tl;dr: Longform articles are extended, in-depth pieces that often serve as feature stories in newspapers and magazines. In this work, we develop a system to automatically identify longform content across the web. Our novel classifier is highly accurate despite huge variation within longform in terms of topic, voice, and editorial taste. It is also scalable and interpretable, requiring a surprisingly small set of features based only on language and parse structures, length, and document interest. We implement our system at scale and use it to identify a corpus of several million longform documents. Using this corpus, we provide the first web-scale study with quantifiable and measurable information on longform, giving new insight into questions posed by the media on the past and current state of this famed literary medium.

Circumlocution is when many words are used to describe what could be said with fewer, e.g., "a machine that takes moisture out of the air" instead of "dehumidifier." Web search is a perfect backdrop for circumlocution where people struggle to name what they seek. In some domains, not knowing the correct term can have a significant impact on the search results that are retrieved. We study the medical domain, where professional medical terms are not commonly known and where the consequence of not knowing the correct term can impact the accuracy of surfaced information, as well as escalation of anxiety, and ultimately the medical care sought. Given a free-form colloquial health search query, our objective is to find the underlying professional medical term. The problem is complicated by the fact that people issue quite varied queries to describe what they have. Machine-learning algorithms can be brought to bear on the problem, but there are two key complexities: creating high-quality training data and identifying predictive features. To our knowledge, no prior work has been able to crack this important problem due to the lack of training data. We give novel solutions and demonstrate their efficacy via extensive experiments, greatly improving over the prior art.

With recent advances in storage technology, it is now possible to store the vast amounts of data generated by cloud computing applications. The sheer size of ‘big data’ motivates the need for streaming algorithms that can compute approximate solutions without full random access to all of the data.

Extracting knowledge by performing computations on graphs is becoming increasingly challenging as graphs grow in size. A standard approach distributes the graph over a cluster of nodes, but performing computations on a distributed graph is expensive if large amount of data have to be moved. Without partitioning the graph, communication quickly becomes a limiting factor in scaling the system up. Existing graph partitioning heuristics incur high computation and communication cost on large graphs, sometimes as high as the future computation itself. Observing that the graph has to be loaded into the cluster, we ask if the partitioning can be done at the same time with a lightweight streaming algorithm.

One of the most influential results in network analysis is that many natural networks exhibit a power-law or log-normal degree distribution. This has inspired numerous generative models that match this property. However, more recent work has shown that while these generative models do have the right degree distribution, they are not good models for real life networks due to their differences on other important metrics like conductance. We believe this is, in part, because many of these real-world networks have very different joint degree distributions, i.e. the probability that a randomly selected edge will be between nodes of degree k and l. Assortativity is a sufficient statistic of the joint degree distribution, and it has been previously noted that social networks tend to be assortative, while biological and technological networks tend to be disassortative. We suggest that the joint degree distribution of graphs is an interesting avenue of study for further research into network structure. We provide a simple greedy algorithm for constructing simple graphs from a given joint degree distribution, and a Monte Carlo Markov Chain method for sampling them. We also show that the state space of simple graphs with a fixed degree distribution is connected via endpoint switches. We empirically evaluate the mixing time of this Markov Chain by using experiments based on the autocorrelation of each edge.