In the , we showed the character relationship for the by using NetworkX and Gephi. In this post, we will show you how to access data in Nebula Graph by using NetworkX. last post Game of Thrones NetworkX is a modeling tool for the graph theory and complex networks written by Python. With its rich, easy-to-use built-in graphs and analysis algorithms, it’s easy to perform complex network analysis and simulation modeling. NetworkX In NetworkX, a graph (network) is a collection of nodes together with a collection of edges. Attributes are often associated with nodes and/or edges and are optional. Nodes represent data. Edges are uniquely determined by two nodes, representing the relationship between the two nodes. Nodes and edges can also have many attributes so that more information can be stored. The following four basic graph types are provided in NetworkX: Graph: undirected graph.DiGraph: directed graph.MultiGraph: A flexible graph class that allows multiple undirected edges between pairs of nodes.MultiDiGraph: A directed version of a MultiGraph. Create an undirected graph in NetworkX: networkx nx
G = nx.Graph() import as Add nodes: G.add_node( )
G.add_nodes_from([ , , ])
G.add_node( ,name= ,age= ) 1 2 3 4 2 'Tom' 23 Add edges: G.add_edge( , )
G.add_edges_from([( , ),( , )])
g.add_edge( , , start_year= , end_year= ) 2 3 1 2 1 3 1 2 1996 2019 In the last post (part one of this series), we have displayed the community detection algorithm Girvan-Newman provided by NetworkX. Graph Database Nebula Graph NetworkX usually uses local files as the data source, which is totally okay for static network researches. But when the graph network changes a lot, for example, some central nodes are deleted or important network topology changes are introduced, it is a little troublesome to generate, load, and analyze the new static files. At this time, we’d better persist the entire change process in a database, and load subgraphs or whole graphs directly from the database for real-time analysis. This post uses Nebula Graph [3] as the graph database for storing the graph data. Nebula Graph provides two methods to get graph structures: Query to return a subgraph. Scan the underlying storage to get the whole graph. The first method is suitable for obtaining certain nodes and edges that meet the requirements through fine filtering and pruning in a large-scale graph network. Whereas the second method is more suitable for the whole graph analysis. It is usually used in the early stage of the project when conducting heuristic exploration. When you further understand the graph, you can use the first method for fine pruning analysis. By reading the code of the python client for Nebula Graph, we know that the and the file are the APIs that interact with the underlying storage. These files contain a series of interfaces to scan nodes, scan edges, read columns, and so on. The following two interfaces can be used to scan all nodes and edges: nebula-python/nebula/ngStorage/StorageClient.py nebula-python/nebula/ngMeta/MetaClient.py def scanVertex (self, space, returnCols, allCols, limit, startTime, endTime) def scanEdge (self, space, returnCols, allCols, limit, startTime, endTime) 1. Initialize a client and a ScanEdgeProcessor. The ScanEdgeProcessor decodes the edge data read from Nebula Graph: metaClient = MetaClient([( , )])
metaClient.connect()
storageClient = StorageClient(metaClient)
scanEdgeProcessor = ScanEdgeProcessor(metaClient) '192.168.8.16' 45500 2. Initialize the parameters for the scanEdge interface: spaceName = returnCols = {} returnCols[ ] = [ , ]
returnCols[ ] = [ ]
allCols = limit = startTime = endTime = sys.maxsize 'nba' # The name of the graph space to be read. # The edges (or nodes) and their property columns to be returned. 'serve' 'start_year' 'end_year' 'follow' 'degree' False # Return all the property columns or not. When set to False, only the specified property columns in the returnCols are returned. When set to True, all property columns are returned. 100 # Maximum data entry number returned. 0 3. Call the scanPartEdge interface to return the iterator for the canEdgeResponse object: scanEdgeResponseIterator = storageClient.scanEdge(spaceName, returnCols, allCols, limit, startTime, endTime) 4. Keep reading the data in the ScanEdgeResponse object which the iterator points to till all the data are read: scanEdgeResponseIterator.hasNext():
    scanEdgeResponse = scanEdgeResponseIterator.next() scanEdgeResponse :
        print( ) processEdge(space, scanEdgeResponse) while if is None "Error occurs while scaning edge" break In the preceding snippet, the process_edge is a custom function for processing the scanned edge data. This function first uses the scan_edge_processor to decode the data in the scan_edge_response. The decoded data can either be directly printed out or processed for other purposes such as importing these data into the calculation framework NetworkX. 5. Process data. Here we import all the scanned data into Graph G in the NetworkX: result = scanEdgeProcessor.process(space, scanEdgeResponse) edgeName, edgeRows result.rows.items(): row edgeRows:
            srcId = row.defaultProperties[ ].getValue()
            dstId = row.defaultProperties[ ].getValue()
            print( % (srcId, dstId))
            props = {} prop row.properties:
                propName = prop.getName()
                propValue = prop.getValue()
                props[propName] = propValue
