Systems for Near Real-Time Analysis of Large-Scale Dynamic Graphs

Graphs are widespread data structures used to model a wide variety of problems. The sheer amount of data to be processed has prompted the creation of a myriad of systems that help us cope with massive scale graphs. The pressure to deliver fast responses to queries on the graph is higher than ever before, as it is demanded by many applications (e.g. online recommendations, auctions, terrorism protection, etc.). In addition, graphs change continuously (so do the real world entities that typically represent). Systems must be ready for both: near real-time and dynamic massive graphs. We survey systems taking their scalability, real-time potential and capability to support dynamic changes to the graph as driving guidelines. The main techniques and limitations are distilled and categorised. The algorithms run on top of graph systems are not ready for prime time dynamism either. Therefore,a short overview on dynamic graph algorithms has also been included.

The Weakness of Weak Ties in the Classroom

Granovetters strength of weak ties hypothesizes that isolated social ties offer limited access to external prospects, while heterogeneous social ties diversify ones opportunities. We analyze the most complete record of college student interactions to date (approximately 80,000 interactions by 290 students -- 16 times more interactions with almost 3 times more students than previous studies on educational networks) and compare the social interaction data with the academic scores of the students. Our first finding is that social diversity is negatively correlated with performance. This is explained by our second finding: highly performing students interact in groups of similarly performing peers. This effect is stronger the higher the student performance is. Indeed, low performance students tend to initiate many transient interactions independently of the performance of their target. In other words, low performing students act disassortatively with respect to their social network, whereas high scoring students act assortatively. Our data also reveals that highly performing students establish persistent interactions before mid and low performing ones and that they use more structured and longer cascades of information from which low performing students are excluded.