{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-networkx","slug":"pypi-networkx","name":"networkx","type":"repo","url":"https://pypi.org/project/networkx/","page_url":"https://unfragile.ai/pypi-networkx","categories":["frameworks-sdks"],"tags":["Networks","Graph","Theory","Mathematics","network","graph","discrete","mathematics","math"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-networkx__cap_0","uri":"capability://data.processing.analysis.graph.construction.from.multiple.formats","name":"graph-construction-from-multiple-formats","description":"Creates graph objects from diverse input formats including adjacency matrices, edge lists, GML, GraphML, JSON, and edge-weighted dictionaries. NetworkX uses a flexible node-edge abstraction where nodes can be any hashable Python object and edges store arbitrary attribute dictionaries, enabling heterogeneous graph representations without schema enforcement. The library automatically infers graph directionality and handles self-loops and multi-edges through specialized graph classes (DiGraph, MultiGraph, MultiDiGraph).","intents":["I need to load a network dataset from a CSV edge list and convert it to a graph structure for analysis","I want to construct a knowledge graph programmatically by adding nodes and edges with custom attributes","I need to import a graph from a standard format like GraphML or GML and manipulate it in Python"],"best_for":["data scientists building network analysis pipelines","researchers working with graph datasets in multiple formats","developers prototyping graph-based applications"],"limitations":["No built-in streaming support for graphs larger than available RAM","Attribute dictionaries stored per node/edge can cause memory overhead for dense graphs with many attributes","Format conversion is lossy for some specialized graph properties not in NetworkX's data model"],"requires":["Python 3.9+","NumPy (optional, for matrix operations)","SciPy (optional, for sparse matrix input)"],"input_types":["adjacency matrix (NumPy array or SciPy sparse matrix)","edge list (list of tuples or pandas DataFrame)","GML file","GraphML file","JSON","edge-weighted dictionary"],"output_types":["Graph object (undirected)","DiGraph object (directed)","MultiGraph object (undirected with multi-edges)","MultiDiGraph object (directed with multi-edges)"],"categories":["data-processing-analysis","graph-data-structures"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_1","uri":"capability://data.processing.analysis.graph.traversal.and.path.finding","name":"graph-traversal-and-path-finding","description":"Implements breadth-first search (BFS), depth-first search (DFS), and shortest path algorithms (Dijkstra, Bellman-Ford, A*) using iterator-based traversal patterns that yield nodes/edges on-the-fly rather than materializing full paths. The library uses deque-based queue management for BFS and recursive/stack-based DFS, with optional weight-aware variants for weighted graphs. Path algorithms return both the shortest distance and the actual path as a list of nodes.","intents":["I need to find the shortest path between two nodes in a weighted network","I want to traverse a graph and visit all reachable nodes from a starting point","I need to detect if two nodes are connected and find the connecting path"],"best_for":["network analysts computing connectivity metrics","routing and navigation system developers","social network researchers analyzing reachability"],"limitations":["Dijkstra and A* assume non-negative edge weights; negative weights require Bellman-Ford which is O(VE) complexity","All-pairs shortest path (Floyd-Warshall) requires O(V³) time and O(V²) space, impractical for graphs with >10k nodes","BFS/DFS iterators consume memory proportional to graph branching factor; deep graphs can exhaust stack"],"requires":["Python 3.9+","Graph object with nodes and edges defined"],"input_types":["Graph/DiGraph/MultiGraph/MultiDiGraph object","source node (any hashable object)","target node (any hashable object)","edge weight attribute name (string, optional)"],"output_types":["path (list of nodes)","distance (numeric)","iterator of nodes (for BFS/DFS)","dictionary of paths (for all-pairs)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_10","uri":"capability://data.processing.analysis.bipartite.graph.analysis","name":"bipartite-graph-analysis","description":"Analyzes bipartite graphs (graphs with two disjoint node sets where edges only connect nodes from different sets) using specialized algorithms for bipartite matching, projection, and property checking. Includes maximum bipartite matching (Hopcroft-Karp algorithm), bipartite projection (creating unipartite graphs from bipartite structure), and bipartiteness checking (2-coloring via BFS). Returns matching as edge set, projections as new Graph objects, or boolean for bipartiteness.","intents":["I need to find optimal matching between two groups (e.g., students to schools)","I want to project a bipartite network (e.g., users and items) into a