Document dangling CITES targets and fix PyG example to filter them

README.md

@@ -128,6 +128,14 @@ Relation semantics:
 - `BELONGS_TO` — `Paper → Concept`
 - `COLLABORATES_WITH` — `Author → Author` (co-authorship; symmetric, may appear in both directions)
 
+**Dangling `CITES` targets.** Each scale is built from a Computer Science slice
+of OpenAlex, so the `nodes` table only contains CS papers (plus their authors,
+venues, and concepts). However, those CS papers may cite papers from outside
+CS — those external papers appear as `target` in `CITES` edges but are **not**
+present in the `nodes` table. Filter or add placeholder nodes as appropriate
+for your task. Sources are always present in `nodes`; only `CITES` targets can
+be dangling.
+
 ## Usage
 
 ### Load with the `datasets` library
@@ -159,7 +167,7 @@ edges = pd.read_parquet("hf://datasets/jugalgajjar/CS-Knowledge-Graph-Dataset/10
 ### Build a PyTorch Geometric graph
 
 ```python
-import json
+import numpy as np
 import torch
 from torch_geometric.data import HeteroData
 from datasets import load_dataset
@@ -168,6 +176,7 @@ scale = "10k"
 nodes = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", f"{scale}_nodes", split="train").to_pandas()
 edges = load_dataset("jugalgajjar/CS-Knowledge-Graph-Dataset", f"{scale}_edges", split="train").to_pandas()
 
+# Build per-type id -> contiguous index maps
 data = HeteroData()
 id_maps = {}
 for ntype, group in nodes.groupby("node_type"):
@@ -175,17 +184,23 @@ for ntype, group in nodes.groupby("node_type"):
     id_maps[ntype] = {nid: i for i, nid in enumerate(ids)}
     data[ntype].num_nodes = len(ids)
 
-# infer source/target type from the node_id prefix
+# Each node_id is prefixed with its type
 type_from_prefix = {"paper": "Paper", "author": "Author", "venue": "Venue", "concept": "Concept"}
 def ntype_of(nid: str) -> str:
     return type_from_prefix[nid.split("_", 1)[0]]
 
+# Drop CITES edges whose target isn't in the node set (cross-domain citations).
+node_id_set = set(nodes["node_id"])
+edges = edges[edges["target"].isin(node_id_set)].reset_index(drop=True)
+
 for relation, group in edges.groupby("relation"):
     src_type = ntype_of(group["source"].iloc[0])
     dst_type = ntype_of(group["target"].iloc[0])
-    src = group["source"].map(id_maps[src_type]).to_numpy()
-    dst = group["target"].map(id_maps[dst_type]).to_numpy()
-    data[src_type, relation, dst_type].edge_index = torch.tensor([src, dst], dtype=torch.long)
+    src = group["source"].map(id_maps[src_type]).to_numpy(dtype=np.int64)
+    dst = group["target"].map(id_maps[dst_type]).to_numpy(dtype=np.int64)
+    data[src_type, relation, dst_type].edge_index = torch.from_numpy(np.stack([src, dst]))
+
+print(data)
 ```
 
 ## Raw SQLite databases