Networkx with neo4j back-end

Related tags

General Utilities neo4jnx
Overview
  1. Dump networkx graph into nodes/relations TSV
from neo4jnx.tsv import graph_to_tsv
g = pklload('indranet_dir_graph.pkl')
graph_to_tsv(g, 'docker/nodes.tsv.gz', 'docker/edges.tsv.gz')
  1. Run Docker build for neo4j which loads dumped TSVs
cd docker
docker build --tag neo4j_test:latest .
  1. Launch Docker container
docker run -d -it -p 7475:7474 -p 7688:7687 neo4j_test:latest
  1. Instantiate Neo4jDiGraph object with config matching the launched container
from neo4jnx import Neo4jDiGraph
g = Neo4jDiGraph(neo4j_url='neo4j://localhost:7688',
                 neo4j_auth=('neo4j', 'admin'),
                 property_loaders={'statements': json.loads,
                                   'belief': float, 'weight': float,
                                   'z_score': float, 'corr_weight': float})
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Comments
  • Extend graph methods

    Extend graph methods

    This PR extends and updates the graph methods available in the Neo4jDiGraph.

    Tests of algorithms are added.

    A couple of notes about the usage of the graph:

    • In shortest_simple_paths in indra.explanation.pathfinding, g[u][v] is used for edge lookup; g.edges[(u, v)] should be used instead as this uses one query instead of two: https://github.com/sorgerlab/indra/blob/master/indra/explanation/pathfinding/pathfinding.py#L158, the same goes for open_dijkstra_search: https://github.com/sorgerlab/indra/blob/master/indra/explanation/pathfinding/pathfinding.py#L846-L848
    • g.reverse is used in open_dijkstra_search in indra.explanation.pathfinding to distinguish between up- or downstream search. g.reverse is currently not implemented, but could be replaced by aliasing successor/predecessor lookup like its done in e.g. get_sorted_neighbors (used in bfs_search): https://github.com/sorgerlab/indra/blob/master/indra/explanation/pathfinding/util.py#L121-L146
    • Weighted mesh searches are not available as they require the graph to be writable, see e.g. shortest_simple_paths
    • simple_paths_with_constraints in indra.explanation.pathfinding is very slow, this can be mitigated a bit (~1 order of magnitude) by using g.succ[n] instead of g[n] for lookup in the algorithm. We can also see if __getitem__ for the graph could be remade to be faster.
    opened by kkaris 0
Owner
Benjamin M. Gyori
Sysbio & AI researcher. Human-machine collaboration, probabilistic modeling, knowledge assembly, text mining @ indra.bio, dialogue.bio, emmaa.indra.bio.
Benjamin M. Gyori
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