English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we propose the novel problem of Subteam Replacement: given ateam of people embedded in a social network to complete a certain task, and asubset of members - subteam - in this team which have become unavailable, findanother set of people who can perform the subteam's role in the larger team.The ability to simultaneously replace multiple team members is highlyappreciated in settings such as corporate management where team structure ishighly volatile and large-scale changes are commonplace. We conjecture that agood candidate subteam should have high skill and structural similarity withthe replaced subteam while sharing a similar connection with the larger team asa whole. Based on this conjecture, we propose a novel graph kernel whichevaluates the goodness of candidate subteams in this holistic way freelyadjustable to the need of the situation. To tackle the significantcomputational difficulties, we combine our kernel with a fast approximatealgorithm which (a) employs effective pruning strategies, (b) exploits thesimilarity between candidate team structures to reduce kernel computations, and(c) features a solid theoretical bound obtained from mathematical properties ofthe problem. We extensively test our solution on both synthetic and realdatasets to demonstrate its consistency and efficiency. Our proposed graphkernel results in more suitable replacements being proposed compared to graphkernels used in previous work, and our algorithm consistently outperformsalternative choices by finding near-optimal solutions while scaling linearlywith the size of the replaced subteam.

REFORM: Fast and Adaptive Solution for Subteam Replacement