216 lines
12 KiB
Markdown
216 lines
12 KiB
Markdown
You're absolutely on point considering **Temporal Graph Networks (TGNs)** as a mechanism to maintain coherence in your HCFS DB as context evolves. These models are explicitly designed for **time-evolving graph data**—perfect for tracking nodes (e.g., paths or context blobs) and edges (e.g. inheritance, version links) over time.
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---
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## 🧠 1. Temporal Graph Networks (TGNs) — an ideal fit for evolving context
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* **TGN architecture** processes a stream of time-stamped events (e.g. new context blob at path, version update, parent-child change) and updates **node-level memory embeddings**, allowing representation of each entity’s evolving state ([arXiv][1], [GitHub][2]).
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* These embeddings capture both structural and temporal dynamics—so your system can reason about past context versions, detect divergence, and flag incoherence across agents over time.
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Other relevant models include:
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* **Know‑Evolve**: modeling non-linear evolution of relationships over time as point processes benchmarked in knowledge graphs ([arXiv][3], [Proceedings of Machine Learning Research][4]).
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* **EvolveGCN**: uses an RNN to evolve GCN parameters rather than individual embeddings, helpful in link or version prediction scenarios ([arXiv][5]).
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---
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## 🎯 2. How temporal graph modeling applies to HCFS
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Imagine your HCFS DB as a **temporal knowledge graph**:
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* **Nodes**: context blobs, path nodes, agents
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* **Edges**: version-of, parent-path, contributor-of, merge-from
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* **Timestamps** on each event (e.g. blob creation/update, merge operation)
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A TGN can ingest an **event stream** like:
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```
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(t₁) blob₁ created at /project/x, version=1
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(t₂) blob₂ created at same path, version=2 (parent blob₁)
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(t₃) agent A publishes blob₃ at /project/x
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(t₄) merge blob₂, blob₃ => blob₄
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...
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```
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As events flow in, TGN updates embeddings per node. Downstream you can:
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* Detect **drifts** or divergence across path histories
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* Cluster similar changes and summarize coherent updates
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* **Predict coherence issues**: e.g. if agents diverge, versus converge
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---
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## 📚 Peer-Reviewed Foundations
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* **Temporal GNN survey** outlines formal settings, tasks, and taxonomy for TGNNs, helping in understanding processing models, discretization, and evaluation frameworks ([arXiv][6], [arXiv][7]).
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* **Dynamic Neighborhood Selection (CATGNN)** handles evolving neighborhood interactions via memory modules—analogous to how sibling path nodes or contributors change over time ([SpringerLink][8]).
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* The broader literature on temporal knowledge graphs and reasoning frameworks (e.g., **EvoKG**, **WGP‑NN**) reinforce that modeling both event timing and structure is essential for accurate prediction and anomaly detection ([arXiv][9], [arXiv][10]).
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---
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## 🧩 Integration Options for HCFS
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### Option A: Lightweight TGN-based coherence monitor
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* Build your versioned graph model (relational or graph DB)
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* Hook in an incremental TGN (e.g. using Twitter TGN or PyTorch Geometric Temporal)
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* Train a lightweight task: detect divergence or context drift across paths
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### Option B: Embedded temporal embedding layer
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* Couple TGN-style memory modules with context retrieval
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* When context is fetched for `(path, depth=N)`, combine static inherited blobs *and* dynamic embedding summarizers for coherence-aware top-k suggestions
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* Enables agents to get both content and confidence signals
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---
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## ✅ Summary Table
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| Use Case | Model | Benefit |
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| --------------------------------- | --------------------------- | ----------------------------------------------------- |
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| Evolving context tracking | TGN with node-memory | captures time-series history of path and blob changes |
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| Predicting coherence issues | Know-Evolve or EvoKG models | anticipate divergence before agents conflict |
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| Summarization & relevance ranking | CATGNN style selection | focus context retrieval on recent or critical updates |
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| Hybrid relational/graph DB | Graph topology + timestamps | supports both hierarchy and version/time semantics |
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---
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## 🚀 Next Steps
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1. Define your **graph event schema** (blob creation, merge, parent change).
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2. Select a framework: e.g. Twitter’s TGN library ([GitHub][2]) or PyTorch Geometric Temporal.
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3. Train simple prediction tasks: e.g. coherence anomalies, version conflicts.
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4. Use learned embeddings as features in context relevance ranking or ambiguity detection.
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[1]: https://arxiv.org/abs/2006.10637?utm_source=chatgpt.com "Temporal Graph Networks for Deep Learning on Dynamic Graphs"
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[2]: https://github.com/twitter-research/tgn?utm_source=chatgpt.com "GitHub - twitter-research/tgn: TGN: Temporal Graph Networks"
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[3]: https://arxiv.org/abs/1705.05742?utm_source=chatgpt.com "Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs"
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[4]: https://proceedings.mlr.press/v70/trivedi17a/trivedi17a.pdf?utm_source=chatgpt.com "Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs"
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[5]: https://arxiv.org/abs/1902.10191?utm_source=chatgpt.com "EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs"
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[6]: https://arxiv.org/abs/2302.01018?utm_source=chatgpt.com "Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities"
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[7]: https://arxiv.org/html/2302.01018?utm_source=chatgpt.com "Graph Neural Networks for temporal graphs: State of the art, open ..."
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[8]: https://link.springer.com/article/10.1007/s12559-024-10359-0?utm_source=chatgpt.com "Dynamic Neighborhood Selection for Context Aware Temporal Evolution ..."
