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Temporal Network Analysis of Email Communication Patterns in a Long Standing Hierarchy

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arxiv 2311.13442 v1 pith:KFWLC7RO submitted 2023-11-22 cs.SI

classification cs.SI
keywords communicationhierarchylevelsorganisationietfpatternshighernetwork
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An important concept in organisational behaviour is how hierarchy affects the voice of individuals, whereby members of a given organisation exhibit differing power relations based on their hierarchical position. Although there have been prior studies of the relationship between hierarchy and voice, they tend to focus on more qualitative small-scale methods and do not account for structural aspects of the organisation. This paper develops large-scale computational techniques utilising temporal network analysis to measure the effect that organisational hierarchy has on communication patterns within an organisation, focusing on the structure of pairwise interactions between individuals. We focus on one major organisation as a case study - the Internet Engineering Task Force (IETF) - a major technical standards development organisation for the Internet. A particularly useful feature of the IETF is a transparent hierarchy, where participants take on explicit roles (e.g. Area Directors, Working Group Chairs). Its processes are also open, so we have visibility into the communication of people at different hierarchy levels over a long time period. We utilise a temporal network dataset of 989,911 email interactions among 23,741 participants to study how hierarchy impacts communication patterns. We show that the middle levels of the IETF are growing in terms of their dominance in communications. Higher levels consistently experience a higher proportion of incoming communication than lower levels, with higher levels initiating more communications too. We find that communication tends to flow "up" the hierarchy more than "down". Finally, we find that communication with higher-levels is associated with future communication more than for lower-levels, which we interpret as "facilitation". We conclude by discussing the implications this has on patterns within the wider IETF and for other organisations.

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  1. Multi-View Graph Convolution Network for Internal Talent Recommendation Based on Enterprise Emails

    cs.LG 2025-08 conditional novelty 5.0 of 10

    A dual-graph GCN with gating fusion recommends internal talent from enterprise email structure and subject-line semantics, achieving 40.9% Hit@100 on one company dataset, with learned per-job-family fusion weights.

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