EasyGraph mentioned in this article refers to v0.2a38, unless specified otherwise.
NetworkX has a LOT more features EasyGraph, but lacks SHS detection features. It's first commit was in 2005.
It has lots of contributors and is still being actively developed, and is used by a lot of people (107,831 Repositories, 4,003 Packages).
EasyGraph is developed by Fudan University students, and its development and code base is quite messy and not governed by a written rule. The first commit was in 2020.
EasyGraph GitHub Pulse (last week), (last month), and the only project that depends on EasyGraph is my easygraph-test repository.
The documentation sucks, the tutorial code doesn't work as advertised (cuz it's outdated), and the API reference only contains class and function signatures.
It's likely you'd find the source code to be more useful than the documentation.
EG devs say they have better support for reading from and writing to a wide range of graph formats, but that's not true. NetworkX still wins in that respect.
NetworkX, surprisingly, has no dependencies, only some optional dependencies, specified in its requirements directory.
Example: the [default] optional dependency group includes libraries like matplotlib for drawing.
The core functionalities of NetworkX (graph representation, graph algorithms) does not depend on any other libraries.
EasyGraph, on the other hand, specified a bunch of utility dependencies (joblib, progressbar, tqdm) heavy dependencies (numpy, pandas, matplotlib, scikit-learn, tensorflow) as required.
Its core classes don't require these dependencies.
numpy is heavily used in many of EasyGraph's code base, for various purposes:
- Graph representation (
graphml.py,ucinet.py,gexf.py) - Utility code (
alias.py,to_numpy_matrix,to_numpy_array) - Almost all files that implement some SHS detection algorithms (
maxBlock.pyand many others)
ML libs sklearn and tensorflow are used only for implementing SHS detection algorithms.
The utility functions aren't really necessary and could be moved to optional deps.
Both libraries have classes for undirected and directed graphs and multigraphs (parallel edges allowed).
NetworkX also supports Ordered Graphs which give a consistent order for reporting of nodes and edges, but it's being deprecated.
NetworkX graphs' edges attribute (or should I say, property) returns data of type EdgeView,
the library also has DegreeView, EdgeView, NodeView, AdjacencyView view classes and FilterAdjacency, FilterMultiInner, FilterMultiAdjacency filter classes defined.
The view classes are quiet sophisticated and define dunder methods like __len__, __iter__ and __contains__, and provide Pythonic interface for the users.
classes/filters.py defines [ "no_filter", "hide_nodes", "hide_edges", "hide_multiedges", "hide_diedges", "hide_multidiedges", "show_nodes", "show_edges", "show_multiedges", "show_diedges", "show_multidiedges"], which provides toggles for graph instances so that users can choose what kinds of nodes / edges to receive.
classes/function.py defines common functions (neighbors, density, is_directed, to_directed etc) that operate on the view classes mentioned above.
The undirected graph class Graph has a C++ counter part GraphC, which is still quite buggy.
The operations.py, like function.py in NetworkX, implements a small number of common graph operations.
There's no view or filter classes, just the four graph classes.
The edges property returns a list of edges.
Dunder methods are only defined directly on the graph classes. (NetworkX does this too)
networkx/algorithms
networkx/algorithms
├── __init__.py
├── approximation
│ ├── __init__.py
│ ├── clique.py
│ ├── clustering_coefficient.py
│ ├── connectivity.py
│ ├── distance_measures.py
│ ├── dominating_set.py
│ ├── kcomponents.py
│ ├── matching.py
│ ├── maxcut.py
│ ├── ramsey.py
│ ├── steinertree.py
│ ├── traveling_salesman.py
│ ├── treewidth.py
│ └── vertex_cover.py
├── assortativity
│ ├── __init__.py
│ ├── connectivity.py
│ ├── correlation.py
│ ├── mixing.py
│ ├── neighbor_degree.py
│ └── pairs.py
├── asteroidal.py
├── bipartite
│ ├── __init__.py
│ ├── basic.py
│ ├── centrality.py
│ ├── cluster.py
│ ├── covering.py
│ ├── edgelist.py
│ ├── generators.py
│ ├── matching.py
│ ├── matrix.py
│ ├── projection.py
│ ├── redundancy.py
│ └── spectral.py
├── boundary.py
├── bridges.py
