Measurement studies of online social networks (OSNs)show that all social links are not equal, and the strength of each link is best characterized by the frequency of interactions between the linked users. To date, few studieshave been able to examine detailed interactiondata over time. In this paper, we first analyze the interaction dynamics in a large online social network. We find that users invite new friends to interact at a nearly constant rate, prefer to continue interacting with friends with whom they have a larger number of historical interactions,and most social links drop in interaction frequency over time. Then, we use our insights from the analysis to derive a generative model of social interactionsthat can capture fundamental processes underlinguser interactions.
To survey trigenic interactions, we designed query strains that sampled key features of the global digenic interaction network: (i) digenic interaction strength, (ii) average number of digenic interactions, and (iii) digenic interaction profile similarity. In total, we tested 400,000 double and 200,000 triple mutants for fitness defects and identified 9500 digenic and 3200 trigenic negative interactions. Although trigenic interactions tend to be weaker than digenic interactions, they were both enriched for functional relationships. About one-third of trigenic interactions identified "novel" connections that were not observed in our digenic control network, whereas the remaining approximately two-thirds of trigenic interactions "modified" a digenic interaction, suggesting that the global digenic interaction network is important for understanding the trigenic interaction network. We estimate that the global trigenic interaction network is 100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance.
We tested most of the 6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including 550,000 negative and 350,000 positive interactions, spanning 90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell.
The Yahoo News Feed dataset is a collection based on a sample of anonymized user interactions on the news feeds of several Yahoo properties, including the Yahoo homepage, Yahoo News, Yahoo Sports, Yahoo Finance, Yahoo Movies, and Yahoo Real Estate. The dataset stands at a massive 110B lines (1.5TB bzipped) of user-news item interaction data, collected by recording the user- news item interaction of about 20M users from February 2015 to May 2015. In addition to the interaction data, we are providing the demographic information (age segment and gender) and the city in which the user is based for a subset of the anonymized users. On the item side, we are releasing the title, summary, and key-phrases of the pertinent news article. The interaction data is timestamped with the user's local time and also contains partial information of the device on which the user accessed the news feeds, which allows for interesting work in contextual recommendation and temporal data mining.
The first one is called an engagement interaction. Those are the interactions within circles of trust, family, friends, close co-workers, etc. Higher frequency of engagement interactions are a predictor of productivity, as it helps coordinating the behavior of a group. The second type of interaction is called an exploration interaction, referring to times when we expose ourselves to people outside of our regular circles. This is generally how we learn new ideas. Having more exploration interactions can be a predictor of the level of innovation of a company or team.