In this paper, we provide the first practical algorithms with provable guarantees for the problem of inferring the topics assigned to each document in an LDA topic model. This is the primary inference problem for many applications of topic models in social science, data exploration, and causal inference settings. We obtain this result by showing a novel non-gradient-based, combinatorial approach to estimating topic models. This yields algorithms that converge to near-optimal posterior probability in logarithmic parallel computation time (adaptivity)—exponentially faster than any known LDA algorithm. We also show that our approach can provide interpretability guarantees such that each learned topic is formally associated with a known keyword. Finally, we show that unlike alternatives, our approach can maintain the independence assumptions necessary to use the learned topic model for downstream causal inference methods that allow researchers to study topics as treatments. In terms of practical performance, our approach consistently returns solutions of higher semantic quality than solutions from state-of-the-art LDA algorithms, neural topic models, and LLM-based topic models across a diverse range of text datasets and evaluation parameters.

In this paper, we describe a new algorithm called Preferential Attac hment k-class Classifier (PreAttacK) for detecting fake accounts in a social network. Recently, several algorithms have obtained high accuracy on this problem. However, they have done so by relying on information about fake accounts' friendships or the content they share with others-the very things we seek to prevent.

In this paper we describe a new parallel algorithm called Fast Adaptive Sequencing Technique (FAST) for maximizing a monotone submodular function under a cardinality constraint k. This algorithm achieves the optimal 1-1/e approximation guarantee and is orders of magnitude faster than the state-of-the-art on a variety of experiments over real-world data sets. Following recent work by Balkanski and Singer (2018), there has been a great deal of research on algorithms whose theoretical parallel runtime is exponentially faster than algorithms used for submodular maximization over the past 40 years. However, while these new algorithms are fast in terms of asymptotic worst-case guarantees, it is computationally infeasible to use them in practice even on small data sets because the number of rounds and queries they require depend on large constants and high-degree polynomials in terms of precision and confidence. The design principles behind the FAST algorithm we present here are a significant departure from those of recent theoretically fast algorithms. Rather than optimize for asymptotic theoretical guarantees, the design of FAST introduces several new techniques that achieve remarkable practical and theoretical parallel runtimes. The approximation guarantee obtained by FAST is arbitrarily close to 1 - 1/e, and its asymptotic parallel runtime (adaptivity) is O(log(n) log^2(log k)) using O(n log log(k)) total queries. We show that FAST is orders of magnitude faster than any algorithm for submodular maximization we are aware of, including hyper-optimized parallel versions of state-of-the-art serial algorithms, by running experiments on large data sets.

In this paper, we study the problem of early detection of fake user accounts on social networks based solely on their network connectivity with other users. Removing such accounts is a core task for maintaining the integrity of social networks, and early detection helps to reduce the harm that such accounts inflict. However, new fake accounts are notoriously difficult to detect via graph-based algorithms, as their small number of connections are unlikely to reflect a significant structural difference from those of new real accounts. We present the SybilEdge algorithm, which determines whether a new user is a fake account (‘sybil’) by aggregating over (I) her choices of friend request targets and (II) these targets’ respective responses. SybilEdge performs this aggregation giving more weight to a user’s choices of targets to the extent that these targets are preferred by other fakes versus real users, and also to the extent that these targets respond differently to fakes versus real users. We show that SybilEdge rapidly detects new fake users at scale on the Facebook network and outperforms state-of-the-art algorithms. We also show that SybilEdge is robust to label noise in the training data, to different prevalences of fake accounts in the network, and to several different ways fakes can select targets for their friend requests. To our knowledge, this is the first time a graph-based algorithm has been shown to achieve high performance (AUC > 0.9) on new users who have only sent a small number of friend requests.

In this paper we consider parallelization for applications whose objective can be expressed as maximizing a non-monotone submodular function under a cardinality constraint. Our main result is an algorithm whose approximation is arbitrarily close to 1/2e in O(log^2 n) adaptive rounds, where n is the size of the ground set. This is an exponential speedup in parallel running time over any previously studied algorithm for constrained non-monotone submodular maximization. Beyond its provable guarantees, the algorithm performs well in practice. Specifically, experiments on traffic monitoring and personalized data summarization applications show that the algorithm finds solutions whose values are competitive with state-of-the-art algorithms while running in exponentially fewer parallel iterations.