Weighted network graph for interpersonal communication with temporal regularity

Abstract

Over the last decade, interpersonal communication has attracted more attention from researchers than before. Although the volume of data generated through various communication devices and tools could be enormous, the recent decrease in storage cost enables us to record and store it. The analysis of interpersonal communication is useful to estimate influence in social relationships among people, to detect communities, and to recommend potential friends for users on social networking services. A network graph, which is a mathematical model that represents people as nodes and past opportunities of interpersonal communication as edges, works in such analysis. However, when the capacity of the number of edges recordable in a graph database is limited, or when only a limited number of edges is used for high-speed analysis, it is still unclear which edges should be prioritized and utilized in the analysis. Previous studies suggested that edges in network graphs can be weighted on the basis of the aggregated duration of connections, the number of connections, or the connection time. However, temporal regularity in interpersonal communication has not been well considered in the previous studies. Therefore, in this paper, we propose an edge weighting method for network graphs from interpersonal communication that determines edge weighs on the basis of the scores obtained from the spectral analysis technique. The spectral analysis technique is utilized to numerically deal with temporal regularity and frequency of interpersonal communication. An examination using real records verifies that by using our edge weighting method, link prediction works better under a condition of the limited number of edges usable for the analysis. We also deeply analyze and present the distributions of the frequencies that characterize interpersonal communication.

Current Trends and Challenges in Link Prediction Methods in Dynamic Social Networks: A Literature Review

“…In [17], it is introduced a method for link prediction based on graph theory. The method used the spectral analysis technique to calculate the score for each edge in the network graph.…”

“…As such, [64] employed a measure that computes the sum of the absolute difference to distinguish between various types of models. The methods discussed in [4,55,15,17] employed ROC and AUC for evaluation.…”

“…An interesting state-of-the-art method for facilitating link prediction is the use of the temporal regularity in interpersonal communication as a means of prioritizing weighted edges in network graphs [17]. Compared with other methods, this method predicts links even if there is a scarcity in the number of edges needed for analysis.…”

“…In doing so, they have improved the prediction of links compared with other methods that use structural SNs. An interesting method that determines the edge weight based on a calculation of the scores using spectral analysis is given in [17]. The method achieved good prediction results.…”

Intensity of Bilateral Contacts in Social Network Analysis

“…While the reaction time between emails does not necessarily correspond to the time that has passed for a specific email to be responded, it still provides a quantifiable indicator of the temporal dimension of the email interaction between two members of a social network. Shinkuma et al [34] suggest that the frequency of interaction can be used as an indicator to characterize interpersonal communication in the network graphs.…”