weight_scheme_cum_temporal: Weight Cumulative Temporal Projection Scheme

In sailuh/kaiaulu: Kaiaulu

Weight Cumulative Temporal Projection Scheme

Description

This weight scheme sums the deleted node adjacent edges when re-wired in the projection graph that *occur before* the temporal edge. See the in line documentation for details.

Usage

weight_scheme_cum_temporal(temporally_ordered_projected_graph)

Arguments

temporally_ordered_projected_graph

A temporally row-wise ordered table semi-processed bipartite projection network resulting from temporal_graph_projection when specifying weight_scheme_function = NA and lag = all_lag.

Details

Note this function assumes the rows of 'temporally_ordered_projected_graph' are temporally ordered. This property is only guaranteed by the temporal_graph_projection function if 'lag = "all_lag"'. For 'lag=one_lag', use weight_scheme_sum_edges or weight_scheme_count_deleted_nodes.

Refer to this weight scheme unit tests for examples.

References

M. Joblin, W. Mauerer, S. Apel, J. Siegmund and D. Riehle, "From Developer Networks to Verified Communities: A Fine-Grained Approach," 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Florence, 2015, pp. 563-573, doi: 10.1109/ICSE.2015.73.

See Also

Other weight_scheme: weight_scheme_count_deleted_nodes(), weight_scheme_pairwise_cum_temporal()

sailuh/kaiaulu documentation built on Dec. 10, 2024, 3:14 a.m.