├── centrality
│ ├── __init__.py
│ ├── betweenness.py
│ ├── betweenness_subset.py
│ ├── closeness.py
│ ├── current_flow_betweenness.py
│ ├── current_flow_betweenness_subset.py
│ ├── current_flow_closeness.py
│ ├── degree_alg.py
│ ├── dispersion.py
│ ├── eigenvector.py
│ ├── flow_matrix.py
│ ├── group.py
│ ├── harmonic.py
│ ├── katz.py
│ ├── load.py
│ ├── percolation.py
│ ├── reaching.py
│ ├── second_order.py
│ ├── subgraph_alg.py
│ ├── trophic.py
│ └── voterank_alg.py
├── chains.py
├── chordal.py
├── clique.py
├── cluster.py
├── coloring
│ ├── __init__.py
│ ├── equitable_coloring.py
│ └── greedy_coloring.py
├── communicability_alg.py
├── community
│ ├── __init__.py
│ ├── asyn_fluid.py
│ ├── centrality.py
│ ├── community_utils.py
│ ├── kclique.py
│ ├── kernighan_lin.py
│ ├── label_propagation.py
│ ├── louvain.py
│ ├── lukes.py
│ ├── modularity_max.py
│ └── quality.py
├── components
│ ├── __init__.py
│ ├── attracting.py
│ ├── biconnected.py
│ ├── connected.py
│ ├── semiconnected.py
│ ├── strongly_connected.py
│ └── weakly_connected.py
├── connectivity
│ ├── __init__.py
│ ├── connectivity.py
│ ├── cuts.py
│ ├── disjoint_paths.py
│ ├── edge_augmentation.py
│ ├── edge_kcomponents.py
│ ├── kcomponents.py
│ ├── kcutsets.py
│ ├── stoerwagner.py
│ └── utils.py
├── core.py
├── covering.py
├── cuts.py
├── cycles.py
├── d_separation.py
├── dag.py
├── distance_measures.py
├── distance_regular.py
├── dominance.py
├── dominating.py
├── efficiency_measures.py
├── euler.py
├── flow
│ ├── __init__.py
│ ├── boykovkolmogorov.py
│ ├── capacityscaling.py
│ ├── dinitz_alg.py
│ ├── edmondskarp.py
│ ├── gomory_hu.py
│ ├── maxflow.py
│ ├── mincost.py
│ ├── networksimplex.py
│ ├── preflowpush.py
│ ├── shortestaugmentingpath.py
│ └── utils.py
├── graph_hashing.py
├── graphical.py
├── hierarchy.py
├── hybrid.py
├── isolate.py
├── isomorphism
│ ├── __init__.py
│ ├── ismags.py
│ ├── isomorph.py
│ ├── isomorphvf2.py
│ ├── matchhelpers.py
│ ├── temporalisomorphvf2.py
│ ├── tree_isomorphism.py
│ └── vf2userfunc.py
├── link_analysis
│ ├── __init__.py
│ ├── hits_alg.py
│ └── pagerank_alg.py
├── link_prediction.py
├── lowest_common_ancestors.py
├── matching.py
├── minors
│ ├── __init__.py
│ └── contraction.py
├── mis.py
├── moral.py
├── node_classification
│ ├── __init__.py
│ ├── hmn.py
│ ├── lgc.py
│ └── utils.py
├── non_randomness.py
├── operators
│ ├── __init__.py
│ ├── all.py
│ ├── binary.py
│ ├── product.py
│ └── unary.py
├── planar_drawing.py
├── planarity.py
├── polynomials.py
├── reciprocity.py
├── regular.py
├── richclub.py
├── shortest_paths
│ ├── __init__.py
│ ├── astar.py
│ ├── dense.py
│ ├── generic.py
│ ├── unweighted.py
│ └── weighted.py
├── similarity.py
├── simple_paths.py
├── smallworld.py
├── smetric.py
├── sparsifiers.py
├── structuralholes.py
├── summarization.py
├── swap.py
├── threshold.py
├── tournament.py
├── traversal
│ ├── __init__.py
│ ├── beamsearch.py
│ ├── breadth_first_search.py
│ ├── depth_first_search.py
│ ├── edgebfs.py
│ └── edgedfs.py
├── tree
│ ├── __init__.py
│ ├── branchings.py
│ ├── coding.py
│ ├── decomposition.py
│ ├── mst.py
│ ├── operations.py
│ └── recognition.py
├── triads.py
├── vitality.py
├── voronoi.py
└── wiener.py
easygraph/functions
functions
├── __init__.py
├── centrality
│ ├── __init__.py
│ ├── betweenness.py
│ ├── clossness.py
│ ├── degree.py
│ └── flowbetweenness.py
├── community
│ ├── __init__.py
│ ├── LPA.py
│ ├── modularity.py
│ ├── modularity_max_detection.py
│ └── motif.py
├── components
│ ├── __init__.py
│ ├── biconnected.py
│ ├── connected.py
│ └── ego_betweenness.py
├── drawing
│ ├── __init__.py
│ ├── drawing.py
│ ├── plot.py
│ └── positioning.py
├── graph_embedding
│ ├── __init__.py
│ ├── deepwalk.py
│ ├── line.py
│ ├── NOBE.py
│ ├── node2vec.py
│ └── sdne.py
├── graph_generator
│ ├── __init__.py
│ ├── classic.py
│ └── RandomNetwork.py
├── not_sorted
│ ├── __init__.py
│ ├── bridges.py
│ ├── cluster.py
│ ├── laplacian.py
│ ├── mst.py
│ └── pagerank.py
├── path
│ ├── __init__.py
│ └── path.py
└── structural_holes
├── __init__.py
├── AP_Greedy.py
├── evaluation.py
├── HAM.py
├── HIS.py
├── ICC.py
├── maxBlock.py
├── MaxD.py
├── metrics.py
├── NOBE.py
├── SHII_metric.py
├── strong_connected_component.py
└── weakTie.py
- EasyGraph
- NetworkX
- easygraph-bench Benchmarking code and results of easygraph (Python / C++) and networkx