Gaining timely insights on real-world emergency events, such as infectious disease outbreaks, is critical for developing appropriate response strategies. In this work, we propose a data-driven approach to study the spreading dynamics of the global Covid-19 pandemic. Specifically, we aim to identify a set of most “similar” geographic regions as proxies for making predictions on a targeted location. Example predictions include the number of new cases, number of hospitalizations, and number of deaths. Such predictions can be made at different levels of regional granularities, including city, county, and state levels. Our approach starts by transforming regional time series into graph representations using the natural visibility graph (NVG) model in order to capture their intrinsic trends and properties. These graphs are then projected onto a common embedding space using graph-level network embedding techniques. Essentially, each time series is converted as a data point in a feature embedding space, where spatial proximity indicates similarity among time series. Given a targeted region, our approach can identify the most “relevant” geographic regions by finding its k-nearest neighbors in the embedding space. Subsequently, appropriate response strategies and policies (e.g., school shutdown, indoor dining restriction) can be adapted based on the success or failure experiences from relevant regions. Our approach will potentially provide valuable insights in mitigating the spreading of infectious disease.

Subgraph Matching is one of the fundamental problems in network analysis, with a wide range of applications ranging from drug repurposing and discovery to programming language analysis. Due to the increasing prevalence of knowledge graphs (KGs), there has been growing interests in extending existing subgraph matching algorithms to the KG domain. One of the main challenges here lies on the structural gap, which refers to the difference between the query pattern and the corresponding subgraph instance due to variations in semantic expression. To address this challenge, we propose a semantic guided subgraph matching method for knowledge graphs, which extends our prior filtering-based method. Specifically, our approach leverages an external semantic ontology to estimate the overall fitness/quality of subgraph candidates with respect to the query template. In addition, our approach incorporates effective query decomposition strategies to reduce the overall query cost. Furthermore, we develop a distributed implementation of the algorithm such that it can be scaled up to handle knowledge graphs with a large number of entities and relations. We demonstrate the effectiveness of the proposed approach on a variety of semantic networks provided in the DARPA Modeling Adversarial Activity (MAA) program.

Networks are natural representations in modeling adversarial activities, such as smuggling, human trafficking, and illegal arms dealing. However, such activities are often covert and embedded across multiple domains and sources. They are generally not detectable and recognizable from the perspective of an isolated network, and only become apparent when multiple networks are analyzed in a unified m anner. T o t his e nd, we propose Complex Analytics of Network of Networks (CANON), a mathematical and computational framework for modeling adversarial activities from large-scale, multi-sourced data inputs. Central to our framework is a network-of-networks model, where nodes and edges can be defined across different domains and at multiple resolutions. Based on this model, we address the key challenges in modeling adversarial activities via four technical components, including optimization-based network alignment, network embedding and conditioning, approximate subgraph matching, and investigative subgraph discovery.In this paper, we describe the design and implementation of the individual components as well as integrating these components into a unified system using a modular microservice architecture. Extensive experiments have been conducted in both synthetics and real-world datasets to demonstrate the effectiveness of our proposed system under the DARPA Modeling Adversarial Activity (MAA) program.

In this paper, we present MATRICS, a humanmachine hybrid system that accurately performs geopolitical forecasting by combining crowdsourcing with ensemble machine learning on online data. The system employs a pair of parallel, but highly-interconnected processing pipelines to perform “machine-aided human forecasting” and “human-aided machine forecasting”. This configuration allows the machine to provide information to the human population, saving research time and reducing fatigue, while simultaneously allowing the human population to provide feedback to the machine learning components, allowing them to filter data sources and quickly adapt to a task via online machine learning. The final forecast for each question was computed as an aggregate of the human and machine responses. The system was evaluated using data collected during the IARPA Hybrid Forecasting Competition, in which it answered 187 forecasting questions with a mean Brier score of 0.27 using volunteers and participants that were recruited via Amazon Mechanical Turk and open-source “big” data scraped from online sources such as social media, search engine results, and online historical data.

In this paper, we present MATRICS, a human-machine hybrid system that accurately performs geopolitical forecasting by combining crowdsourcing with ensemble machine learning on online data. The system employs a pair of parallel, but highly-interconnected processing pipelines to perform “machine-aided human forecasting” and “human-aided machine forecasting”. This configuration allows the machine to provide information to the human population, saving research time and reducing fatigue, while simultaneously allowing the human population to provide feedback to the machine learning components, allowing them to filter data sources and quickly adapt to a task via online machine learning. The final forecast for each question was computed as an aggregate of the human and machine responses. The system was evaluated using data collected during the IARPA Hybrid Forecasting Competition, in which it answered 187 forecasting questions with a mean Brier score of 0.27 using volunteers and participants that were recruited via Amazon Mechanical Turk and open-source “big” data scraped from online sources such as social media, search engine results, and online historical data.

