Finding remarkably dense sequences of contacts in link streams

Abstract

A link stream is a set of quadruplets (b, e, u, v) meaning that a link exists between u and v from time b to time e. Link streams model many real-world situations like contacts between individuals, connections between devices, and others. Much work is currently devoted to the generalization of classical graph and network concepts to link streams. We argue that the density is a valuable notion for understanding and characterizing links streams. We propose a method to capture specific groups of links that are structurally and temporally densely connected and show that they are meaningful for the description of link streams. To find such groups, we use classical graph community detection algorithms, and we assess obtained groups. We apply our method to several real-world contact traces (captured by sensors) and demonstrate the relevance of the obtained structures.

Stream graphs and link streams for the modeling of interactions over time

“…A notion close to average degree is introduced in [60] for dense dynamic sub-graphs searching. We also studied preliminary notions of density, cliques, quotient streams, and dense substreams in our own previous work [84,22,23,83,82].…”

Profiling giants: the networks and influence of Buchanan and Tullock

“…Unfortunately, there is yet no established way of uncovering the time dimensions of established networks, although computer scientists are developing tools that should soon facilitate detecting both the temporal and structural nature of scholarly interactions (see Gaumont et al 2016;Latapy et al 2017).…”

Larc

“…In addition, we compare to static methods by aggregating the temporal interactions and demonstrate that utilizing the temporal information is advantageous for recovering ground truth communities. Temporal communities: Various temporal subgraph detection methods have also been considered in the literature: communities [17,25], dense subgraphs [15,24,42,43], heavy-weight subgraphs [8,37] and persistent subgraphs [3,36]. Many of these methods detect one subgraph over one łactive intervalž at a time, as opposed to recurring activation of multiple subgraphs [3,8,17,36,37].…”

“…Other methods enforce user-deined consistency by introducing parameters such as number of occurrences and time span [43] or some notion of persistence (e.g. time-to-live interval) for interaction edges [3,15,24,42]. Diferent from all the above, we detect multiple overlapping communities over time and let the data deine the natural periods of activity which may vary with communities, application and across time.…”