], [280, 550], [283, 565], [282, 548], [282, 551], [282, 554], [282, 557], [282, 559], [281, 545], [281, 552], [281, 555], [281, 558], [286, 560], [286, 562], [286, 563], [287, 561], [287, 564], [285, 553], [285, 556], [285, 566], [285, 567], [285, 568], [265, 512], [265, 514], [265, 515], [265, 537], [265, 544], [267, 513], [272, 530], [269, 516], [273, 523], [266, 511]], 
            'SRC-A|to|TGT-B': [[0, 1], [1, 2], [2, 6], [3, 8], [4, 11], [5, 16], [6, 17], [7, 19], [8, 18], [9, 24], [10, 25], [11, 28], [13, 30], [18, 32], [19, 33], [14, 31], [15, 36], [16, 38], [17, 43], [12, 29], [20, 47], [21, 51], [22, 57], [23, 60], [24, 55], [25, 49], [26, 52], [27, 81], [29, 65], [30, 66], [31, 68], [32, 72], [33, 75], [34, 77], [28, 67], [35, 84], [36, 85], [37, 94], [38, 93], [39, 96], [40, 97], [52, 100], [41, 99], [53, 106], [42, 104], [54, 111], [43, 109], [55, 115], [44, 98], [45, 121], [46, 119], [47, 123], [48, 120], [49, 125], [50, 124], [51, 126], [56, 128], [58, 131], [59, 132], [60, 136], [61, 140], [62, 137], [57, 129], [63, 145], [64, 148], [65, 151], [66, 152], [67, 157], [68, 158], [69, 161], [71, 162], [72, 166], [73, 171], [74, 163], [70, 160], [75, 172], [80, 174], [81, 175], [76, 173], [82, 179], [77, 178], [83, 185], [78, 184], [84, 190], [79, 189], [85, 194], [86, 218], [88, 198], [89, 199], [94, 202], [95, 203], [90, 201], [91, 207], [92, 211], [93, 212], [87, 200], [96, 219], [97, 222], [98, 225], [99, 226], [100, 231], [102, 233], [107, 235], [108, 236], [103, 234], [109, 242], [110, 243], [111, 244], [104, 240], [112, 247], [113, 248], [114, 249], [105, 245], [115, 252], [116, 253], [117, 254], [106, 250], [118, 256], [119, 257], [120, 258], [101, 232], [121, 262], [122, 261], [123, 264], [124, 263], [125, 265], [126, 267], [131, 271], [127, 272], [132, 275], [128, 269], [133, 277], [129, 273], [134, 279], [130, 266], [135, 280], [136, 283], [139, 282], [138, 281], [140, 286], [141, 287], [137, 285]], 
            'SRC-A|regions|SRC-Region': [[11, 2], [12, 3], [13, 4], [27, 6], [28, 7], [40, 9], [41, 10], [42, 11], [43, 12], [56, 14], [57, 15], [69, 17], [70, 18], [75, 19], [76, 20], [77, 21], [78, 22], [79, 23], [86, 25], [87, 26], [89, 27], [100, 29], [101, 30], [102, 31], [103, 32], [104, 33], [105, 34], [106, 35], [126, 37], [127, 38], [128, 39], [129, 40], [130, 41], [136, 42], [136, 43]], 
            'SRC-Region|react|SRC-Exit': [[3, 0], [14, 1]], 
            'SRC-Exit|include|SRC-T': [[0, 110], [0, 118], [0, 127], [0, 128], [1, 275]], 
            'SRC-T|target|SRC-Exit': [[110, 0], [118, 0], [127, 0], [128, 0], [275, 1]]
        }
        
        # Convert nodes to PyTorch tensors and set HeteroData
        for node_type, node_list in self.nodes.items():
            self.data[node_type].num_nodes = len(node_list)
            self.data[node_type].node_id = torch.Tensor([item['id'] for item in node_list]).long()

        # convert edges to PyTorch tensors
        for edge_type, edge_list in self.edge_index.items():
            src_name, rel_name, tgt_name = edge_type.split('|')
            self.data[src_name, rel_name, tgt_name].edge_index = torch.tensor(edge_list, dtype=torch.long).t().contiguous()
        
        return self.data
    

test = TestPyG()
graph = test.get_hetero_graph()
graph = T.ToUndirected()(graph)