Polarizing topics and events are often widely discussed and debated in social media, allowing researchers a unique view into the minds of large populations on everything from politics to entertainment. Previous work in identifying polarization in social media has largely used traditional community detection methods that are often confounded by the existence of neutral users and content. To address this problem, we propose a novel approach based on nonnegative matrix factorization (NMF) on a tripartite network to illuminate latent polarization patterns. The proposed methods are designed to work in contexts varying in the nature of the controversy, the level of polarization, the number of polarity groups involved and the presence of neutral entities. We use real-world Tumblr datasets to show that our algorithm exhibits superior performance in identifying polarization in online communities with respect to a range of real-world topics. To the best of our knowledge, our work is the first attempt to analyze polarization on the Tumblr platform.

Predicting the outcome of geopolitical events is of huge importance to many organizations, as these forecasts may be used to make consequential decisions. Prediction polling is a common method used in crowdsourcing platforms for geopolitical forecasting, where a group of non-expert participants are asked to predict the outcome of a geopolitical event and the collected responses are aggregated to generate a forecast. It has been demonstrated that forecasts by such a crowd can be more accurate than the forecasts of experts. However, geopolitical prediction polling is challenging because participants are highly heterogeneous and diverse in terms of their skills and background knowledge and human resources are often limited. As a result, it is crucial to refer each question to the subset of participants that possess suitable skills to answer it, such that individual efforts are not wasted. In this paper, we propose an algorithm based on multitask learning to learn the skills of participants of a forecasting platform by using their performance history. The learned model then can be used to recommend suitable questions to forecasters. Our experimental results demonstrate that the prediction accuracy can be increased based on the proposed algorithm as opposed to when questions have been randomly assigned.

Networks are natural analytic tools in modeling adversarial activities(e.g., human trafficking, illicit drug production, terrorist financial transaction) using different intelligence data sources. However, such activities are often covert and embedded across multiple domains and contexts. They are generally not detectable and recognizable from the perspective of an isolated network, and only become apparent when multiple networks are analyzed in a joint manner. Thus, one of the main research topics in modeling adversarial activities is to develop effective techniques to align and fuse information from different networks into a unified representation for global analysis. Based on the combined network representation, an equally important research topic is on detecting and matching indicating patterns to recognize the underlining adversarial activities in the integrated network. The focus of this workshop is to gather together the researchers from all relevant fields to share their experience and opinions on graph mining techniques in the era of big data, with emphasis on two fundamental problems - "Connecting the dots" and "finding a needle in a haystack", in the context of graph-based adversarial activity analytics.

How well can we predict which posts on social media platforms will eventually become popular? What approaches should we use to accomplish this task? In an era where human attention has become a commodity, the early detection of trending posts in online social media platforms is a problem of special importance, with applications ranging from business marketing to political campaigns and beyond. In this work we provide new insight towards tackling this problem by performing in-depth experiments on discovering trending posts on the popular mixed-media microblogging platform Tumblr. Inspired by prior art, our approach involves examining the key traits of the early adopters of online posts and utilizes a variety of network and temporal features to assist in performing classification. We obtain the complete data coverage of Tumblr for two consecutive months in order to study the characteristics and emerging patterns of popular posts. Based on the data, we investigate the interplay between the observation window of early adopters, the performance of individual classifiers, and the importance of various features. We find consistent trends where the importance of network features increases as the observation window increases, while the importance of content and temporal features decreases. To the best of our knowledge, this work is the first attempt to carry out a large-scale study on the early detection of trending posts on Tumblr.

The Islamic State of Iraq and Syria (ISIS) is a extremist militant group in the Middle East known to employ social media for propaganda and recruiting purposes. In particular, the social media website Twitter is well known to be exploited by ISIS supporters. To this end, we devise an effective and scalable classification scheme to filter out ISIS propaganda accounts from the rest of the Twitter accounts covering the general population in specific geographic regions. We take a data-driven approach to train and test a classifier using a combination of both content-based and network-based features on a large-scale dataset consisting of 10% random sample of all public Twitter posts in 2014. Such a dataset contains a total of 4,820 confirmed ISIS propaganda accounts and a large collection of general user accounts originated in nearby Egypt and Saudi Arabia regions. We show that high precision and specificity is obtained with the proposed classifier through cross-validation. In addition, we demonstrate that the use of network-based features derived from the Twitter @mention network is crucial on accurte classification of ISIS propaganda accounts.

Mining users'= topics of interest is one of the most important tasks for social media services. Given known topic associations for some fraction of the users in an online microblogging platform, our goal is to infer the topics of interest for the remaining users in the same site. Specifically, we proposed a novel bi-relational graph model to capture the interactions among users and their shared topics of interests. The proposed graph model contains two sub-graphs: one corresponds to users and the other corresponds to topics. Such a representation allows for effective exploitation of both user homophily relation and topic correlation simultaneously. This is in contrast with previous work where these two factors are considered in isolation. Subsequently, the user interest discovery problem is formulated as a multi-label learning problem on the bi-relational graph, with the goal to estimate the optimized associations between user nodes and topic nodes across the two sub-graphs. Our experiment is carried out with a complete month-long data collected from Twitter and Tumblr via GNIP Decahose1and Firehose2respectively. The large-scale studies shed light on the effectiveness of inferring user interests based on the underlying social connections.