Knowledge Graph vs Graph Neural Network
| Aspect | Graph Neural Network (GNN) | Knowledge Graph (KG) |
|---|---|---|
| Definition | A neural network architecture that operates on graph-structured data | A structured representation of knowledge in the form of entities and relationships |
| Input | Graphs with nodes, edges, and features (often numeric or categorical) | Triples: (subject, predicate, object), like (“Product A”, “is_variant_of”, “Product B”) |
| Purpose | Learn representations (embeddings) for nodes, edges, or the entire graph | Store and query semantic information, often used for reasoning and inference |
| Learning? | Yes, end-to-end training using backpropagation | Not inherently “learning-based”, though can be used alongside ML models |
| Representation | Dense vectors learned through message passing between nodes | Symbolic, usually stored as RDF triples or a property graph (Neo4j, etc.) |
| Examples | Node classification, link prediction, graph classification | Entity linking, semantic search, recommendation |
| Applications | Social Nw analysis: Predict user behavior, friend recommendation. Molecule property prediction: For drug discovery (moledules as graphs) . Recommendation systems: Personalized recommendations by modeling user-item interaction graph. Fraud detection: Detect anomalies in transaction networks. | Search engines: Google’s Knowledge Panel (e.g., “Barack Obama → Born In → Hawaii”). Chatbots & QA systems: Semantic understanding and retrieval. Product information management: Unified representation of items and their relationships. Personalization engines: Capture customer interests and intent |
| Tools & Libraries | PyTorch Geometric, DGL, TensorFlow GNN. Models: GCN, GAT, GraphSAGE, R-GCN, LightGCN | Graph DBs: Neo4j, Amazon Neptune, RDFLib. Ontology tools: Protégé |
In Ecommerce:
| Aspect | Graph Neural Network (GNN) | Knowledge Graph (KG) |
|---|---|---|
| What they do | Learn powerful embeddings over graphs (users–products, product–product similarity, etc.). Predict next product to buy, bundle offers, or cross-sell suggestions | Unify information across systems (products, customers, categories, brands). Provide semantic reasoning (e.g., “Lingerie is a type of women’s underwear”). Enable personalized recommendations, faceted search, and chatbots |
| Example Use Cases | Session-based recommendation: Use GNNs (e.g., GAT, GCN) to predict what user will click next. Fraud detection: Model user–transaction–device graph to flag suspicious behavior. User embedding learning: Capture deep user interests from interaction patterns. Link prediction: Identify potential connections like “also bought” or “co-viewed” | Product enrichment: Build a KG linking SKUs → categories → attributes → usage occasions. Semantic search: “Show me affordable red dresses under $50” → link price, color, category. Customer interest graph: Track product interactions → build a user–product graph. Multi-language/catalog linking: Map equivalent products across locales |
| Use Case | Knowledge Graph | Graph Neural Network |
|---|---|---|
| Entity Linking & Search | ✅ Yes | ❌ Not designed for this |
| Recommendation Systems | ✅ With rules | ✅ With learning & embeddings |
| Fraud Detection | ❌ | ✅ Especially on transaction graphs |
| Personalized Experience | ✅ With semantic relations | ✅ With behavior modeling |
| Customer Segmentation | ✅ Hierarchical structure | ✅ Graph clustering |
Combine GNNs + KGs for Advanced Applications:
- Knowledge-Enhanced Recommendations:
- Use KG as input for GNN (e.g., KGAT: Knowledge Graph Attention Network)
- Products and users embedded into a semantic space for improved relevance
- Explainable AI:
- Use KG paths to explain why a product was recommended: “Because you liked X, which is similar to Y”
Example Use Case 1: KG in E-commerce
Business Problem An ecommerce company face:
- Product data inconsistency (e.g., same T-shirt under multiple SKUs for different colors).
- Customers searching for “black summer dress” get incomplete results.
- Hard to recommend complementary items (handbags with dresses, etc.).
Solution Build a KG that can connects related entities to unify and enrich product data.
You can build a KG from the following input in the form of (subject, predicate, object):
(SKU123, "is_variant_of", SKU456)
(SKU123, "has_color", "black")
(CustomerA, "purchased", SKU123)
(SKU123, "belongs_to", "summer collection")
(SKU123, "belongs_to", "dresses")
(SKU123, "frequently_bought_with", SKU456)
And then store it in Neo4j or Amazon Neptune for querying. This can help with:
- Semantic Search:
- User types: “black cotton summer dress under $50”
- The KG connects synonyms & related categories (e.g., “cocktail” → “party dress”).
- Cross-sell & Up-sell:
- Via paths connected with relationships: “purchased”, “frequently_bought_with”.
- Then recommend handbags that were co-purchased.
- Product QA & Chatbots:
- Answers questions like “What is the care instruction for this?” by linking to attribute nodes.
Tools used:
- Neo4j graph database to store and query KG.
- NLP pipelines to auto-extract relationships from product descriptions.
- Graph algorithms (like PageRank or community detection) to rank popular products in segments.
Example Use Case 2: GNN in E-commerce Business Problem The same ecommerce company wants to improve personalized recommendations beyond collaborative filtering. They want to capture:
- Sequential clicks (session-based).
- Product-Product similarity (from user co-interactions).
- Social signals (influencer likes, etc.)
Solution Use a GNN to predict the next likely purchase.
Graph Structure:
- Nodes: Customers, Products.
- Edges:
- viewed, added_to_card, purchased.
- Weighted by recency & frequency.
Models:
- GNN type: GraphSAGE or GAT (Graph Attention Network).
- Task: Link Prediction (predict edge from a customer to new products).
Results (Potential Impact):
- Improved CTR and conversion on recommendations by +12% over classic matrix factorization.
- More robust to cold-start: new products get embedded via graph strcuture.
Tools used
- PyTorch Geometric for GNNs.
- Features: Recency & Frequency of interactions, product category embeddings.
- Trained with historical sessions.
References
Academic Papers
Knowledge Graphs for E-commerce:
- Alibaba’s Product Knowledge Graph: “Building a Product Knowledge Graph from E-commerce Websites”
- Amazon Product Graph: large-scale taxonomy & entity linking.
Graph Neural Networks in Recommender Systems:
- PinSage (Pinterest’s large-scale GNN for recommendations):
- Graph Neural Networks for Recommender Systems (survey):.
- Knowledge Graph Attention Network (KGAT) for recommendation:.
Libaries & Tools
- KG:
- Neo4j, Amazon Neptune, Grakn, RDFLib (Python).
- Stanford’s Protege for ontology design.
- GNN:
- PyTorch Geometric:
- DGL (Deep Graph Library):
- TensorFlow GNN:
Blogs & Tutorials
- Graph ML for recommendations (Pinterest):
- Alibaba’s KG pipeline:
- GNN explained simply by Jay Alammar:
- Neo4j E-commerce KG demo:
Open Code Examples
- Session-based GNN recommendation (PyG): Graph-based session recommendation)
- KGAT implementation (RecBole): (standard toolkit with KG-enhanced recs)