htna:
Heterogeneous Transition Network Analysis
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htna:
Heterogeneous Transition Network Analysis
htna is an R package for Heterogeneous Transition
Network Analysis (HTNA) that models processes or interactions between a
mix two or more actor groups (e.g. Human and AI) as a single network.
HTNA builds on the traditions of Transition Network Analysis (TNA) and
Co-occurrence Network Analysis (CNA) and maintains the rigor of either
method.
The package provides a focused API on top of the Nestimate estimation engine and the cograph rendering engine: build a network over the combined sequence while preserving the actor partition, so downstream bootstrap, permutation, reliability, centrality, and plotting functions treat each actor’s codes as a distinct node group.
Install the released version of htna from CRAN:
install.packages("htna")Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github("sonsoleslp/htna")library("htna")Load the example data shipped with htna:
data(human_ai)build_htna() takes a data frame with a column indicating
the actor type, combines the sequences, estimates transition
probabilities, and stores the actor partition on the resulting
network:
net <- build_htna(human_ai, actor_type = "actor_type")plot_htna() auto-detects the actor groups and renders
them with distinct colours:
plot_htna(net, threshold = 0.05, layout = "circular")
By default extract_meta_paths() enumerates concrete
state-level patterns and tags each row with the type-level template it
instantiates:
extract_meta_paths(net)
#> Patterns (state-level) over 429 sequences
#> Rows: 5445 | Lengths: 2, 3, 4 | Gaps: 0
#> schema meta_schema length gap count n_seq support frequency lift
#> Request->Specify Human->Human 2 0 1042 402 0.937 0.055 2.27
#> Ask->Plan AI->AI 2 0 964 346 0.807 0.051 4.87
#> Execute->Request AI->Human 2 0 921 269 0.627 0.049 1.80
#> Request->Execute Human->AI 2 0 904 299 0.697 0.048 1.77
#> Specify->Execute Human->AI 2 0 796 261 0.608 0.042 1.66
#> Specify->Ask Human->AI 2 0 784 292 0.681 0.041 2.20
#> Check->Execute Human->AI 2 0 534 243 0.566 0.028 2.50
#> Plan->Request AI->Human 2 0 503 268 0.625 0.027 1.98
#> Request->Ask Human->AI 2 0 459 222 0.517 0.024 1.21
#> Execute->Frustrate AI->Human 2 0 451 230 0.536 0.024 1.50
#> ... (5435 more)Filter to concrete instances of a type-level template. Schema parts
can mix type names, concrete codes, and *:
extract_meta_paths(net, schema = "Human->AI->Human")
#> State-level instances of schema 'Human->AI->Human' over 429 sequences
#> Rows: 163 | Lengths: 3 | Gaps: 0
#> schema meta_schema length gap count n_seq support
#> Request->Execute->Request Human->AI->Human 3 0 401 194 0.452
#> Specify->Execute->Request Human->AI->Human 3 0 176 107 0.249
#> Request->Ask->Request Human->AI->Human 3 0 130 91 0.212
#> Check->Execute->Request Human->AI->Human 3 0 123 96 0.224
#> Specify->Ask->Request Human->AI->Human 3 0 120 84 0.196
#> Specify->Execute->Frustrate Human->AI->Human 3 0 114 88 0.205
#> Request->Execute->Frustrate Human->AI->Human 3 0 106 88 0.205
#> Request->Execute->Inquire Human->AI->Human 3 0 97 76 0.177
#> Specify->Ask->Frustrate Human->AI->Human 3 0 86 70 0.163
#> Inquire->Execute->Request Human->AI->Human 3 0 73 52 0.121
#> frequency lift
#> 0.112 5.00
#> 0.049 2.33
#> 0.036 2.19
#> 0.034 3.67
#> 0.033 2.15
#> 0.032 2.57
#> 0.030 2.24
#> 0.027 4.40
#> 0.024 2.61
#> 0.020 3.31
#> ... (153 more)
extract_meta_paths(net, schema = "Human->Ask->*")
#> State-level instances of schema 'Human->Ask->*' over 429 sequences
#> Rows: 63 | Lengths: 3 | Gaps: 0
#> schema meta_schema length gap count n_seq support
#> Specify->Ask->Plan Human->AI->AI 3 0 340 197 0.459
#> Frustrate->Ask->Plan Human->AI->AI 3 0 214 187 0.436
#> Request->Ask->Plan Human->AI->AI 3 0 139 106 0.247
#> Request->Ask->Request Human->AI->Human 3 0 130 91 0.212
#> Specify->Ask->Request Human->AI->Human 3 0 120 84 0.196
#> Specify->Ask->Frustrate Human->AI->Human 3 0 86 70 0.163
#> Inquire->Ask->Plan Human->AI->AI 3 0 53 45 0.105
#> Refine->Ask->Plan Human->AI->AI 3 0 52 46 0.107
#> Request->Ask->Frustrate Human->AI->Human 3 0 50 44 0.103
#> Specify->Ask->Specify Human->AI->Human 3 0 49 41 0.096
#> frequency lift
#> 0.170 11.65
#> 0.107 11.71
#> 0.070 4.48
#> 0.065 2.19
#> 0.060 2.15
#> 0.043 2.61
#> 0.027 6.22
#> 0.026 6.57
#> 0.025 1.43
#> 0.025 0.93
#> ... (53 more)Pass level = "type" for the type-level meta-path
summary:
extract_meta_paths(net, level = "type")
#> Meta-paths (type-level) over 429 sequences
#> Rows: 28 | Lengths: 2, 3, 4 | Gaps: 0
#> schema length gap count n_seq support frequency lift
#> Human->AI 2 0 5970 428 0.998 0.316 1.28
#> AI->Human 2 0 5693 424 0.988 0.301 1.22
#> Human->Human 2 0 4674 422 0.984 0.247 0.79
#> AI->AI 2 0 2581 403 0.939 0.136 0.70
#> Human->AI->Human 3 0 3593 402 0.937 0.194 1.41
#> Human->Human->AI 3 0 3172 422 0.984 0.172 1.25
#> AI->Human->Human 3 0 2828 403 0.939 0.153 1.11
#> AI->Human->AI 3 0 2744 383 0.893 0.148 1.36
#> Human->AI->AI 3 0 2189 403 0.939 0.118 1.09
#> AI->AI->Human 3 0 2100 397 0.925 0.114 1.04
#> ... (18 more)tna –
Transition Network Analysis for homogeneous sequences.Nestimate
– Network estimation, bootstrap, permutation, reliability, and
centrality.cograph
– Network visualisation and rendering.codyna
– Sequence patterns, outcomes, and indices.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.