# community_detection URL: /api/analysis/community_detection/ Section: analysis -------------------------------------------------------------------------------- community_detection - Bengal window.BENGAL_THEME_DEFAULTS = { appearance: 'dark', palette: 'snow-lynx' }; // Progressive Enhancement System Configuration window.Bengal = window.Bengal || {}; window.Bengal.enhanceBaseUrl = '/bengal/assets/js/enhancements'; window.Bengal.watchDom = true; window.Bengal.debug = false; (function () { try { var defaults = window.BENGAL_THEME_DEFAULTS || { appearance: 'system', palette: '' }; var defaultAppearance = defaults.appearance; if (defaultAppearance === 'system') { defaultAppearance = (window.matchMedia && window.matchMedia('(prefers-color-scheme: dark)').matches) ? 'dark' : 'light'; } var storedTheme = localStorage.getItem('bengal-theme'); var storedPalette = localStorage.getItem('bengal-palette'); var theme = storedTheme ? 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Documentation Info About Arrow Clockwise Get Started Note Tutorials File Text Content Palette Theming Settings Building Starburst Extending Bookmark Reference Learning Tracks Releases Dev GitHub API Reference bengal CLI Palette Appearance Chevron Down Mode Monitor System Sun Light Moon Dark Palette Snow Lynx Brown Bengal Silver Bengal Charcoal Bengal Blue Bengal API Reference __main__ bengal Caret Right Folder Analysis community_detection graph_analysis graph_reporting graph_visualizer knowledge_graph link_suggestions link_types page_rank path_analysis performance_advisor results Caret Right Folder Assets manifest pipeline Caret Right Folder Autodoc base config docstring_parser utils virtual_orchestrator Caret Right Folder Extractors cli openapi python Caret Right Folder Models cli common openapi python Caret Right Folder Cache asset_dependency_map cache_store cacheable compression dependency_tracker page_discovery_cache query_index query_index_registry taxonomy_index utils Caret Right Folder Build Cache autodoc_tracking core file_tracking fingerprint parsed_content_cache rendered_output_cache taxonomy_index_mixin validation_cache Caret Right Folder Indexes author_index category_index date_range_index section_index Caret Right Folder Cli __main__ base site_templates utils Caret Right Folder Commands assets build clean collections config debug explain fix health init perf project serve site skeleton sources theme utils validate Caret Right Folder Graph __main__ bridges communities orphans pagerank report suggest Caret Right Folder New config presets scaffolds site wizard Caret Right Folder Helpers cli_app_loader cli_output config_validation error_handling menu_config metadata progress site_loader traceback validation Caret Right Folder Skeleton hydrator schema Caret Right Folder Templates base registry Caret Right Folder Blog template Caret Right Folder Changelog template Caret Right Folder Default template Caret Right Folder Docs template Caret Right Folder Landing 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reload_controller request_handler request_logger resource_manager utils Caret Right Folder Services validation Caret Right Folder Themes config Caret Right Folder Utils atomic_write autodoc build_context build_stats build_summary cli_output css_minifier dates dotdict error_handlers file_io file_lock hashing incremental_constants js_bundler live_progress logger metadata observability page_initializer pagination path_resolver paths performance_collector performance_report profile progress retry rich_console sections swizzle text theme_registry theme_resolution thread_local traceback_config traceback_renderer url_normalization url_strategy API Reference Analysis ᗢ Caret Down Link Copy URL External Open LLM text Copy Copy LLM text Share with AI Ask Claude Ask ChatGPT Ask Gemini Ask Copilot Module analysis.community_detection Community Detection for Bengal SSG. Implements the Louvain method for discovering topical clusters in content. The algorithm optimizes modularity to find natural groupings of pages. The Louvain method works in two phases: Local optimization: Move nodes to communities that maximize modularity gain Aggregation: Treat each community as a single node and repeat References: Blondel, V. D., et al. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment. View source 3 Classes 1 Function Classes Community dataclass A community of related pages discovered through link structure. Represents a group of pages that a… 2 Caret Right A community of related pages discovered through link structure. Represents a group of pages that are densely connected to each other and share similar topics or themes. Useful for understanding content organization and identifying topic clusters. Attributes Name Type Description id int Unique community identifier pages set[Page] Set of pages belonging to this community size — Number of pages in the community density — Internal connection density (0.0-1.0) Methods 2 Tag size property Number of pages in this community. int Caret Right def size(self) -> int Number of pages in this community. Returns int get_top_pages_by_degree Get most connected pages in this community. 1 list[Page] Caret Right def get_top_pages_by_degree(self, limit: int = 5) -> list[Page] Get most connected pages in this community. Parameters 1 limit int Returns list[Page] CommunityDetectionResults dataclass Results from community detection analysis. Contains discovered communities and quality metrics. Co… 3 Caret Right Results from community detection analysis. Contains discovered communities and quality metrics. Communities represent natural groupings of related pages based on link structure. Attributes Name Type Description communities list[Community] List of detected communities modularity float Modularity score (quality metric, -1.0 to 1.0, higher is better) iterations int num_communities — Total number of communities detected Methods 3 get_community_for_page Find which community a page belongs to. 1 Community | None Caret Right def get_community_for_page(self, page: Page) -> Community | None Find which community a page belongs to. Parameters 1 page Page Returns Community | None get_largest_communities Get largest communities by page count. 