Predictability of Conversation Partners

Abstract

Recent developments in sensing technologies have enabled us to examine the nature of human social behavior in greater detail. By applying an information-theoretic method to the spatiotemporal data of cellphone locations, [C. Song et al., Science 327, 1018(2010] found that human mobility patterns are remarkably predictable. Inspired by their work, we address a similar predictability question in a different kind of human social activity: conversation events. The predictability in the sequence of one's conversation partners is defined as the degree to which one's next conversation partner can be predicted given the current partner. We quantify this predictability by using the mutual information. We examine the predictability of conversation events for each individual using the longitudinal data of face-to-face interactions collected from two company offices in Japan. Each subject wears a name tag equipped with an infrared sensor node, and conversation events are marked when signals are exchanged between sensor nodes in close proximity. We find that the conversation events are predictable to a certain extent; knowing the current partner decreases the uncertainty about the next partner by 28.4% on average. Much of the predictability is explained by longtailed distributions of interevent intervals. However, a predictability also exists in the data, apart from the contribution of their long-tailed nature. In addition, an individual's predictability is correlated with the position of the individual in the static social network derived from the data. Individuals confined in a community-in the sense of an abundance of surrounding triangles-tend to have low predictability, and those bridging different communities tend to have high predictability.

Memory in network flows and its effects on spreading dynamics and community detection

“…Recent work has indicated that a first-order Markov model may fail to adequately predict real dynamics 15,20,23,26 . That is, real dynamics often have at least one-step memory, which conventional network analysis cannot capture.…”

“…Shannon 10 introduced higher-order memory models in 1948, and there is a substantial body of work on analysing memory effects in, for example, time-series analysis for forecasting financial markets 11 , correlated random walks for predicting animal movements 12 and exponential random graph models for capturing social networks 13 . Moreover, there is recent evidence that memory is necessary for accurately predicting web traffic 14,15 , for improving search and navigation in information networks [16][17][18] and for capturing important phenomena in the spread of information [19][20][21][22][23] and epidemics [24][25][26][27][28][29] . Nevertheless, little is known about memory effects on community detection, ranking and spreading analysis, three principal methods in network science.…”

Temporal networks

“…Because the human body acts as a shield for the proximity-sensing RF signals, such sensors only record contacts when the individuals are facing each other, and thus a contact can also be considered as indicative of communication between the individuals [24,64,121,141]. Recording of face-to-face communication events has also been realized with infrared sensor devices [145]. Another type of largescale proximity data comes from hospitals where contacts between two patients that have been admitted to the same ward at the same time are recorded, sometimes including the medical staff [99,154].…”

Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap

“…The standard approach of modeling dynamical processes on networks with first-order flows oversimplifies the real dynamics and sets a limit of what can actually be detected in the system (Figure 1). Capturing critical phenomena in the dynamics and function of complex systems therefore often requires models of higher-order network flows [5][6][7][23][24][25][26][27][28].…”