A critical component to any modern cybersecurity endeavor is effective use of its human resources to secure networks, maintain services and mitigate adversarial events. Despite the importance of the human cyber- analyst and operator to cybersecurity, there has not been a corresponding rise in data-driven analytical approaches for understanding, evaluating, and improving the effectiveness of cybersecurity teams as a whole. Fortunately, cyber defense competitions are well-established and provide a critical window into what makes a cybersecurity team more or less effective. We examined data collected at the national finals and four regional events of the Collegiate Cyber Defense Competition and posited that experience, access to simulation-based training, and functional role composition by the teams would predict team performance on four scoring dimensions relevant to the application of information assurance skills and defensive cyber operations: (a) maintaining services, (b) help-desk customer support, (c) handling scenario injects, and (d) mitigating red team attacks. Bayesian analysis highlighted that experience was a strong predictor of service availability, scenario injects, and red team defense. Simulation training was also associated with good performance along these scoring dimensions. High-performing and experienced teams clustered with one another based on the functional role composition of team skills. These results are discussed within the context of stages of team development, the efficacy of challenge-based learning events, and reinforce previous analytical results from cyber competitions.

State-of-the-art in network science of teams offers effective recommendation methods to answer questions like who is the best replacement, what is the best team expansion strategy, but lacks intuitive ways to explain why the optimization algorithm gives the specific recommendation for a given team optimization scenario. To tackle this problem, we develop an interactive prototype system, Extra, as the first step towards addressing such a sense-making challenge, through the lens of the underlying network where teams embed, to explain the team recommendation results. The main advantages are (1) Algorithm efficacy: we propose an effective and fast algorithm to explain random walk graph kernel, the central technique for networked team recommendation; (2) Intuitive visual explanation: we present intuitive visual analysis of the recommendation results, which can help users better understand the rationality of the underlying team recommendation algorithm.

The PART-WHOLE relationship routinely finds itself in many disciplines, ranging from collaborative teams, crowdsourcing, autonomous systems to networked systems. From the algorithmic perspective, the existing work has primarily focused on predicting the outcomes of the whole and parts, by either separate models or linear joint models, which assume the outcome of the parts has a linear and independent effect on the outcome of the whole. In this paper, we propose a joint predictive method named PAROLE to simultaneously and mutually predict the part and whole outcomes. The proposed method offers two distinct advantages over the existing work. First (Model Generality), we formulate joint PART-WHOLE outcome prediction as a generic optimization problem, which is able to encode a variety of complex relationships between the outcome of the whole and parts, beyond the linear independence assumption. Second (Algorithm Efficacy), we propose an effective and efficient block coordinate descent algorithm, which is able to find the coordinate-wise optimum with a linear complexity in both time and space. Extensive empirical evaluations on real-world datasets demonstrate that the proposed PAROLE (1) leads to consistent prediction performance improvement by modeling the non-linear part-whole relationship as well as part-part interdependency, and (2) scales linearly in terms of the size of the training dataset.

Existing approaches to compute team process measures are primarily based on survey ratings, semantic classification of communications, and social network analyses. Although existing approaches reveal important information about team performance, they face specific limitations. Survey methodologies are in general unreliable, biased, and not dynamic; communication classifications are often a-theoretical; and social network analytics ignore the meanings of messages. Accordingly, we develop a better-defined formal empirical approach for computing team process measures. Our contribution builds on existing work in semantic classification of messages in broadcast communications and proposes a general set of meanings of messages for team processes. Using the meanings of messages, we propose formal approaches to compute team process measures. We evaluate these measures using a military data set and find the following. First, our text mining approach to infer meanings of messages significantly improves over the bag of words approach and yields macroaverage and microaverage F-measures of 70% and 80%, respectively. Second, compared with baseline measures such as degree centrality, cognitive processes remain significantly stable with time, whereas measures such as affective process significantly increase with time.

In this paper, we study ways to enhance the composition of teams based on new requirements in a collaborative environment. We focus on recommending team members who can maintain the team's performance by minimizing changes to the team's skills and social structure. Our recommendations are based on computing team-level similarity, which includes skill similarity, structural similarity as well as the synergy between the two. Current heuristic approaches are one-dimensional and not comprehensive, as they consider the two aspects independently. To formalize team-level similarity, we adopt the notion of graph kernel of attributed graphs to encompass the two aspects and their interaction. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. Extensive empirical evaluations on real world datasets validate the effectiveness and efficiency of our algorithms.

The science of team science is a rapidly emerging research field that studies strategies to understand and enhance the process and outcomes of collaborative, team-based research. An interesting research question we address in this work is how to maintain and optimize the team performance should certain changes happen to the team. In particular, we take the network approach to understanding the teams and consider optimizing the teams with several operations (e.g., replacement, expansion, shrinkage). We develop TEAMOPT, a system to assist users in optimizing the team performance interactively to support the changes to a team. TEAMOPT takes as input a large network of individuals (e.g., co-author network of researchers) and is able to assist users in assembling a team with specific requirements and optimizing the team in response to the changes made to the team. It is effective in finding the best candidates, and interactive with users' feedback in the loop. The system is developed using HTML5, JavaScript, D3.js (front-end) and Python CGI (back-end). A prototype system is already deployed. We will invite the audience to experiment with our TEAMOPT in terms of its effectiveness, efficiency and applicability to various scenarios.

In this paper, we study the problem of TEAM MEMBER REPLACEMENT -- given a team of people embedded in a social network working on the same task, find a good candidate to best replace a team member who becomes unavailable to perform the task for certain reason (e.g., conflicts of interests or resource capacity). Prior studies in teamwork have suggested that a good team member replacement should bring synergy to the team in terms of having both skill matching and structure matching. However, existing techniques either do not cover both aspects or consider the two aspects independently. In this work, we propose a novel problem formulation using the concept of graph kernels that takes into account the interaction of both skill and structure matching requirements. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations and user studies on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall and (b) scale sub-linearly.