networkx | Network Analysis in Python

 by   networkx Python Version: 3.2rc0 License: Non-SPDX

kandi X-RAY | networkx Summary

kandi X-RAY | networkx Summary

networkx is a Python library typically used in User Interface applications. networkx has no bugs, it has no vulnerabilities, it has build file available and it has high support. However networkx has a Non-SPDX License. You can install using 'pip install networkx' or download it from GitHub, PyPI.

Network Analysis in Python

            kandi-support Support

              networkx has a highly active ecosystem.
              It has 12745 star(s) with 2929 fork(s). There are 281 watchers for this library.
              There were 3 major release(s) in the last 6 months.
              There are 184 open issues and 2801 have been closed. On average issues are closed in 65 days. There are 203 open pull requests and 0 closed requests.
              It has a positive sentiment in the developer community.
              The latest version of networkx is 3.2rc0

            kandi-Quality Quality

              networkx has 0 bugs and 0 code smells.

            kandi-Security Security

              networkx has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              networkx code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              networkx has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              networkx releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              networkx saves you 63373 person hours of effort in developing the same functionality from scratch.
              It has 81323 lines of code, 6257 functions and 640 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed networkx and discovered the below as its top functions. This is intended to give you an instant insight into networkx implemented functionality, and help decide if they suit your requirements.
            • Constructs a networkx problem
            • Augment the flow
            • Find a cycle
            • Add an edge to the subtree
            • Build a LFR curve
            • Generate random integers
            • Return a random value from a sequence
            • Generate communities
            • R Return the edge connectivity of a graph
            • Return an iterator over all simple paths in a graph
            • R Compute the group of a graph
            • Compute the matrix attribute of a graph
            • R Compute a Gomory - Hu tree representation
            • Decompose a graph into a directed graph
            • R Compute minimum edge cut
            • Return an extended barabasi - analogy graph
            • Return a list of edges
            • Generate a spanning graph
            • R Return a pairwise adjacency matrix
            • Calculate the capacity of a graph
            • Compute a quotient graph
            • Finds a thresholding threshold for a given transition graph
            • R Draw a forest tree
            • Simulate a simulatedannealing t
            • Group between two nodes
            • R Return a set of all node cuts
            Get all kandi verified functions for this library.

            networkx Key Features

            No Key Features are available at this moment for networkx.

            networkx Examples and Code Snippets

            Build a networkx graph .
            pythondot img1Lines of Code : 48dot img1License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def build_graph(device, dtype, data_format, input_shape, filter_shape, strides,
                            padding, num_iters, warmup_iters):
              """builds a graph containing a sequence of conv2d operations.
                device: String, the device to run on.
            Generate a networkx solution .
            pythondot img2Lines of Code : 33dot img2License : Permissive (MIT License)
            copy iconCopy
            def solution(filename: str = "p107_network.txt") -> int:
                Find the maximum saving which can be achieved by removing redundant edges
                whilst ensuring that the network remains connected.
                >>> solution("test_network.txt")
            Constructs a networkx graph .
            pythondot img3Lines of Code : 16dot img3License : Permissive (MIT License)
            copy iconCopy
            def construct_graph(cluster, nodes):
                X = cluster[max(cluster.keys())]
                cluster[max(cluster.keys()) + 1] = "Header"
                graph = {}
                for i in X:
                    if tuple(["Header"]) in graph:
            Recovering nodes from indices in 2D grid graph using Python
            Pythondot img4Lines of Code : 7dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            nodes = {n: i for i, n in enumerate(G.nodes, start=1)}
            indices= [(1, 1), (2, 2)]
            result = [nodes[i] for i in indices]
            [5, 9]
            Creating a DiGraph using networkx
            Pythondot img5Lines of Code : 25dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df.Start_date = pd.to_datetime(df.Start_date)  # Just to make sure
            df = df.sort_values("Start_date")
            df["Next_Project"] = df.groupby("Name").Project.shift(-1)
               Name Project Start_date Next_Project
            0   Joe       A
            How to adjust radius size when plotting circular graph with networkx?
            Pythondot img6Lines of Code : 35dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import networkx as nx
            from matplotlib import pyplot as plt
            from matplotlib.pyplot import figure
            plt.rcParams["figure.figsize"] = (8,8)
            labels1={k:str(k) for k in range(n1)}
            Generating random connected graph with adjacency matrix in Python
            Pythondot img7Lines of Code : 14dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import networkx as nx
            G = nx.grid_2d_graph(4, 4)
            new_nodes = {e: n for n, e in enumerate(G.nodes, start=1)}
            new_edges = [(new_nodes[e1], new_nodes[e2]) for e1, e2 in G.edges]
            G = nx.Graph()
            Build an adjacency matrix from distances between cosmological objects
            Pythondot img8Lines of Code : 36dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            from sklearn.neighbors import radius_neighbors_graph
            # Your example data in runnable format
            dx = np.array([2.63370612e-01, 3.48350511e-01, -1.23379511e-02, 
                           6.63767411e+00, 1.32910697e+01,  8.75469902e+00])
            dy = np.array([0
            Modifying adjacency matrix based on edges
            Pythondot img9Lines of Code : 5dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            >>> H1 - np.identity(3)*H1.sum(axis=1)
            array([[-2.,  1.,  1.],
                   [ 1., -2.,  1.],
                   [ 1.,  1., -2.]])
            Getting adjacency matrix from random graph in Python
            Pythondot img10Lines of Code : 3dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            A = nx.adjacency_matrix(G)