single-mode network","I need to verify if a graph is bipartite and find the two node sets"],"best_for":["recommendation system developers working with user-item bipartite networks","matching problem solvers (assignment, hiring, dating)","network analysts studying two-mode networks"],"limitations":["Bipartite matching assumes simple graphs; MultiGraph/MultiDiGraph require preprocessing","Bipartite projection creates dense graphs; projecting large bipartite networks can produce >100k edges from sparse input","Projection loses information about the original bipartite structure; not reversible","No support for weighted bipartite matching (requires external solvers like Hungarian algorithm)"],"requires":["Python 3.9+","Graph object with bipartite structure (nodes must have 'bipartite' attribute)"],"input_types":["Graph/MultiGraph object with bipartite node partition","optional: edge weight attribute name"],"output_types":["set of edges (matching)","Graph object (projection)","boolean (bipartiteness check)","tuple of two node sets (bipartite partition)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_11","uri":"capability://data.processing.analysis.graph.export.and.serialization","name":"graph-export-and-serialization","description":"Exports graphs to multiple file formats including GML (Graph Modelling Language), GraphML (XML-based), JSON, edge lists (CSV/TSV), and adjacency matrices (NumPy/SciPy). Export functions serialize node/edge attributes as format-specific metadata; GML and GraphML preserve full graph structure and attributes, while edge lists and matrices lose attribute information. Supports both text-based (GML, GraphML, JSON) and binary (pickle) serialization.","intents":["I need to save a graph to a standard format for sharing with collaborators","I want to export a graph to a format compatible with other network analysis tools (Gephi, Cytoscape)","I need to convert a graph to an adjacency matrix for machine learning pipelines"],"best_for":["researchers sharing network datasets","data engineers integrating graphs with other tools","machine learning practitioners converting graphs to matrix form"],"limitations":["GML and GraphML export can produce large files for dense graphs; no compression support","Adjacency matrix export requires O(V²) space; impractical for sparse graphs with >10k nodes","JSON export is verbose; not suitable for very large graphs","Pickle serialization is Python-specific; not portable to other languages"],"requires":["Python 3.9+","Graph object","NumPy/SciPy (for matrix export)"],"input_types":["Graph/DiGraph/MultiGraph/MultiDiGraph object","optional: output file path or stream"],"output_types":["GML file","GraphML file","JSON string/file","CSV/TSV edge list","NumPy/SciPy matrix","pickle binary"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_2","uri":"capability://data.processing.analysis.centrality.and.importance.metrics","name":"centrality-and-importance-metrics","description":"Computes node importance scores using multiple centrality algorithms: degree centrality (node degree normalized by graph size), betweenness centrality (fraction of shortest paths passing through a node), closeness centrality (inverse average distance to all other nodes), eigenvector centrality (importance based on connections to important nodes), PageRank (iterative importance propagation), and harmonic centrality. Each algorithm returns a dictionary mapping nodes to numeric scores; algorithms use matrix operations (NumPy/SciPy) or iterative approximation for scalability.","intents":["I need to identify the most influential nodes in a social network","I want to find bottleneck nodes that control information flow in a network","I need to rank nodes by their structural importance for resource allocation"],"best_for":["social network analysts identifying influencers","infrastructure planners finding critical nodes","recommendation system developers ranking entities"],"limitations":["Betweenness centrality is O(VE) for unweighted graphs, O(V³) for weighted graphs; infeasible for >10k nodes","Eigenvector centrality requires matrix eigenvalue computation; can be numerically unstable for poorly-conditioned graphs","PageRank requires tuning of damping factor and convergence tolerance; results sensitive to parameter choices","All centrality metrics assume connected or weakly-connected graphs; disconnected components produce misleading scores"],"requires":["Python 3.9+","Graph object with nodes and edges","NumPy (for eigenvector and some matrix-based centralities)"],"input_types":["Graph/DiGraph/MultiGraph/MultiDiGraph object","optional: edge weight attribute name","optional: algorithm-specific parameters (e.g., PageRank damping factor)"],"output_types":["dictionary mapping nodes to centrality scores (float)","sorted list of (node, score) tuples"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_3","uri":"capability://data.processing.analysis.community.detection.and.clustering","name":"community-detection-and-clustering","description":"Detects