Edward K. Kao

Joseph Schlessinger, Richard Bennet, Jacob Coakwell et al.

This study quantifies influence between media outlets by applying a novel methodology that uses causal effect estimation on networks and transformer language models. We demonstrate the obscured influence of state-controlled outlets over other outlets, regardless of orientation, by analyzing a large dataset of quotations from over 100 thousand articles published by the most prominent European and Russian traditional media outlets, appearing between May 2018 and October 2019. The analysis maps out the network structure of influence with news wire services serving as prominent bridges that connect outlets in different geo-political spheres. Overall, this approach demonstrates capabilities to identify and quantify the channels of influence in intermedia agenda setting over specific topics.

Frank Wanye, Vitaliy Gleyzer, Edward Kao et al.

Community detection is a well-studied problem with applications in domains ranging from networking to bioinformatics. Due to the rapid growth in the volume of real-world data, there is growing interest in accelerating contemporary community detection algorithms. However, the more accurate and statistically robust methods tend to be hard to parallelize. One such method is stochastic block partitioning (SBP) - a community detection algorithm that works well on graphs with complex and heterogeneous community structure. In this paper, we present a sampling-based SBP (SamBaS) for accelerating SBP on sparse graphs. We characterize how various graph parameters affect the speedup and result quality of community detection with SamBaS and quantify the trade-offs therein. To evaluate SamBas on real-world web graphs without known ground-truth communities, we introduce partition quality score (PQS), an evaluation metric that outperforms modularity in terms of correlation with F1 score. Overall, SamBaS achieves speedups of up to 10X while maintaining result quality (and even improving result quality by over 150% on certain graphs, relative to F1 score).

Steven T. Smith, Edward K. Kao, Erika D. Mackin et al.

The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IOs). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a novel network causal inference approach to quantify the impact of individual actors in spreading IO narratives. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections, and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007 to February 2020), over 50,000 accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the-PR-curve, maps out salient network communities, and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from U.S. Congressional reports, investigative journalism, and IO datasets provided by Twitter.

Steven T. Smith, Edward K. Kao, Kenneth D. Senne et al.

A novel unified Bayesian framework for network detection is developed, under which a detection algorithm is derived based on random walks on graphs. The algorithm detects threat networks using partial observations of their activity, and is proved to be optimum in the Neyman-Pearson sense. The algorithm is defined by a graph, at least one observation, and a diffusion model for threat. A link to well-known spectral detection methods is provided, and the equivalence of the random walk and harmonic solutions to the Bayesian formulation is proven. A general diffusion model is introduced that utilizes spatio-temporal relationships between vertices, and is used for a specific space-time formulation that leads to significant performance improvements on coordinated covert networks. This performance is demonstrated using a new hybrid mixed-membership blockmodel introduced to simulate random covert networks with realistic properties.

Steven T. Smith, Kenneth D. Senne, Scott Philips et al.

Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relatively small subgraph in an enormous, potentially uninteresting background. This aspect characterizes network detection as a "big data" problem. Graph partitioning and network discovery have been major research areas over the last ten years, driven by interest in internet search, cyber security, social networks, and criminal or terrorist activities. The specific problem of network discovery is addressed as a special case of graph partitioning in which membership in a small subgraph of interest must be determined. Algebraic graph theory is used as the basis to analyze and compare different network detection methods. A new Bayesian network detection framework is introduced that partitions the graph based on prior information and direct observations. The new approach, called space-time threat propagation, is proved to maximize the probability of detection and is therefore optimum in the Neyman-Pearson sense. This optimality criterion is compared to spectral community detection approaches which divide the global graph into subsets or communities with optimal connectivity properties. We also explore a new generative stochastic model for covert networks and analyze using receiver operating characteristics the detection performance of both classes of optimal detection techniques.