Package index • Dynet

Construction

One constructor for four log shapes, and the two verbs that read a network back out as a time series.

dynet(): Build a temporal network
as_dynet(): Convert an object to a Dynet temporal network
as_dynet(<networkDynamic>): Import a networkDynamic object
events(): Edge formation and dissolution over time
snapshots(): The network sliced into snapshots

Editing

Rewrite the spell and vertex tables without breaking time. Every verb returns a new network and leaves the input unchanged.

add_nodes(): Add nodes to a temporal network
add_ties(): Add temporal ties
add_arcs(): Add directed temporal arcs
add_vertex_spells(): Add declared vertex-activity spells
remove_nodes(): Remove nodes from a temporal network
remove_ties(): Remove temporal ties
remove_arcs(): Remove directed temporal arcs
remove_vertex_spells(): Remove declared vertex-activity components
update_nodes(): Update static node attributes
update_ties(): Update temporal ties and their attributes
update_vertex_spells(): Update declared vertex-activity components
rename_nodes(): Rename nodes everywhere in a temporal network
rename_sessions(): Rename session walls
set_observations(): Replace observation support
set_tie_sessions(): Assign or remove tie sessions
set_vertex_spells(): Replace declared vertex activity
clear_observations(): Restore implicit observation support

Measurement

The measuring verbs. All take the same four grid arguments - start, end, step, window - and return a tidy one-row-per-observation table.

centrality_series(): Time-varying vertex centrality
path_centrality(): Closeness and betweenness on time-respecting paths
reachability(): Reachability of every vertex
metrics(): Time-varying graph-level structure
mixing(): Mixing between vertex groups over time
pshifts(): Gibson participation shifts from raw temporal turns
burstiness(): Burstiness and memory of each vertex's activity
durations(): How long each relationship lasted
similarity(): Similarity between the networks at each pair of time points

Paths

Time-respecting paths, and the four ways of looking at what they found.

paths(): Time-respecting paths from a vertex
pathways(): Most frequent time-respecting routes
path_network(): Build the union network of optimal temporal paths
path_trajectories(): Optimal temporal routes as a counted trajectory tree
plot_path_trajectories(): Draw optimal temporal paths as a trajectory tree

Structure

Turn a temporal network into another object: a time-expanded projection, a static weighted network, or a subgraph.

projection(): Project a temporal network into directed vertex-time states
collapse_network(): Collapse temporal activity to a static weighted network
induce_subgraph(): Extract an induced temporal subgraph

Animation

animate(): Animate a temporal network over its measurement grid

Data

Bundled logs used throughout the documentation and examples.

forum_people: People in the discussion forum
forum_posts: Discussion forum posts
mooc_people: Participants in a MOOC discussion forum
mooc_posts: Posts in a MOOC discussion forum
school_contacts: Student contacts recorded as intervals
seminar_attendance: Seminar attendance
synthdata: Synthetic code-transition network (Trees of Thought stand-in)
thought_chains: Trees of Thought reply links, augmented by simulation

Methods

Print, summary, plot and as.data.frame for every result class.

Dynet Dynet-package: Dynet: Tidy Temporal Network Analysis
dyn_centrality(): Deprecated name for centrality_series() and path_centrality()
dyn_reachability(): Deprecated name for reachability()
print(<dynet>): Print a temporal network
print(<dynet_animation>): Print an animation's bin table
print(<dynet_collapsed>): Print a collapsed temporal network
print(<dynet_collapsed_list>): Print session-specific collapsed networks
print(<dynet_metric>): Print a temporal measure
print(<dynet_path_trajectories>): Print a temporal trajectory tree
print(<dynet_paths>): Print time-respecting paths
print(<dynet_pathways>): Print ranked pathways
print(<dynet_projection>): Print a time-projected network
print(<dynet_pshifts>): Print participation shift counts
print(<dynet_similarity>): Print time-bin similarity
print(<dynet_snapshot>): Print snapshot edges
summary(<dynet>): Describe a temporal network
summary(<dynet_animation>): Summarise an animation
summary(<dynet_collapsed_list>): Summarise session-specific collapsed networks
summary(<dynet_metric>): Summarise a temporal measure
summary(<dynet_path_trajectories>): Summarise path trajectories
summary(<dynet_paths>): Summarise time-respecting paths
summary(<dynet_pathways>): Summarise ranked pathways
summary(<dynet_projection>): Summarise a time projection
summary(<dynet_pshifts>): Summarise participation shifts by family
summary(<dynet_similarity>): Summarise snapshot similarity
summary(<dynet_snapshot>): Summarise snapshot edges by time bin
plot(<dynet>): Draw a temporal network
plot(<dynet_collapsed>): Draw a collapsed temporal network
plot(<dynet_metric>): Plot a temporal measure
plot(<dynet_path_network>): Draw a path network
plot(<dynet_path_trajectories>): Plot path trajectories
plot(<dynet_paths>): Plot time-respecting paths when a valid renderer exists
plot(<dynet_pathways>): Plot pathways on a time axis
plot(<dynet_pshifts>): Plot participation shift counts
plot(<dynet_similarity>): Draw time-bin similarity as a heatmap
plot(<dynet_snapshot>): Plot how many ties each snapshot holds
plot_path_trajectories(): Draw optimal temporal paths as a trajectory tree
head(<dynet_metric>): First rows of a temporal measure
tail(<dynet_metric>): Last rows of a temporal measure
as.data.frame(<dynet>): Tidy tables from a temporal network
as.data.frame(<dynet_animation>): Coerce an animation to a data frame
as.data.frame(<dynet_collapsed>): Tidy tables from a collapsed temporal network
as.data.frame(<dynet_collapsed_list>): Tidy data frame of session-specific collapsed networks
as.data.frame(<dynet_metric>): Tidy data frame of a temporal measure
as.data.frame(<dynet_path_network>): Tidy tables from a temporal path-union network
as.data.frame(<dynet_path_trajectories>): Tidy table of a temporal trajectory tree
as.data.frame(<dynet_paths>): Tidy data frame of time-respecting paths
as.data.frame(<dynet_pathways>): Tidy data frame of ranked pathways
as.data.frame(<dynet_projection>): Tidy tables from a time-projected network
as.data.frame(<dynet_pshifts>): Tidy data frame of participation shift counts
as.data.frame(<dynet_similarity>): Tidy table of time-bin similarity
as.data.frame(<dynet_snapshot>): Tidy table of snapshot edges