communities (densely-connected subgraphs) using modularity optimization algorithms (Louvain, greedy modularity), spectral clustering, and label propagation. The Louvain algorithm uses hierarchical agglomeration with local modularity optimization to find high-quality partitions; label propagation assigns community labels through iterative neighbor voting. Returns a partition as a dictionary or set of sets mapping nodes to community IDs. Modularity score quantifies partition quality (higher = better separation).","intents":["I need to partition a social network into communities of similar users","I want to find densely-connected clusters in a collaboration network","I need to evaluate the quality of a community partition using modularity"],"best_for":["social network researchers studying group structure","recommendation system developers finding user cohorts","organizational network analysts identifying teams"],"limitations":["Louvain algorithm is non-deterministic; results vary across runs due to random initialization","Modularity optimization is NP-hard; algorithms use greedy approximation, not guaranteed optimal","Label propagation requires tuning of iteration count and convergence criteria; sensitive to initial label assignment","All algorithms assume undirected graphs or require conversion of directed graphs; directed community detection is unsupported"],"requires":["Python 3.9+","Graph object (undirected or converted to undirected)","SciPy (for spectral clustering)"],"input_types":["Graph/MultiGraph object","optional: edge weight attribute name","optional: seed for reproducibility"],"output_types":["generator of sets (each set is a community)","dictionary mapping nodes to community IDs","modularity score (float between -1 and 1)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_4","uri":"capability://data.processing.analysis.graph.isomorphism.and.matching","name":"graph-isomorphism-and-matching","description":"Detects graph isomorphism (structural equivalence) and finds maximum matchings (sets of non-adjacent edges) using backtracking-based isomorphism checking and augmenting path algorithms. Graph isomorphism uses VF2 algorithm with pruning heuristics to compare node/edge structure; maximum matching uses augmenting paths (Hopcroft-Karp for bipartite graphs, general matching for arbitrary graphs). Returns boolean for isomorphism or matching as a set of edge tuples.","intents":["I need to check if two molecular structures are chemically equivalent","I want to find the maximum matching in a bipartite graph for assignment problems","I need to detect duplicate network topologies in a dataset"],"best_for":["chemoinformatics researchers comparing molecular structures","operations researchers solving assignment and matching problems","network deduplication and canonicalization tasks"],"limitations":["Graph isomorphism is NP-complete; VF2 algorithm has exponential worst-case complexity on dense graphs","Isomorphism checking requires node/edge attribute matching; semantic equivalence requires custom comparison functions","Maximum matching algorithms assume simple graphs; MultiGraph/MultiDiGraph require preprocessing to remove multi-edges","No support for weighted matching (maximum weight matching requires external solvers)"],"requires":["Python 3.9+","Graph objects for comparison"],"input_types":["two Graph/DiGraph objects (for isomorphism)","Graph/MultiGraph object (for matching)","optional: node/edge attribute comparison functions"],"output_types":["boolean (isomorphism result)","node mapping dictionary (if isomorphic)","set of edge tuples (matching result)","integer (matching cardinality)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_5","uri":"capability://data.processing.analysis.connectivity.and.component.analysis","name":"connectivity-and-component-analysis","description":"Analyzes graph connectivity by computing connected components (maximal connected subgraphs), strongly connected components (SCCs) in directed graphs, and bridge/articulation point detection. Uses union-find (disjoint set) for component identification and Tarjan's algorithm for SCC computation. Returns components as generators of node sets or dictionaries mapping nodes to component IDs. Bridge detection identifies edges whose removal disconnects the graph; articulation points identify nodes with the same property.","intents":["I need to identify isolated subnetworks in a large graph","I want to find critical edges whose failure would partition the network","I need to analyze strongly connected components in a directed network like citation graphs"],"best_for":["network resilience analysts studying failure modes","infrastructure planners identifying critical links","directed graph analysts studying reachability structure"],"limitations":["Union-find component detection is O(V + E) but requires full graph traversal; streaming detection unsupported","Bridge/articulation point detection requires DFS traversal; O(V + E) but can be slow on very large graphs","Strongly