Temporal stability in human interaction networks

Abstract

This paper reports on stable (or invariant) properties of human interaction networks, with benchmarks derived from public email lists. Activity, recognized through messages sent, along time and topology were observed in snapshots in a timeline, and at different scales. Our analysis shows that activity is practically the same for all networks across timescales ranging from seconds to months. The principal components of the participants in the topological metrics space remain practically unchanged as different sets of messages are considered. The activity of participants follows the expected scale-free trace, thus yielding the hub, intermediary and peripheral classes of vertices by comparison against the Erd\"os-R\'enyi model. The relative sizes of these three sectors are essentially the same for all email lists and the same along time. Typically, $<15\%$ of the vertices are hubs, 15-45\% are intermediary and $>45\%$ are peripheral vertices. Similar results for the distribution of participants in the three sectors and for the relative importance of the topological metrics were obtained for 12 additional networks from Facebook, Twitter and ParticipaBR. These properties are consistent with the literature and may be general for human interaction networks, which has important implications for establishing a typology of participants based on quantitative criteria.Comment: See ancillary Supporting Information PDF file for further tables and figures. More information on code and further files can be found at https://github.com/ttm/articleStabilityInteractionNetwork

Information processing by networks of quantum decision makers

“…Modeling the behavior of multi-agent social systems and social networks has recently attracted a substantial amount of activities of researchers, as can be inferred from the review articles [1][2][3] and numerous original papers, of which we can cite just a few recent [4][5][6][7][8]. High interest to such a modeling is due to two reasons.…”

Topological stability and textual differentiation in human interaction networks: statistical analysis, visualization and linked data

“…Nearly identical distributions are found in all systems as indicated in the Supporting Information of. 13 Although slightly higher activity rates are found in the beginning of the month, the most important feature seems to be the homogeneity made explicit by the high circular dispersion in Table 2. This specific example and empirical table correspond to the activity of the MET email list.…”

“…This example table was constructed using all LAD messages, and the results are the same for other lists, as shown in the Supporting Information document of. 13 The most uniform distribution of activity was found in seconds and minutes. Hours of the day exhibited the most concentrated activity (lowest δ(z)), with mean between 2 p.m. and 3 p.m. (θ = −9.61).…”

“…Nearly identical distributions were observed on other social systems as shown in the Supporting Information document of. 13 Highest activity was observed between noon and 6pm (with 1/3 of total day activity), followed by the time period between 6pm and midnight. Around 2/3 of the activity takes place from noon to midnight but the activity peak occurs between 11 a.m. and 12 p.m.…”

“…Four email lists and additional networks from Twitter, Facebook and ParticipaBR were selected for a thorough investigation of the topological stability in. 13 For the careful consideration of the textual features, 18 email lists were selected at random. The fact that unreciprocated edges often exceed 50% in human interaction networks 8 motivated the inclusion of symmetry metrics in our analysis.…”