In the era dominated by language models and machine learning, the importance of structured data is growing rapidly: social networks, biological relationships, and business connections. This data is represented in the form of graphs, which are often not homogeneous: they contain nodes of different types (e.g., people, products, companies) and different types of edges (e.g., “purchased”, “recommended”, “works at”). Processing such heterogeneous graphs requires specialized methods.
What are heterogeneous graphs?
A heterogeneous graph is a structure in which:
- there are multiple types of nodes and edges,
- each type can have different features and serve a different role,
- relationships are semantically complex (e.g., “watched”, “reviewed”, “produced”).
This reflects many real-world systems: financial networks, social media platforms, or encyclopedic knowledge bases (like Wikipedia).
What is prompt learning?
Prompt learning is a technique that gained popularity thanks to large language models like GPT. Instead of training a model from scratch for each task, we give it a “prompt” that guides it to perform a specific objective, such as classification, generation, or answering questions.
Example prompt: “Classify the following text as positive or negative: …”
In the context of graphs, prompt learning is less straightforward but very promising: it allows activating different model behaviors without changing its architecture.
What makes HGMP unique?
HGMP is a method that combines:
- Task unification – every task (e.g., node classification, edge prediction, ranking) is reformulated as a graph-level prompt.
- Contrastive pre-training – the model learns to distinguish between different nodes and relationships by contrasting their representations.
- Feature prompts – specialized vectors that fine-tune the features of different node types, improving adaptation to data heterogeneity.
Thanks to this, HGMP can perform multiple tasks on a graph simultaneously, maintaining high performance.
How does HGMP work step-by-step?
- Data representation – the graph is transformed into a unified format where each node has a clearly defined type and features.
- Contrastive pre-training – the model compares different node and edge representations, learning to detect key similarities and differences.
- Prompt learning – each node type gets its own feature prompt, providing better alignment with the node’s semantics.
- Downstream tasks – at inference time, the model uses prompts to carry out specific tasks (e.g., node prediction), without needing to train a separate model.
Why is this a breakthrough?
HGMP demonstrates that prompt learning can be effectively applied not only to text models, but also to graph-based models – in scenarios involving:
- heterogeneous structures,
- multi-task objectives,
- limited labeled data.
This paves the way for more universal graph models, which can be applied in:
- financial network analysis,
- gene relationship inference,
- knowledge understanding in graph databases.
Advanced insight
HGMP builds on solid mathematical foundations:
- it uses contrastive loss to maximize the distance between representations of unrelated nodes,
- feature prompts function as parameterized functions embedded in the representation space,
- the entire model is trained end-to-end.
Summary
The “HGMP” publication introduces not only an innovative method, but also signals a new era in graph analysis. By combining prompt learning with contrastive pre-training and feature adaptation, the authors have created a universal tool that may redefine how we process relational data.
For practitioners and researchers, it’s a clear signal: graphs are not just a data structure, but an environment where LLM-inspired techniques can be applied creatively – yielding impressive results.