# print(f'Node types:\n {graph.node_types}')
# print(f'Edge types:\n {graph.edge_types}')
# print('Info on the target link')
# print(graph['SRC-A', 'to', 'TGT-B'].num_edges)
# print(graph['TGT-B', 'rev_to', 'SRC-A'].num_edges)

print(graph)

if not graph.validate():
    raise "The graph is not valid"

transform = T.RandomLinkSplit(
    is_undirected=False,
    # training (80%), validation (10%), and testing edges (10%).
    num_val=0.1,
    num_test=0.1,
    # Across the training edges, we use 70% of edges for message passing,
    # and 30% of edges for supervision.
    disjoint_train_ratio=0.3,
    # We further want to generate fixed negative edges for evaluation with a ratio of 2:1.
    # Negative edges during training will be generated on-the-fly.
    neg_sampling_ratio=2.0,
    add_negative_train_samples=True,
    edge_types=[('SRC-A', 'to', 'TGT-B')],
    rev_edge_types=[('TGT-B', 'rev_to', 'SRC-A')])

train, valid, test = transform(graph)


class GNN(torch.nn.Module):
    def __init__(self, hidden_channels):
        super().__init__()
        self.conv1 = SAGEConv(hidden_channels, hidden_channels)
        self.conv2 = SAGEConv(hidden_channels, hidden_channels)

    def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
        # A 2-layer GNN computation graph.
        # `ReLU` is the non-lineary function used in-between.
        x = self.conv1(x, edge_index).relu()
        x = self.conv2(x, edge_index)
        return x

# Our final classifier applies the dot-product between source and destination
# node embeddings to derive edge-level predictions:


class Classifier(torch.nn.Module):
    def forward(self, x_i: Tensor, x_j: Tensor, edge_label_index: Tensor) -> Tensor:
        # Convert node embeddings to edge-level representations:
        edge_feat_a = x_i[edge_label_index[0]]
        edge_feat_b = x_j[edge_label_index[1]]

        # Apply dot-product to get a prediction per supervision edge:
        return (edge_feat_a * edge_feat_b ).sum(dim=-1)


class Model(torch.nn.Module):
    def __init__(self, hidden_channels):
        super().__init__()
        # initial embeddings
        self.x_i_emb = torch.nn.Embedding(
            graph["SRC-A"].num_nodes, hidden_channels)
        self.x_j_emb = torch.nn.Embedding(
            graph["TGT-B"].num_nodes, hidden_channels)

        # Instantiate homogeneous GNN:
        self.gnn = GNN(hidden_channels)

        # Convert GNN model into a heterogeneous variant:

        self.gnn = to_hetero(self.gnn, metadata=graph.metadata())

        self.classifier = Classifier()

    def forward(self, data) -> Tensor:
        x_dict = {
            "SRC-A": self.x_i_emb(data["SRC-A"].node_id),
            "TGT-B": self.x_j_emb(data["TGT-B"].node_id),
        }

        # `x_dict` holds feature matrices of all node types
        # `edge_index_dict` holds all edge indices of all edge types
        x_dict = self.gnn(x_dict, data.edge_index_dict)

        pred = self.classifier(
            x_dict["SRC-A"],
            x_dict["TGT-B"],
            data['SRC-A', 'to', 'TGT-B'].edge_label_index,
        )

        return pred


model = Model(hidden_channels=64)

print(model)

Just running it, it raises: RuntimeError: Node conv1__SRC_Data target conv1.SRC_Region__rev_regions__SRC_Data references nonexistent attribute SRC_Region__rev_regions__SRC_Data of conv1

rusty1s Oct 26, 2023
Maintainer

Ah, got it. It looks like - is not a valid character in node type/edge types names. This should work just when replacing - with _ (or something else).

Answer selected by jameswpm

jameswpm Oct 30, 2023
Author

Oh! It was much simpler than I thought. It was just a typo, as usual.

Now, the model is well-defined and being printed without problems. Thanks for the help! Are these not allowed characters documented anywhere? I can contribute to the documentation with a PR if not. :-)

Can you help me with an extra question?

Most of the examples that I found in the PyG community regarding Link Prediction always consider a few node types (like users and movies or customers and products). Does my approach of having many node types and many edge types and trying to predict the existence of one of these edges look reasonable?

The only almost similar example I found was this one on Kaggle, but despite the number of node types and edge types, they are ignoring most parts of nodes in the embedding part, so I think that I can be in the wrong direction.

rusty1s Nov 2, 2023
Maintainer

I think we can make the logic here https://github.com/pyg-team/pytorch_geometric/blob/master/torch_geometric/nn/to_hetero_transformer.py#L164 much more robust to include like characters such as -. Happy to accept a PR on this one if you find some time :)

Yes, this looks totally correct. You use the whole graph to learn node representations to guide you whether there exists a link between two nodes of a single edge type - that's the classic GNN+LP approach :)

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