            Community Discussions


            Generate all digraphs of a given size up to isomorphism
            Asked 2022-Apr-02 at 01:08

            I am trying to generate all directed graphs with a given number of nodes up to graph isomorphism so that I can feed them into another Python program. Here is a naive reference implementation using NetworkX, I would like to speed it up:



            Answered 2022-Mar-31 at 13:58

            98-99% of computation time is used for the isomorphism tests, so the name of the game is to reduce the number of necessary tests. Here, I create the graphs in batches such that graphs have to be tested for isomorphisms only within a batch.

            In the first variant (version 2 below), all graphs within a batch have the same number of edges. This leads to appreaciable but moderate improvements in running time (2.5 times faster for graphs of size 4, with larger gains in speed for larger graphs).

            In the second variant (version 3 below), all graphs within a batch have the same out-degree sequence. This leads to substantial improvements in running time (35 times faster for graphs of size 4, with larger gains in speed for larger graphs).

            In the third variant (version 4 below), all graphs within a batch have the same out-degree sequence. Additionally, within a batch all graphs are sorted by in-degree sequence. This leads to modest improvements in speed compared to version 3 (1.3 times faster for graphs of size 4; 2.1 times faster for graphs of size 5).



            How to efficiently generate multiple random graphs with random edge weights in networkx
            Asked 2022-Feb-25 at 17:19

            I would like to generate multiple Erdos-Renyi graphs with random edge weights. However, my code works quite slow since there are two nested loops. I was wondering if someone can help me with improving my code.



            Answered 2022-Feb-25 at 17:19

            This benchmark shows the performance of many graphs libraries (from different languages). It confirms NetworkX is very slow. The graph-tool Python package seems a significantly faster alternative to NetworkX. Please note that the performance of a given package is dependent of what you want to achieve because the performance of a graph algorithm is very dependent of the chosen internal representation.



            How to convert each row in data frame to a node with attributes?
            Asked 2022-Feb-22 at 13:36

            Given a sample data frame df:



            Answered 2022-Feb-22 at 13:36

            The from_pandas_edgelist function requires both the source and target.



            Networkx - entropy of subgraphs generated from detected communities
            Asked 2022-Feb-02 at 21:27

            I have 4 functions for some statistical calculations in complex networks analysis.



            Answered 2022-Jan-26 at 15:38

            It looks like, in calculate_community_modularity, you use greedy_modularity_communities to create a dict, modularity_dict, which maps a node in your graph to a community. If I understand correctly, you can take each subgraph community in modularity_dict and pass it into shannon_entropy to calculate the entropy for that community.

            pseudo code

            this is pseudo code, so there may be some errors. This should convey the principle, though.

            after running calculate_community_modularity, you have a dict like this, where the key is each node, and the value is that which the community belongs to



            Edge weight in networkx
            Asked 2022-Feb-02 at 14:20

            How do I assign to each edge a weight equals to the number of times node i and j interacted from an edge list?