1 list[Community] Caret Right def get_largest_communities(self, limit: int = 10) -> list[Community] Get largest communities by page count. Parameters 1 limit int Returns list[Community] get_communities_above_size Get communities with at least min_size pages. 1 list[Community] Caret Right def get_communities_above_size(self, min_size: int) -> list[Community] Get communities with at least min_size pages. Parameters 1 min_size int Returns list[Community] LouvainCommunityDetector Detect communities using the Louvain method. The Louvain algorithm is a greedy optimization method… 7 Caret Right Detect communities using the Louvain method. The Louvain algorithm is a greedy optimization method that attempts to optimize the modularity of a partition of the network. It runs in two phases: Modularity Optimization: Each node is moved to the community that yields the largest increase in modularity. Community Aggregation: A new network is built where nodes are communities and edges represent connections between communities. These phases are repeated until no further improvement is possible. Methods 1 detect Detect communities using Louvain method. 0 CommunityDetectionResults Caret Right def detect(self) -> CommunityDetectionResults Detect communities using Louvain method. Returns CommunityDetectionResults — CommunityDetectionResults with discovered communities Internal Methods 6 Caret Right __init__ Initialize Louvain community detector. 3 None Caret Right def __init__(self, graph: KnowledgeGraph, resolution: float = 1.0, random_seed: int | None = None) Initialize Louvain community detector. Parameters 3 graph KnowledgeGraph KnowledgeGraph with page connections resolution float Resolution parameter (higher = more communities) random_seed int | None Random seed for reproducibility _build_edge_weights Build edge weights from the graph. Uses frozenset to represent undirected edges. 1 dict[frozenset[Page… Caret Right def _build_edge_weights(self, pages: list[Page]) -> dict[frozenset[Page], float] Build edge weights from the graph. Uses frozenset to represent undirected edges. Parameters 1 pages list[Page] Returns dict[frozenset[Page], float] _compute_node_degrees Compute weighted degree for each node. 2 dict[Page, float] Caret Right def _compute_node_degrees(self, pages: list[Page], edge_weights: dict[frozenset[Page], float]) -> dict[Page, float] Compute weighted degree for each node. Parameters 2 pages list[Page] edge_weights dict[frozenset[Page], float] Returns dict[Page, float] _get_neighboring_communities Get communities that are neighbors of this page. 3 set[int] Caret Right def _get_neighboring_communities(self, page: Page, page_to_community: dict[Page, int], edge_weights: dict[frozenset[Page], float]) -> set[int] Get communities that are neighbors of this page. Parameters 3 page Page page_to_community dict[Page, int] edge_weights dict[frozenset[Page], float] Returns set[int] _modularity_gain Calculate modularity gain from moving page to new community. This uses the fas… 6 float Caret Right def _modularity_gain(self, page: Page, to_community: int, page_to_community: dict[Page, int], edge_weights: dict[frozenset[Page], float], node_degrees: dict[Page, float], total_weight: float) -> float Calculate modularity gain from moving page to new community. This uses the fast incremental formula for modularity change. Parameters 6 page Page to_community int page_to_community dict[Page, int] edge_weights dict[frozenset[Page], float] node_degrees dict[Page, float] total_weight float Returns float _compute_modularity Compute Newman's modularity Q. 4 float Caret Right def _compute_modularity(self, page_to_community: dict[Page, int], edge_weights: dict[frozenset[Page], float], node_degrees: dict[Page, float], total_weight: float) -> float Compute Newman's modularity Q. Parameters 4 page_to_community dict[Page, int] edge_weights dict[frozenset[Page], float] node_degrees dict[Page, float] total_weight float Returns float Functions detect_communities Convenience function to detect communities. 3 CommunityDetectionResults Caret Right def detect_communities(graph: KnowledgeGraph, resolution: float = 1.0, random_seed: int | None = None) -> CommunityDetectionResults Convenience function to detect communities. Parameters 3 Name Type Default Description graph KnowledgeGraph — KnowledgeGraph with page connections resolution float 1.0 Resolution parameter (higher = more communities) random_seed int | None None Random seed for reproducibility Returns CommunityDetectionResults — CommunityDetectionResults with discovered communities ← Previous analysis Next → graph_analysis List © 2025 Bengal ᓚᘏᗢ window.BENGAL_LAZY_ASSETS = { tabulator: '/bengal/assets/js/tabulator.min.js', dataTable: '/bengal/assets/js/data-table.js', mermaidToolbar: '/bengal/assets/js/mermaid-toolbar.9de5abba.js', mermaidTheme: '/bengal/assets/js/mermaid-theme.344822c5.js', graphMinimap: '/bengal/assets/js/graph-minimap.cc7e42e3.js', graphContextual: '/bengal/assets/js/graph-contextual.440e59c6.js' }; window.BENGAL_ICONS = { close: '/bengal/assets/icons/close.911d4fe1.svg', enlarge: '/bengal/assets/icons/enlarge.652035e5.svg', copy: '/bengal/assets/icons/copy.3d56e945.svg', 'download-svg': '/bengal/assets/icons/download.04f07e1b.svg', 'download-png': '/bengal/assets/icons/image.c34dfd40.svg', 'zoom-in': '/bengal/assets/icons/zoom-in.237b4a83.svg', 'zoom-out': '/bengal/assets/icons/zoom-out.38857c77.svg', reset: '/bengal/assets/icons/reset.d26dba29.svg' }; Arrow Up X -------------------------------------------------------------------------------- Metadata: - Author: lbliii - Word Count: 1807 - Reading Time: 9 minutes