install
source · Clone the upstream repo
git clone https://github.com/plurigrid/asi
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/plurigrid/asi "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/exponential-topology-communication" ~/.claude/skills/plurigrid-asi-exponential-topology-communication-e63485 && rm -rf "$T"
manifest:
skills/exponential-topology-communication/SKILL.mdsource content
Exponential Topology Communication
Category: Phase 3 Core - Scalable Communication Status: Skeleton Implementation Dependencies:
oriented-simplicial-networks (for topological structure)
Overview
Implements ExpoComm framework for exponentially efficient communication in large-scale systems using hyperbolic embeddings, O(log N) routing, and spectral gap optimization for rapid information dissemination.
Capabilities
- Hyperbolic Embeddings: Embed agents in hyperbolic space
- O(log N) Routing: Greedy routing with logarithmic complexity
- Spectral Gap Optimization: Maximize mixing time via graph structure
- Scalable Broadcast: Efficient all-to-all communication
Core Components
-
Hyperbolic Embeddings (
)hyperbolic_embeddings.jl- Poincaré disk model
- Greedy embedding algorithms
- Distance computation
-
ExpoComm Routing (
)expocomm_routing.jl- Greedy hyperbolic routing
- Load balancing strategies
- Fault tolerance
-
Spectral Optimization (
)spectral_optimization.jl- Graph Laplacian analysis
- Spectral gap maximization
- Expander graph construction
-
Scalability Analysis (
)scalability_analysis.jl- Communication complexity bounds
- Scaling experiments
- Comparison with Euclidean approaches
Integration Points
- Input from:
(communication topology)oriented-simplicial-networks - Output to:
(communication structure influences roles)emergent-role-assignment - Coordinates with:
(consensus over hyperbolic graphs)sheaf-theoretic-coordination
Usage
using ExponentialTopologyCommunication # Create network of N agents N = 1000 graph = random_power_law_graph(N, exponent=2.5) # Compute hyperbolic embeddings embeddings = hyperbolic_embedding(graph, dim=2) # Route message from source to target path = greedy_route(embeddings, source=1, target=N) @assert length(path) <= 2 * log2(N) # O(log N) guarantee # Analyze spectral properties spectral_gap = compute_spectral_gap(graph) mixing_time = estimate_mixing_time(spectral_gap, N)
References
- Krioukov et al. "Hyperbolic Geometry of Complex Networks" (2010)
- Kleinberg "Navigation in a Small World" (Nature 2000)
- Hoory et al. "Expander Graphs and their Applications" (2006)
Implementation Status
- Basic hyperbolic embeddings
- Greedy routing implementation
- Full spectral gap optimization
- Fault-tolerant routing
- Large-scale benchmarks