gephi

What is a parallel edge in Gephi and how do I deal with them when importing an edgelist?

旧城冷巷雨未停 提交于 2019-12-05 03:59:14
When I import my .gexf file, it flags a bunch of parallel edges. I don't know what parallel edges are and why there's an issue with my graph. I'm trying to create a dynamic gephi graph. If you have several edges between one pair of two nodes, these edges are parallel to each other, so they are the parallel edges. There is no way to represent parallel edges in Gephi. So the Gephi will automatically merge your parallel edges into one edge. (But fortunately the weight will be also merged, e.g. if you put 3 parallel edges, the weight of new merged edge will be the sum of weights of given 3

Exporting twitter data to Gephi using R

安稳与你 提交于 2019-12-03 08:51:07
I have compiled a dataset consisting of thousands of tweets using R. The dataset basically looks like this: Data <- data.frame( X = c(1,2), text = c("Hello @User1 #hashtag1, hello @User2 and @User3, #hashtag2", "Hello @User2 #hashtag3, hello @User1 and @User3, #hashtag4"), screenname = c("author1", "author2") ) Now I want to export this dataset to a Gephi supported graph format (see Supported Graph Formats - Gephi ) Whenever an "author" mentions a @user in the text, there should be a direct link from the author to the user. In the case above, the results should be like this: author1 -> @User2

How to create Gephi network graphs from Python?

杀马特。学长 韩版系。学妹 提交于 2019-12-03 08:17:45
问题 I have just found out about GephiStreamer. https://pypi.python.org/pypi/GephiStreamer Using this package one can send instructions from python to Gephi to create nodes and edges in Gephi. # Create a node with a custom_property node_a = graph.Node("A",custom_property=1) # Create edge edge_ab = graph.Edge(node_a,node_b,custom_property="hello") stream.add_edge(edge_ab) Similarly I want to do everything in Gephi through Python. Here is what I typically do. ex: steps: load nodes load edges

Neo4j Configuration with Gephi

匿名 (未验证) 提交于 2019-12-03 01:36:02
可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效,请关闭广告屏蔽插件后再试): 问题: I want to use Neo4j to store a number of graphs I created in python. I was using Gephi for visualization, and I thought the export to Neo4j plugin would be a very simple way to get the data across. The problem is that the server is seemingly not recognizing the neostore...db files that Gephi generated. I'm guessing I configured things incorrectly, but is there a way to fix that? Alternatively, I'm also open to importing the files directly. I have two files: one with node titles and attributes and another with an edge list of title to title.

How to create Gephi network graphs from Python?

北城以北 提交于 2019-12-02 20:58:14
I have just found out about GephiStreamer. https://pypi.python.org/pypi/GephiStreamer Using this package one can send instructions from python to Gephi to create nodes and edges in Gephi. # Create a node with a custom_property node_a = graph.Node("A",custom_property=1) # Create edge edge_ab = graph.Edge(node_a,node_b,custom_property="hello") stream.add_edge(edge_ab) Similarly I want to do everything in Gephi through Python. Here is what I typically do. ex: steps: load nodes load edges calculate betweeness centrality change the size/color of nodes as per their centrality scores change the graph

Graph visualization frameworks

北城余情 提交于 2019-12-01 15:31:30
I want to visualize a Neo4j database in a graphical application. (I'm doing this primarily as a learning exercise). I want it to behave almost identically to the visualization in Neo4j's WebAdmin: When the graphic appears, I want to see the nodes moving around as they settle into their final locations, and I want the user to be able to click and drag node icons around the screen and have them stick where they are dropped, with the rest of the graph adjusting to account for the change. I have two questions. What are the technical terms for the three requirements crudely described in the

Gephi简单导入数据

家住魔仙堡 提交于 2019-12-01 12:13:48
使用工具 Gephi-0.9.2 事前要导入的数据 Node.csv 节点:名称可以所以定,格式 .csv Edge.csv 边:名称可以所以定,格式 .csv 导入操作 新建项目 导入节点 新建列要与表格头部的名称一致 选择要导入的节点文件 CSV 常规选项 > 选择分隔符(逗号)-导入数据(节点表格)-字符集(GB2312) 输入设置 > 选择导入的类型 输入报告 > 显示是否导入成功-选择图的类型-选择工作区 导入成功的数据 概览 > 查看导入之后的节点 导入边 选择 边 先导出一下 修改 边 文件的头信息 - 不修改导入会失败 选择要导入的边文件 CSV 常规选项 > 选择分隔符(逗号)-导入数据(边表格)-字符集(GB2312) 输入设置 > 默认点击完成 输入报告 > 显示是否导入成功-选择图的类型-选择工作区 导入成功的数据 概览 > 查看导入之后的节点和边 保存 来源: https://www.cnblogs.com/taopanfeng/p/11684966.html

Adding Color Attribute to Nodes on NetworkX to export to Gephi

点点圈 提交于 2019-11-30 23:02:24
I am making a graph using NetworkX to export to visualize with Gephi. I've been adding various attributes to the nodes in my graph, without issue, until I tried adding colors. Does anyone know how to export a graph with "colored" nodes using networkx? (I've been writing into a gexf file, but don't care if it is another format as long as it is compatible with Gephi.) Here is the code in which I make the graph: def construct_weighted_graph(nodes, distance_dict, weighting_function, color = True): G = nx.Graph() #nodes automatically added when edges added. for sequence in nodes: #loop through and

Neo4j visualisation-manipulate the graph

我们两清 提交于 2019-11-29 18:05:33
I am currently using Neo4j Python rest client and I would like to visualise the graph and be able to amend it, add new nodes relationships etc. Also I would like the changes in the neo4j database as well. Is that possible? Also can self-loops be visualised? I have read about D3.js and Neoclipse and Gephi in http://www.neo4j.org/develop/visualize but I am not sure which one to use. Thanks in advance. You can manipulate the graph in Neo4J using Cypher, in particular using a the REST API. Any kind of tool that allows you to interface with Cypher is potentially able to do what you are asking: it

R: Gephi: manipulating dataframe to use with write.gexf

∥☆過路亽.° 提交于 2019-11-29 16:07:45
I am trying to manipulate a data frame. As an example: say I have a dataframe containing customers and the shops they visit: df = data.frame(customers = c("a", "b", "b", "c", "c"), shop_visited = c("X", "X", "Y", "X", "Z")) customers shop_visited a X b X b Y c X c Z Summarizing this dataframe: one customer ( b ) shops at X and also at Y ; one customer ( b ) shops at Y and also at X ; one customer ( c ) shops at X and also at Z ; one customer ( c ) shops at Z and also at X Or, more succinctly: relations = data.frame(source = c("X","Y", "X", "Z"), target = c("Y","X","Z","X")) source target X Y Y