connected components require directed graphs; undirected graphs must be converted to directed equivalents","No support for k-connectivity (k-edge or k-vertex connectivity) beyond simple connectivity"],"requires":["Python 3.9+","Graph object with nodes and edges"],"input_types":["Graph/DiGraph/MultiGraph/MultiDiGraph object"],"output_types":["generator of node sets (components)","dictionary mapping nodes to component IDs","set of edges (bridges)","set of nodes (articulation points)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_6","uri":"capability://image.visual.graph.layout.and.visualization.preparation","name":"graph-layout-and-visualization-preparation","description":"Computes node positions for graph visualization using force-directed layout algorithms (spring layout using Fruchterman-Reingold), spectral layout (using graph Laplacian eigenvectors), and circular/shell layouts. Spring layout iteratively adjusts node positions to minimize edge length and node overlap using physics-inspired forces; spectral layout uses eigenvectors of the graph Laplacian for dimensionality reduction. Returns dictionary mapping nodes to (x, y) coordinate tuples suitable for matplotlib or other visualization libraries.","intents":["I need to visualize a network graph with nodes positioned to minimize edge crossings","I want to generate 2D coordinates for a graph to display in a web-based visualization","I need to compute layout positions for a large network without using external visualization software"],"best_for":["data visualization developers building interactive graph displays","network analysts creating publication-quality network diagrams","researchers exploring graph structure through visual inspection"],"limitations":["Spring layout is O(V²) per iteration; converges slowly on large graphs (>5k nodes) and requires iteration tuning","Force-directed algorithms are non-deterministic; different runs produce different layouts unless seed is fixed","Spectral layout requires eigenvalue computation; numerically unstable for poorly-conditioned Laplacian matrices","All layouts assume 2D output; 3D layouts unsupported","No support for constrained layouts (e.g., hierarchical, tree-based layouts)"],"requires":["Python 3.9+","Graph object","NumPy (for spectral layout)","SciPy (for eigenvalue computation)"],"input_types":["Graph/DiGraph/MultiGraph/MultiDiGraph object","optional: edge weight attribute name","optional: layout algorithm parameters (iterations, temperature, etc.)"],"output_types":["dictionary mapping nodes to (x, y) tuples","NumPy array of shape (num_nodes, 2)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_7","uri":"capability://data.processing.analysis.graph.density.and.structural.metrics","name":"graph-density-and-structural-metrics","description":"Computes structural properties of graphs including density (ratio of actual edges to possible edges), diameter (longest shortest path), average clustering coefficient (local triangle density), transitivity (global clustering), and assortativity (correlation of node degrees). These metrics use direct mathematical formulas or graph traversal: density is E / (V * (V-1) / 2), diameter uses all-pairs shortest paths, clustering uses triangle counting. Returns numeric scores characterizing overall graph structure.","intents":["I need to characterize the overall structure of a network with summary statistics","I want to compare the density and clustering properties of different networks","I need to compute the diameter of a network to understand its scale"],"best_for":["network scientists comparing graph properties across datasets","researchers validating network models against real-world data","data quality analysts assessing graph completeness"],"limitations":["Diameter computation requires all-pairs shortest paths; O(V³) for dense graphs, impractical for >10k nodes","Clustering coefficient computation requires triangle enumeration; O(V * E) or worse for dense graphs","Assortativity assumes simple graphs; MultiGraph/MultiDiGraph require preprocessing","All metrics assume connected graphs; disconnected graphs produce misleading diameter and average path length"],"requires":["Python 3.9+","Graph object"],"input_types":["Graph/DiGraph/MultiGraph/MultiDiGraph object"],"output_types":["numeric values (float or int)","dictionary of metric names to values"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_8","uri":"capability://data.processing.analysis.graph.generation.and.synthetic.models","name":"graph-generation-and-synthetic-models","description":"Generates synthetic graphs from parametric models including random graphs (Erdős-Rényi), scale-free networks (Barabási-Albert preferential attachment), small-world networks (Watts-Strogatz), and regular lattices. Each model uses specific generation algorithms: Erdős-Rényi adds edges with fixed probability, Barabási-Albert iteratively adds nodes with preferential attachment to high-degree nodes, Watts-Strogatz starts with a ring lattice and rewires edges with