GFQL Hop Matcher

GFQL Hop Matcher#

Hop is the core primitive behind a single matcher step in chain.

Calling hop directly has performance benefits over calling chain so may be helpful for larger graphs.

For cross-step constraints, use g.gfql([…], where=[…]) (or the explicit Chain(…, where=[…]) form); see GFQL WHERE (Same-Path Constraints).

Graph hop/traversal operations for PyGraphistry.

NOTE: Excluded from pyre (.pyre_configuration) - hop() complexity causes hang. Use mypy.

graphistry.compute.hop.hop(self, nodes=None, hops=1, *, min_hops=None, max_hops=None, output_min_hops=None, output_max_hops=None, label_node_hops=None, label_edge_hops=None, label_seeds=False, to_fixed_point=False, direction='forward', edge_match=None, source_node_match=None, destination_node_match=None, source_node_query=None, destination_node_query=None, edge_query=None, return_as_wave_front=False, include_zero_hop_seed=False, target_wave_front=None, engine=EngineAbstract.AUTO)#

Given a graph and some source nodes, return subgraph of all paths within k-hops from the sources

This can be faster than the equivalent chain([…]) call that wraps it with additional steps

See chain() examples for examples of many of the parameters

g: Plotter nodes: dataframe with id column matching g._node. None signifies all nodes (default). hops: consider paths of length 1 to ‘hops’ steps, if any (default 1). Shorthand for max_hops. min_hops/max_hops: inclusive traversal bounds; defaults preserve legacy behavior (min=1 unless max=0; max defaults to hops). output_min_hops/output_max_hops: optional output slice applied after traversal; defaults keep all traversed hops up to max_hops. Useful for showing a subrange (e.g., min/max = 2..4 but display only hops 3..4). label_node_hops/label_edge_hops: optional column names for hop numbers (omit or None to skip). Nodes record the first retained hop step they are reached (1 = first expansion); when min_hops prunes shorter branches, labels reflect the shortest retained path. Edges record the hop step that traversed them. label_seeds: when True and labeling, also write hop 0 for seed nodes in the node label column. to_fixed_point: keep hopping until no new nodes are found (ignores hops) direction: ‘forward’, ‘reverse’, ‘undirected’ edge_match: dict of kv-pairs to exact match (see also: filter_edges_by_dict) source_node_match: dict of kv-pairs to match nodes before hopping (including intermediate) destination_node_match: dict of kv-pairs to match nodes after hopping (including intermediate) source_node_query: dataframe query to match nodes before hopping (including intermediate) destination_node_query: dataframe query to match nodes after hopping (including intermediate) edge_query: dataframe query to match edges before hopping (including intermediate) return_as_wave_front: Exclude starting node(s) in return, returning only encountered nodes include_zero_hop_seed: internal Cypher opt-in for exact zero-hop path semantics Note: chain() reverse passes set return_as_wave_front=True and use target_wave_front to constrain reachability. target_wave_front: Only consider these nodes + self._nodes for reachability engine: ‘auto’, ‘pandas’, ‘cudf’ (GPU)

Parameters:

  • self (Plottable)

  • nodes (Any | None)

  • hops (int | None)

  • min_hops (int | None)

  • max_hops (int | None)

  • output_min_hops (int | None)

  • output_max_hops (int | None)

  • label_node_hops (str | None)

  • label_edge_hops (str | None)

  • label_seeds (bool)

  • to_fixed_point (bool)

  • direction (str)

  • edge_match (dict | None)

  • source_node_match (dict | None)

  • destination_node_match (dict | None)

  • source_node_query (str | None)

  • destination_node_query (str | None)

  • edge_query (str | None)

  • return_as_wave_front (bool)

  • include_zero_hop_seed (bool)

  • target_wave_front (Any | None)

  • engine (EngineAbstract | str)

Return type:

Plottable

graphistry.compute.hop.query_if_not_none(query, df)#
Parameters:

  • query (str | None)

  • df (Any)

Return type:

Any