            G.add_edges_from([(srcId, dstId, props)]) : def processEdge (space, scanEdgeResponse) # Get the corresponding rows by edge_name for in for in 0 2 '%d -> %d' for in # Add edges to Graph G in the NetworkX Nodes scanning is similar to the preceding process. In addition, for some of the distributed graph calculation frameworks [4], Nebula Graph provides batch reading and storing based on the partitions. We will show you in the later post. Applying Graph Analysis in NetworkX When importing all the nodes and edges into the NetworkX according to the preceding process, we can do some basic graph analysis and calculations: 1. Draw a plot: nx.draw(G, with_labels= , font_weight= ) matplotlib.pyplot plt
plt.show()
plt.savefig( ) True 'bold' import as './test.png' The plot you just drew looks like following: Print out all the nodes and edges in the plot: print( , list(G.nodes))
print( , list(G.edges)) 'nodes: ' 'edges: ' Results printed: nodes:  [ , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ]
edges:  [( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , ), ( , )] 109 119 129 139 149 209 219 229 108 118 128 138 148 208 218 228 107 117 127 137 147 207 217 227 106 116 126 136 146 206 216 226 101 111 121 131 141 201 211 221 100 110 120 130 140 150 200 210 220 102 112 122 132 142 202 212 222 103 113 123 133 143 203 213 223 104 114 124 134 144 204 214 224 105 115 125 135 145 205 215 225 109 100 109 125 109 204 109 219 109 222 119 200 119 205 119 113 129 116 129 121 129 128 129 216 129 221 129 229 129 137 139 138 139 212 139 218 149 130 149 219 209 123 219 130 219 112 219 104 229 147 229 116 229 141 229 144 108 100 108 101 108 204 108 206 108 214 108 215 108 222 118 120 118 131 118 205 118 113 128 116 128 121 128 201 128 202 128 205 128 223 138 115 138 204 138 210 138 212 138 221 138 225 148 127 148 136 148 137 148 214 148 223 148 227 148 213 208 127 208 103 208 104 208 124 218 127 218 110 218 103 218 104 218 114 218 105 228 146 228 145 107 100 107 204 107 217 107 224 117 200 117 136 117 142 127 114 127 212 127 213 127 214 127 222 127 226 127 227 137 136 137 213 137 150 147 136 147 214 147 223 207 121 207 140 207 122 207 134 217 126 217 141 217 124 217 144 106 204 106 212 106 113 116 141 116 126 116 210 116 216 116 121 116 113 116 105 126 216 136 210 136 213 136 214 146 202 146 210 146 215 146 222 146 226 206 123 216 144 216 105 226 140 226 112 226 114 226 144 101 100 101 102 101 125 101 204 101 215 101 113 101 104 111 200 111 204 111 215 111 220 121 202 121 215 121 113 121 134 131 205 131 220 141 124 141 205 141 225 201 145 211 124 221 104 221 124 100 125 100 204 100 102 100 113 100 104 100 144 100 105 110 204 110 220 120 150 120 202 120 205 120 113 140 114 140 214 140 224 150 143 150 213 200 142 200 104 200 145 210 124 210 144 210 115 210 145 102 203 102 204 102 103 102 135 112 204 122 213 122 223 132 225 202 133 202 114 212 103 222 104 103 204 103 114 113 104 113 105 113 125 113 204 133 114 133 144 143 213 143 223 203 135 213 124 213 145 104 105 104 204 104 215 114 115 114 204 134 224 144 145 144 214 204 105 204 125 3. One of the common application is to calculate the shortest path between two nodes: p1 = nx.shortest_path(G, source= , target= )
print( , p1) 114 211 'The shortest path between Node 114 and Node 211: ' Results printed: The shortest path between Node Node :  [ , , , , ] 114 and 211 114 127 208 124 211 4. Calculate the pagerank value for each node in the graph to see their influences: print(nx.pagerank(G)) Results printed: { : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : , : } 109 0.011507076520104863 119 0.007835838669313514 129 0.015304593799331218 139 0.007772926737873626 149 0.0073896601012629825 209 0.0065558926178649985 219 0.014100908598251508 229 0.011454115940170253 108 0.01645334474680034 118 0.01010598371500564 128 0.01594717876199238 138 0.01671097227127263 148 0.015898676579503977 208 0.009437234075904938 218 0.0153795416919104 228 0.005900393773635255 107 0.009745182763645681 117 0.008716335675518244 127 0.021565565312365507 137 0.011642680498867146 147 0.009721031073465738 207 0.01040504770909835 217 0.012054472529765329 227 0.005615576255373405 106 0.007371191843767635 116 0.020955704443679106 126 0.007589432032220849 136 0.015987209357117116 146 0.013922108926721374 206 0.008554794629575304 216 0.011219193251536395 226 0.013613173390725904 101 0.016680863106330837 111 0.010121524312495604 121 0.017545503989576015 131 0.008531567756846938 141 0.014598319866130227 201 0.0058643663430632525 211 0.003936285336338021 221 0.009587911774927793 100 0.02243017302167168 110 0.007928429795381916 120 0.011875669801396205 130 0.0073896601012629825 140 0.01205992633948699 150 0.010045605782606326 200 0.015289870550944322 210 0.017716629501785937 220 0.008666577509181518 102 0.014865431161046641 112 0.007931095811770324 122 0.008087439927630492 132 0.004659566123187912 142 0.006487446038191551 202 0.013579313206377282 212 0.01190888044566142 222 0.011376739416933006 103 0.013438110749144392 113 0.02458154500563397 123 0.01104978432213578 133 0.00743370900670294 143 0.008011123394996112 203 0.006883198710237787 213 0.020392557117890422 223 0.012345866520333572 104 0.024902235588979776 114 0.019369722463816744 124 0.017165705442951484 134 0.008284361176173354 144 0.019363506469972095 204 0.03507634139024834 214 0.015500649025348538 224 0.008320315540621754 105 0.01439975542831122 115 0.007592722237637133 125 0.010808523955754608 135 0.006883198710237788 145 0.014654713389044883 205 0.014660118545887803 215 0.01337467974572934 225 0.009909720748343093 In addition, similar to the first post in the series, you can access to to make the visualization look better. Gephi The code used this post is placed for your reference. here Previously published at https://nebula-graph.io/posts/game-of-thrones-relationship-networkx-gephi-nebula-graph-part-two/

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Gremlin vs Cypher vs nGQL: Graph Query Language Comparison 

Effective Tools To Make A Great Relationships Analysis in Game of Thrones [Part 2]

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