probability. Returns a Graph object with specified number of nodes and edges.","intents":["I need to generate synthetic networks for algorithm benchmarking and testing","I want to create random graphs with specific properties (scale-free, small-world) for simulation","I need to generate baseline networks for comparison with real-world data"],"best_for":["algorithm developers benchmarking graph algorithms on synthetic data","network modelers validating theoretical predictions","researchers studying network properties across parameter ranges"],"limitations":["Barabási-Albert preferential attachment is O(V²) in time and space; impractical for >100k nodes","Watts-Strogatz rewiring is non-deterministic; results vary across runs unless seed is fixed","Generated graphs are unweighted; weighted variants require post-processing","No support for temporal or dynamic graph generation"],"requires":["Python 3.9+","NumPy (for random number generation)"],"input_types":["model parameters (n=number of nodes, p=edge probability, k=average degree, etc.)","optional: random seed for reproducibility"],"output_types":["Graph/DiGraph object with specified structure"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-networkx__cap_9","uri":"capability://data.processing.analysis.graph.coloring.and.independent.sets","name":"graph-coloring-and-independent-sets","description":"Solves graph coloring (assigning colors to nodes such that adjacent nodes have different colors) and maximum independent set (finding largest set of non-adjacent nodes) using greedy approximation algorithms. Graph coloring uses greedy node ordering with various heuristics (largest-degree-first, smallest-degree-last); maximum independent set uses greedy selection and complement graph analysis. Returns color assignment as dictionary or independent set as list of nodes.","intents":["I need to assign resources to tasks with conflict constraints (e.g., scheduling)","I want to find the maximum independent set for optimization problems","I need to compute the chromatic number (minimum colors needed) of a graph"],"best_for":["operations researchers solving scheduling and resource allocation problems","constraint satisfaction problem developers","algorithm researchers studying NP-hard approximation"],"limitations":["Graph coloring is NP-hard; greedy algorithms produce approximations, not optimal solutions","Maximum independent set is NP-hard; greedy approximation has no guaranteed approximation ratio","Greedy coloring quality depends heavily on node ordering heuristic; no universal best heuristic","No support for weighted coloring or independent set variants"],"requires":["Python 3.9+","Graph object"],"input_types":["Graph/DiGraph/MultiGraph/MultiDiGraph object","optional: node ordering strategy (largest-degree-first, etc.)"],"output_types":["dictionary mapping nodes to color IDs (integers)","list of nodes (independent set)","integer (chromatic number estimate)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"low","permissions":["Python 3.9+","NumPy (optional, for matrix operations)","SciPy (optional, for sparse matrix input)","Graph object with nodes and edges defined","Graph object with bipartite structure (nodes must have 'bipartite' attribute)","Graph object","NumPy/SciPy (for matrix export)","Graph object with nodes and edges","NumPy (for eigenvector and some matrix-based centralities)","Graph object (undirected or converted to undirected)"],"failure_modes":["No built-in streaming support for graphs larger than available RAM","Attribute dictionaries stored per node/edge can cause memory overhead for dense graphs with many attributes","Format conversion is lossy for some specialized graph properties not in NetworkX's data model","Dijkstra and A* assume non-negative edge weights; negative weights require Bellman-Ford which is O(VE) complexity","All-pairs shortest path (Floyd-Warshall) requires O(V³) time and O(V²) space, impractical for graphs with >10k nodes","BFS/DFS iterators consume memory proportional to graph branching factor; deep graphs can exhaust stack","Bipartite matching assumes simple graphs; MultiGraph/MultiDiGraph require preprocessing","Bipartite projection creates dense graphs; projecting large bipartite networks can produce >100k edges from sparse input","Projection loses information about the original bipartite structure; not reversible","No support for weighted bipartite matching (requires external solvers like Hungarian algorithm)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:25.058Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-networkx","compare_url":"https://unfragile.ai/compare?artifact=pypi-networkx"}},"signature":"yWWjVvZJ7D0O2CdzGg1k8XUkDWzrEG5Ob8BwcdhM+tg3fakvLH8Agq93/Va1M/0IUPPvRmt+Vlk/cIjsJX9SAw==","signedAt":"2026-06-21T18:41:55.403Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-networkx","artifact":"https://unfragile.ai/pypi-networkx","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-networkx","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}