Worth the Weight: Conceptualizing and Measuring Strong Versus Weak Tie Homophily.

Homophily, or the tendency for social contact to occur among those who are similar, plays a crucial role in structuring our social networks. Most previous work considers whether homophily shapes the patterns of all social ties, regardless of their frequency of interaction or level of intimacy. As complex network data become increasingly available, however, researchers need to evaluate whether homophily operates differently for ties defined by strong versus weak measures of strength. Here, I take this approach by first defining two variants of homophily: (1) strong tie homophily, or the tendency for ties with high measures of strength to cluster together similar peers, and (2) weak tie homophily, or the tendency for ties with low edge weights to connect same-attribute actors. Then, I apply valued ERGMs to demonstrate the utility of differentiating between the two variants across simulated and observed networks. In most networks, I find that there are observable differences in the magnitude of strong versus weak tie homophily. Additionally, when there are low levels of clustering on the attribute of interest, distinguishing between strong and weak tie homophily can reveal that these processes operate in opposite directions. Since strong and weak ties carry substantively different implications, I argue that differentiating between the two homophily variants has the potential to uncover novel insights on a variety of social phenomena.

Central hubs prediction for bio networks by directed hypergraph - GA with validation to COVID-19 PPI.

Network structures have attracted much interest and have been rigorously studied in the past two decades. Researchers used many mathematical tools to represent these networks, and in recent days, hypergraphs play a vital role in this analysis. This paper presents an efficient technique to find the influential nodes using centrality measure of weighted directed hypergraph. Genetic Algorithm is exploited for tuning the weights of the node in the weighted directed hypergraph through which the characterization of the strength of the nodes, such as strong and weak ties by statistical measurements (mean, standard deviation, and quartiles) is identified effectively. Also, the proposed work is applied to various biological networks for identification of influential nodes and results shows the prominence the work over the existing measures. Furthermore, the technique has been applied to COVID-19 viral protein interactions. The proposed algorithm identified some critical human proteins that belong to the enzymes TMPRSS2, ACE2, and AT-II, which have a considerable role in hosting COVID-19 viral proteins and causes for various types of diseases. Hence these proteins can be targeted in drug design for an effective therapeutic against COVID-19.

The influence of strong and weak ties in physician peer networks on new drug adoption.

Physicians interact and exchange information through various social networks. Understanding peer effects through different networks can help accelerate new medical technology and innovative treatment adoption. In this research, we measure the influence of strong-tie and weak-tie connections on new drug adoption and study the overlap between advice-discussion and patient-sharing network. We construct two physician networks with strong and weak ties from peer nomination surveys and commercial medical claims data. We design a dynamic system to define peer adoption status and build patient-level hierarchical logistic models to measure the peer influence on new product adoption for treating new-to-therapy patients. Our results show that A strong-tie early adoption peer has six times more influence on new drug adoption than a weak-tie peer. Weak tie peers collectively exert as much or higher influence than strong-tie peers because of the larger network size. In the case of inaccessibility to strong-tie data, researchers can still reliably use the influence of the weak tie data only even though they will lose the effect of the omitted strong ties.

Psychological antecedents of COVID-19 information sharing within strong-tie and weak-tie networks.

Objective: This study investigates the psychological mechanisms underlying people's sharing of COVID-19 information within their strong-tie networks and weak-tie networks. Methods: A cross-sectional online survey was conducted between March and April 2020 (N = 609 Chinese adults). Measures included emotions and behavioral beliefs about COVID-19 information sharing, risk perceptions, and COVID-19 information acquisition and sharing behaviors. Multiple linear regression was performed to examine the psychological predictors of COVID-19 information sharing. Results: People were more likely to share COVID-19 information within their strong-tie networks when they experienced more negative emotions (ß = .09, p = .01) and had stronger beliefs that information sharing would promote disease prevention (ß = .12, p = .004). By comparison, negative emotions were the only significant predictor of COVID-19 information sharing (ß = .12, p = .002) within weak-tie networks (ß = .04, p = .31 for beliefs about sharing). Conclusion: People may share COVID-19 information within weak-tie networks to cope with negative emotions regardless of whether they perceive information sharing as beneficial to disease prevention. Innovation: Health educators should raise people's awareness of the psychological motivators of COVID-19 information sharing to create a healthy information environment for disease prevention.