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[9]: https://arxiv.org/abs/2202.07648?utm_source=chatgpt.com "[2202.07648] EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs"
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[10]: https://arxiv.org/abs/2301.04977?utm_source=chatgpt.com "[2301.04977] Modeling the evolution of temporal knowledge graphs with uncertainty"
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Absolutely—PyTorch Geometric Temporal (PyG-T) is a great starting point for modeling how **permission changes cascade context shifts** in your HCFS. Let’s map that out:
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---
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## 🔁 1. Graph + Permissions + Temporal Events
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Imagine your HCFS as a temporal graph where:
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* **Nodes** represent paths, context blobs, and agent identities.
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* **Edges** model relationships: `parent-path`, `version-of`, and **permission-grant** (e.g. agent-to-path).
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* Each change (e.g. permission granted/revoked, context blob added/updated) emits an **event**: `(src_node, dst_node, event_type, timestamp)`—fit for PyG-T's `TemporalData` structure ([PyG Documentation][1]).
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Example events:
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```
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(agentA → path /submodule): PERMISSION_GRANTED at t1
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(path /submodule): CONTEXT_UPDATED by agentA at t2
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(agentA → path): PERMISSION_REVOKED at t3
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```
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---
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## 🧠 2. Use Case: Permission Cascades → Context Exposure Changes
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### Scenario:
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* **Submodule `/modX`** changes permissions: agent A loses read access at time t3.
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* That revocation should trigger a **context cascade**: any context blobs previously created by agent A for `/modX` are no longer visible to A or its group, and any derived context (e.g. merged summaries) may need invalidation or regeneration.
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### TGN Role:
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* A **Temporal Graph Neural Network** can learn and flag such cascades. By training on sequences of permission-change events and context-access events, your model can predict which context blobs become stale or inaccessible due to new permission states.
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---
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## ⚙️ 3. Implementation Sketch with PyTorch Geometric Temporal
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### A. Event Stream Construction
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Use PyG-T’s `TemporalData` API to build your dataset:
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```python
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from torch_geometric.data import TemporalData
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events = TemporalData(
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src=torch.tensor([...]), # source node IDs
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dst=torch.tensor([...]), # destination node IDs
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t=torch.tensor([...]), # timestamps
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msg=torch.tensor([...]) # event types encoded
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)
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```
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Event types encode changes: `PERMISSION_GRANTED`, `REVOKE`, `CONTEXT_UPDATE`, `MERGE`, etc. ([PyG Documentation][1])
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### B. Model Choice
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* Use recurrent layers like `GConvGRU` or `GConvLSTM` to process evolving structure and events. This allows the model to remember prior permission relationships and context updates. ([PyTorch Forums][2], [PyTorch Forums][3])
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### C. Training Task
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* **Label** context exposure windows correctly: whether agent had valid permission at query time.
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* **Train** to predict: (a) contexts that must be invalidated upon a permission revocation, (b) agents likely to be impacted by a permission change.
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You can also forecast which agents will be affected by future cascading permission changes.
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---
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## 👁️ 4. Operational Integration
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### Monitoring Pipeline:
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1. Every permission or context change emits an event into your temporal graph.
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2. TGN model continuously ingests event stream, updating node embeddings.
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3. Agent requests for context trigger a check:
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* Is the agent’s embedding synchronized with the latest permission state?
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* Does the model flag outdated or stale blobs needing recompute?
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### Coherence Actions:
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* On permission cascade, generate tasks to:
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* Invalidate or reassign context blobs,
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* Regenerate summaries for authorized agents,
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* Notify subscribed agents of access revocation.
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---
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## 📊 Summary Table
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| Component | Description |
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| --------------------------- | ------------------------------------------------------------------------------------------ |
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| **Event stream** | Captures permission and context changes as timestamped edges/nodes in a TemporalData graph |
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| **Dynamic permission node** | Connection between agent and path nodes representing access rights |
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| **TGN model** | Processes events to update node embedding states over time |
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| **Training objective** | Predict context invalidation needs and agents affected by permission shifts |
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| **Run-time check** | On context fetch, ensure embedding reflects current permissions; flag stale data |
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---
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## 📚 Supporting References
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* PyTorch Geometric Temporal’s `TemporalData` API handles event-based temporal graphs elegantly for this use case ([PyG Documentation][1]).
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* Discussions of node-feature input shapes and recurrent temporal GNNs (`GConvGRU`, `GConvLSTM`) show how to structure the learning model ([PyTorch Forums][3], [PyTorch Forums][2]).
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* Relationship-based access control (ReBAC) aligns with modeling permissions as graph relationships between agents and resources ([Wikipedia][4]).
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---
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## 🚀 Next Steps:
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1. Define node and edge IDs: assign IDs to agents, paths, and context blobs.
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2. Build event collector to log permission and context operations with timestamps.
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3. Construct `TemporalData` sequences for model ingestion.
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4. Prototype a TGN using PyG-T, train on synthetic cascades (grant → context update → revoke).
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5. Validate cascade detection and context invalidation effectiveness.
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[1]: https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.data.TemporalData.html?utm_source=chatgpt.com "torch_geometric.data.TemporalData — pytorch_geometric documentation"
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[2]: https://discuss.pytorch.org/t/pytorch-geometric-temporal-confusion-concerning-input-shape-of-node-features-required-for-recurrent-graph-convolutional-layers/168101?utm_source=chatgpt.com "Pytorch Geometric Temporal: confusion concerning input ... - PyTorch Forums"
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[3]: https://discuss.pytorch.org/t/pytorch-geometric-temporal-graph-convlstm/147561?utm_source=chatgpt.com "Pytorch Geometric Temporal Graph ConvLSTM"
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[4]: https://en.wikipedia.org/wiki/Relationship-based_access_control?utm_source=chatgpt.com "Relationship-based access control"
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