Millions of us post on Facebook, send a tweet, or upload photos on online social networks. But how safe is the information we share? For that matter, how much information do we disclose that we don't even realize? The amount of personal information unwittingly exposed by users on online social networks is staggering. Recent reports indicate that networks like Facebook and Twitter are infested with tens of millions of fake user profiles. Fake users on online social networks can jeopardize your safety both in the online world and in the real world. Our research team has created a set of machine-learning based solutions to identify fake users.

Genealogical research is a popular hobby. Many people pour over sources such as birth and death certificates, wills, letters, and land deeds to assemble their family trees. A lot of these get uploaded and shared in online projects such as WikiTree, which creates vast datasets of interconnected family trees. Machine learning techniques can analyze such data, and the results show patterns in population gender ratios, marriage trends, fertility, lifespans, and the frequency of twins and triplets.

Trends change rapidly in today's world and are readily observed in lists of most important people, rankings of global companies, infectious disease patterns, political opinions, and popularities of online social networks. A key question arises: What is the mechanism behind the emergence of new trends? To answer this question, we employed state-of-the art data science tools and extensive cloud computing resources to create this massive corpora that contains 38,000 real-world networks and 2.5 million graphs.