refactor: move optional arguments of similarity-efficiency functions behind the ellipsis

[krlmlr]

Part of the repo-wide ellipsis migration coordinated in #2757 —
see that PR for the full rationale (CONTRIBUTING.md, Argument Order and the Ellipsis).
Based directly on main (the registry split landed via #2779); a single topic commit.

What this does

Inserts ... between the defining arguments (head) and the optional
modifiers (tail) of 6 exported similarity-efficiency functions.
Arguments after ... become keyword-only.

• Legacy positional or abbreviated calls are recovered by the generated
  ARG_HANDLE block and emit a single lifecycle::deprecate_soft("3.0.0", …) —
  behavior is unchanged.
• No defaults change, no arguments are renamed, deprecated functions untouched.
• Registry: tools/migrations/similarity-efficiency.R; blocks regenerated via
  Rscript tools/generate-migrations.R (idempotent, CI-checked).
• Rd usage/arguments updated mechanically (see note below).

New signatures

function	head (positional)	keyword-only tail
assortativity_degree()	graph	directed
assortativity_nominal()	graph, types	directed, normalized
average_local_efficiency()	graph	weights, directed, mode
global_efficiency()	graph	weights, directed
local_efficiency()	graph, vids	weights, directed, mode
similarity()	graph, vids	mode, loops, method

Notes for review

• The environment used to prepare this PR cannot install igraph.r2cdocs
  (GitHub API unreachable), so man/*.Rd files were updated mechanically
  (usage + ... argument item) instead of via devtools::document().
  R CMD check's usage↔formals validation passes; a follow-up
  devtools::document() run may reflow whitespace but should produce no
  semantic diff.
• Package tests that called these functions positionally were updated to
  named arguments (they are the same soft-deprecation user code will see).

[github-actions]

This is how benchmark results would change (along with a 95% confidence interval in relative change) if 752e97a is merged into main:

• ✔️as_adjacency_matrix: 778ms -> 784ms [-0.97%, +2.46%]
• ✔️as_biadjacency_matrix: 743ms -> 745ms [-0.45%, +1.04%]
• ✔️as_data_frame_both: 1.54ms -> 1.56ms [-2.01%, +4.65%]
• ✔️as_long_data_frame: 4.19ms -> 4.24ms [-1.9%, +4.04%]
• ✔️es_attr_filter: 2.66ms -> 2.68ms [-0.17%, +1.57%]
• ✔️graph_from_adjacency_matrix: 120ms -> 119ms [-1.58%, +0.92%]
• ✔️graph_from_data_frame: 3.45ms -> 3.42ms [-2.65%, +1.16%]
• ✔️vs_attr_filter: 1.52ms -> 1.52ms [-1.21%, +1.44%]
• ✔️vs_by_name: 975µs -> 963µs [-2.39%, 0%]
  Further explanation regarding interpretation and methodology can be found in the documentation.