            Answered 2022-Feb-02 at 14:20

            You can first aggregate the pandas tables to have a weight column, and then load it to networkx with that edge column:



            Removing Edges from a Graph
            Asked 2022-Jan-19 at 09:51

            I made a graph with weights. I am trying to remove Node1's weights. I removed the Node1 but it's weights are still there. How can I remove the weights too? My code:



            Answered 2022-Jan-19 at 04:05

            The reason why the edge weights are plotted is that the weights are not updated after removing a node. Hence, pos and labels in your script should be recalculated after removing the node:



            Removing a Node from a Graph
            Asked 2022-Jan-18 at 20:44

            I made a graph with weights. I am trying to remove Node1. I can remove in console but when I draw the graph, it's still there.

            My code:



            Answered 2022-Jan-18 at 16:42

            Simply call nx.draw(G, pos, with_labels=True) again before plotting new graph. Your code should look like:



            How to cluster pairs without networkx
            Asked 2022-Jan-11 at 01:24

            I have a list of lists:

            [['suzy', 'joe', ...], ['suzy', 'tom', ...], ['paul', 'kristy',...], ['kristy', 'griffin',...], ...]

            My desired output is to cluster the pairs into two lists:



            Answered 2022-Jan-11 at 01:10

            The networkx solution you were referring to created an undirected graph, where the nodes were names and the edges were observed pairings. The clusters are then the connected components of the graph, which you can read off using a graph traversal algorithm (e.g. DFS, BFS).

            So, you have two options:

            1. Construct your own graph implementation, without using networkx, doing the above. Nothing I've just described are things that you can only do with networkx.

            2. Use the disjoint sets data structure to generate the groupings. As the name suggests, we maintain a collection of disjoint sets (where a set represents a cluster of people). Whenever we see a pair, we union the clusters that the two people in the pairing originally belonged to. An implementation of union-find, as well as its application to the problem you've described, is given below:



            Counting triangles in a graph by iteratively removing high-degree nodes
            Asked 2022-Jan-03 at 15:15

            Computing nx.triangles(G) on an undirected graph with about 150 thousand nodes and 2 million edges, is currently very slow (on the scale of 80 hours). If the node degree distribution is highly skewed, is there any problem with counting triangles using the following procedure?



            Answered 2022-Jan-02 at 20:57

            Assuming the graph is not directed (ie. G.is_directed() == False), the number of triangles can be efficiently found by finding nodes that are both neighbors of neighbors and direct neighbors of a same node. Pre-computing and pre-filtering the neighbors of nodes so that each triangle is counted only once helps to improve a lot the execution time. Here is the code:



            How to load in graph from networkx into PyTorch geometric and set node features and labels?
            Asked 2022-Jan-02 at 14:31

            Goal: I am trying to import a graph FROM networkx into PyTorch geometric and set labels and node features.

            (This is in Python)


            1. How do I do this [the conversion from networkx to PyTorch geometric]? (presumably by using the from_networkx function)
            2. How do I transfer over node features and labels? (more important question)

            I have seen some other/previous posts with this question but they weren't answered (correct me if I am wrong).

            Attempt: (I have just used an unrealistic example below, as I cannot post anything real on here)

            Let us imagine we are trying to do a graph learning task (e.g. node classification) on a group of cars (not very realistic as I said). That is, we have a group of cars, an adjacency matrix, and some features (e.g. price at the end of the year). We want to predict the node label (i.e. brand of the car).

            I will be using the following adjacency matrix: (apologies, cannot use latex to format this)

            A = [(0, 1, 0, 1, 1), (1, 0, 1, 1, 0), (0, 1, 0, 0, 1), (1, 1, 0, 0, 0), (1, 0, 1, 0, 0)]

            Here is the code (for Google Colab environment):



            Answered 2021-Dec-22 at 18:32

            The easiest way is to add all information to the networkx graph and directly create it in the way you need it. I guess you want to use some Graph Neural Networks. Then you want to have something like below.

            1. Instead of text as labels, you probably want to have a categorial representation, e.g. 1 stands for Ford.
            2. If you want to match the "usual convention". Then you name your input features x and your labels/ground truth y.
            3. The splitting of the data into train and test is done via mask. So the graph still contains all information, but only part of it is used for training. Check the PyTorch Geometric introduction for an example, which uses the Cora dataset.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install networkx

            You can install using 'pip install networkx' or download it from GitHub, PyPI.
            You can use networkx like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
          • PyPI

            pip install networkx

          • CLONE
          • HTTPS


          • CLI

            gh repo clone networkx/networkx

          • sshUrl


          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link