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A closed triad is a group of three people who are connected with each other. It is the most basic unit for studying group phenomena in social networks. In this paper, we study how closed triads are formed in dynamic networks. More specifically, given three persons, what are the fundamental factors that trigger the formation of triadic closure? There are various factors that may influence the formation of a relationship between persons. Can we design a unified model to predict the formation of triadic closure? Employing a large microblogging network as the source in our study, we formally define the problem and conduct a systematic investigation. The study uncovers how user demographics and network topology influence the process of triadic closure. We also present a probabilistic graphical model to predict whether three persons will form a closed triad in dynamic networks. The experimental results on the microblogging data demonstrate the efficiency of our proposed model for the prediction of triadic closure formation.

The social triad—a group of three people—is one of the simplest and most fundamental social groups. Extensive network and social theories have been developed to understand its structure, such as triadic closure and social balance. Over the course of a triadic closure—the transition from two ties to three among three users, the strength dynamics of its social ties, however, are much less well understood. Using two dynamic networks from social media and mobile communication, we examine how the formation of the third tie in a triad affects the strength of the existing two ties. Surprisingly, we find that in about 80% social triads, the strength of the first two ties is weakened although averagely the tie strength in the two networks maintains an increasing or stable trend. We discover that (1) the decrease in tie strength among three males is more sharply than that among females, and (2) the tie strength between celebrities is more likely to be weakened as the closure of a triad than those between ordinary people. Furthermore, we formalize a triadic tie strength dynamics prediction problem to infer whether social ties of a triad will become weakened after its closure. We propose a TRIST method—a kernel density estimation (KDE)-based graphical model—to solve the problem by incorporating user demographics, temporal effects, and structural information. Extensive experiments demonstrate that TRIST offers a greater than 82% potential predictability for inferring triadic tie strength dynamics in both networks. The leveraging of the KDE and structural correlations enables TRIST to outperform baselines by up to 30% in terms of F1-score.

Abstract-We study the problem of group formation in online social networks. In particular, we focus on one of the most important human groups-the triad-and try to understand how closed triads are formed in dynamic networks, by employing data from a large microblogging network as the basis of our study. We formally define the problem of triadic closure prediction and conduct a systematic investigation. The study reveals how user demographics, network characteristics, and social properties influence the formation of triadic closure. We also present a probabilistic graphical model to predict whether three persons will form a closed triad in a dynamic network. Different kernel functions are incorporated into the proposed graphical model to quantify the similarity between triads. Our experimental results with the large microblogging dataset demonstrate the effectiveness (+10% over alternative methods in terms of F1-Score) of the proposed model for the prediction of triadic closure formation.

Short-time heavy rainfall is a kind of sudden strong and heavy precipitation weather, which seriously threatens people’s life and property safety. Accurate precipitation nowcasting is of great significance for the government to make disaster prevention and mitigation decisions in time. In order to make high-resolution forecasts of regional rainfall, this paper proposes a convolutional 3D GRU (Conv3D-GRU) model to predict the future rainfall intensity over a relatively short period of time from the machine learning perspective. Firstly, the spatial features of radar echo maps with different heights are extracted by 3D convolution, and then, the radar echo maps on time series are coded and decoded by using GRU. Finally, the trained model is used to predict the radar echo maps in the next 1-2 hours. The experimental results show that the algorithm can effectively extract the temporal and spatial features of radar echo maps, reduce the error between the predicted value and the real value of rainfall, and improve the accuracy of short-term rainfall prediction.