Standard co-occurrence treats every pair in a list as equally related. In a keyword list, the first and second terms receive the same link weight as the first and tenth terms; in a collaboration list, adjacent collaborators receive the same link weight as distant collaborators. This can make long lists produce dense networks in which close and distant relations are not distinguished.
Attention weighting addresses this by borrowing the principle behind
attention mechanisms in large language models and graph attention
networks: a node attends most strongly to nearby neighbours, and
influence decays with distance (López-Pernas et al., 2025). In
bibnets, this is most natural for keyword co-occurrence and
collaboration networks where list order carries information about how
strongly two items are related. Instead of giving every co-occurring
pair the same weight, attention weighting makes nearby items influence
each other more strongly than distant items.
Each paper’s total contribution is normalised to sum to one, so a long list contributes the same total as a short list and no single paper can dominate the network. The weights are a fixed positional rule, not learned content-based attention, and the profile you choose states the ordering assumption explicitly.
attention takes one of four profiles. Each shapes how a
paper’s contribution is distributed over ordered items, then normalises
the result to sum to one.
| Profile | Attention concentrates on | Reasoning |
|---|---|---|
"lead" |
early positions | weight is highest at the first position and falls quadratically down the list |
"last" |
late positions | weight rises quadratically toward the final position |
"proximity" |
nearby item pairs | each co-occurrence link is weighted by how close the two items are to each other in the list; influence decays with pairwise distance, suiting keywords and collaborations where nearness encodes relatedness |
"circular" |
both ends of the list | weight is highest at the first and last positions and lower toward the middle |
Three papers, with author order preserved:
papers <- data.frame(
id = c("P1", "P2", "P3"),
authors = c("Alice; Bob; Carol", "Alice; Dave", "Carol; Alice; Eve"),
stringsAsFactors = FALSE
)The canonical use case for attention weighting is ordered keyword and collaboration data, where neighbouring items are expected to be more closely related than distant items. Byline order is a special case: some fields use it to encode lead contribution, senior contribution, or both.
With plain full counting every co-authorship link weighs the same per paper, so the strongest tie is simply the pair that appears together most often:
author_network(papers, counting = "full")
#> # bibnets network: author_collaboration | 5 nodes · 6 edges | counting: full
#> from to weight count
#> 1 ALICE CAROL 2 2
#> 2 ALICE BOB 1 1
#> 3 BOB CAROL 1 1
#> 4 ALICE DAVE 1 1
#> 5 ALICE EVE 1 1
#> 6 CAROL EVE 1 1Switch to attention = "lead" and the weight concentrates
on early byline positions. Alice, who leads two of the three papers, now
anchors the strongest ties:
author_network(papers, attention = "lead")
#> # bibnets network: author_attention_lead | 5 nodes · 6 edges | counting: lead
#> from to weight count
#> 1 ALICE CAROL 0.2296 2
#> 2 ALICE BOB 0.1837 1
#> 3 ALICE DAVE 0.16 1
#> 4 CAROL EVE 0.04592 1
#> 5 BOB CAROL 0.02041 1
#> 6 ALICE EVE 0.02041 1Switch to attention = "last" and the emphasis moves to
the final position, so links involving the last-listed authors rise to
the top instead:
author_network(papers, attention = "last")
#> # bibnets network: author_attention_last | 5 nodes · 6 edges | counting: last
#> from to weight count
#> 1 BOB CAROL 0.1837 1
#> 2 ALICE EVE 0.1837 1
#> 3 ALICE DAVE 0.16 1
#> 4 ALICE CAROL 0.06633 2
#> 5 CAROL EVE 0.04592 1
#> 6 ALICE BOB 0.02041 1The count column is identical across all three — it is
the raw number of shared papers. Only weight changes,
because attention re-distributes each paper’s credit by the selected
ordering rule. The choice of profile can reorder which collaborations
look strongest, so it should match the ordering convention of the
corpus.
attention and counting are mutually
exclusive. When attention is set, the network is built
directly from positional weights and the type and
counting arguments are ignored; the result is labelled with
the profile so a saved edge list records the assumption that produced
it:
Leave attention unset (the default) to use ordinary
counting, where you can choose "full",
"fractional", or one of the position-aware counting methods
instead.
Attention is available wherever list order is meaningful: author, keyword, country, and institution networks.
author_network(data, attention = "lead")
keyword_network(data, attention = "proximity")
country_network(data, attention = "circular")
institution_network(data, attention = "last")For keyword, country, and institution networks the “position” is the order in which the field is listed for each paper. Proximity is the most direct profile when near neighbours in the list are expected to be more related than distant ones.
For each paper, attention applies the selected ordering rule and normalises the paper’s contribution to sum to one, then uses those weights when the network is projected. Because every paper’s contribution sums to one, no single paper — however many items it lists — can dominate the result.
López-Pernas, S., Tikka, S., Misiejuk, K., Oliveira, E., & Saqr, M. (2025). Modeling the Dynamics and Impact of Human-AI Interactions with Attention Transition Network Analysis. SSRN working paper 6187958.
López-Pernas, S., Saqr, M., & Apiola, M. (2023). Scientometrics: A Concise Introduction and a Detailed Methodology for Mapping the Scientific Field of Computing Education Research. In M. Apiola, S. López-Pernas, & M. Saqr (Eds.), Past, Present and Future of Computing Education Research: A Global Perspective (pp. 79–99). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-25336-2_5
Saqr, M., López-Pernas, S., Conde, M. Á., & Hernández-García, Á. (2024). Social Network Analysis: A primer, a guide and a tutorial in R. In M. Saqr & S. López-Pernas (Eds.), Learning Analytics Methods and Tutorials: A Practical Guide Using R (pp. 491–518). Springer, Cham. https://doi.org/10.1007/978-3-031-54464-4_15