Investigation of Team Formation in Dynamic Social Networks

Team Formation Problem (TFP) in Social Networks (SN) is to collect the group of individuals who match the requirements of given tasks under some constraints. It has several applications, including academic collaborations, healthcare, and human resource management. These types of problems are highly challenging because each individual has his or her own demands and objectives that might conflict with team objectives. The major contribution of this dissertation is to model a computational framework to discover teams of experts in various applications and predict the potential for collaboration in the future from a given SN. Inspired by an evolutionary search technique using a higher-order cultural evolution, a framework is proposed using Knowledge-Based Cultural Algorithms to identify teams from co-authorship and industrial settings. This model reduces the search domain while guiding the search direction by extracting situational knowledge and updating it in each evolution. Motivated from the above results, this research examines the palliative care multidisciplinary networks to identify and measure the performance of the optimal team of care providers in a highly dynamic and unbalanced SN of volunteer, community, and professional caregivers. Thereafter, a visualization framework is designed to explore and monitor the evolution in the structure of the care networks. It helps to identify isolated patients, imbalanced resource allocation, and uneven service distribution in the network. This contribution is recognized by Hospice and the Windsor Essex Compassion Care Community in partnership with the Faculty of Nursing. In each setting, several cost functions are attempted to measure the performance of the teams. To support this study, the temporal nature of two important evaluation metrics is analyzed in Dynamic Social Networks (DSN): dynamic communication cost and dynamic expertise level. Afterward, a novel generic framework for TFP is designed by incorporating essential cost functions, including the above dynamic cost functions. The Multi-Objective Cultural Algorithms (MOCA) is used for this purpose. In each generation, it keeps track of the best solutions and enhances exploration by driving mutation direction towards unexplored areas. The experimental results reach closest to the exact algorithm and outperform well-known searching methods. Subsequently, this research focuses on predicting suitable members for the teams in the future, which is typically a real-time application of Link Prediction. Learning temporal behavior of each vertex in a given DSN can be used to decide the future connections of the individual with the teams. A probability function is introduced based on the activeness of the individual. To quantify the activeness score, this study examines each vertex as to how actively it interacts with new and existing vertices in DSN. It incorporates two more objective functions: the weighted shortest distance and the weighted common neighbor index. Because it is technically a classification problem, deep learning methods have been observed as the most effective solution. The model is trained and tested with Multilayer Perceptron. The AUC achieves above 93%. Besides this, analyzing common neighbors with any two vertices, which are expected to connect, have a high impact on predicting the links. A new method is introduced that extracts subgraph of common neighbors and examines features of each vertex in the subgraph to predict the future links. The sequence of subgraphs\u27 adjacency matrices of DSN can be ordered temporally and treated as a video. It is tested with Convolutional Neural Networks and Long Short Term Memory Networks for the prediction. The obtained results are compared against heuristic and state-of-the-art methods, where the results reach above 96% of AUC. In conclusion, the knowledge-based evolutionary approach performs well in searching through SN and recommending effective teams of experts to complete given tasks successfully in terms of time and accuracy. However, it does not support the prediction problem. Deep learning methods, however, perform well in predicting the future collaboration of the teams

Computational approaches for engineering effective teams

The performance of a team depends not only on the abilities of its individual members, but also on how these members interact with each other. Inspired by this premise and motivated by a large number of applications in educational, industrial and management settings, this thesis studies a family of problems, known as team-formation problems, that aim to engineer teams that are effective and successful. The major challenge in this family of problems is dealing with the complexity of the human team participants. Specifically, each individual has his own objectives, demands, and constraints that might be in contrast with the desired team objective. Furthermore, different collaboration models lead to different instances of team-formation problems. In this thesis, we introduce several such models and describe techniques and efficient algorithms for various instantiations of the team-formation problem. This thesis consists of two main parts. In the first part, we examine three distinct team-formation problems that are of significant interest in (i) educational settings, (ii) industrial organizations, and (iii) management settings respectively. What constitutes an effective team in each of the aforementioned settings is totally dependent on the objective of the team. For instance, the performance of a team (or a study group) in an educational setting can be measured as the amount of learning and collaboration that takes place inside the team. In industrial organizations, desirable teams are those that are cost-effective and highly profitable. Finally in management settings, an interesting body of research uncovers that teams with faultlines are prone to performance decrements. Thus, the challenge is to form teams that are free of faultlines, that is, to form teams that are robust and less likely to break due to disagreements. The first part of the thesis discusses approaches for formalizing these problems and presents efficient computational methods for solving them. In the second part of the thesis, we consider the problem of improving the functioning of existing teams. More precisely, we show how we can use models from social theory to capture the dynamics of the interactions between the team members. We further discuss how teams can be modified so that the interaction dynamics lead to desirable outcomes such as higher levels of agreement or lesser tension and conflict among the team members

Productive Cluster Hire

Discovering a group of experts to complete a set of tasks that require various skills is known as Cluster Hire Problem. Each expert has a set of skills which he/she can offer and charges a monetary cost to offer their expertise. We are given a set of projects that need to be completed and on completion of each project, the organization gets a Profit. For performing a subset of given projects, we are given a predetermined budget. This budget is spent on hiring experts. We extend this problem by introducing the productivity and capacity of experts. We want to hire experts that are more productive, and this factor is determined on the basis of their past experience. We also want to make sure that no expert is overworked as it is not possible for a single expert to provide his/her expertise for unlimited times. Our goal is to hire as many experts as possible in which the sum of their hiring costs (i.e., salary) is under the given budget as we are interested to maximize the profit and also maximize the productivity of the group of experts, our problem is a bi-objective optimization problem. To achieve this, we propose two different approaches that maximize our Profit and Productivity

The Impact of AI on Recruitment and Selection Processes: Analysing the role of AI in automating and enhancing recruitment and selection procedures

Human resource management is the process of identifying, recruiting, hiring, and training talented individuals, as well as providing them with career advancement possibilities and critical feedback on their performance. The purpose of this study was to investigate the function of AI in HRM practises using qualitative bibliometric analysis. Scopus, emerald, and the Jstore library are used as data sources. This analysis contains adjustments to data spanning 18 years. It also showed that there is a constant improvement and introduction of new technological conveniences. In accordance with the present market climate, which promotes and celebrates process management and people management practises targeted at making the organisation economically viable and different from the competition, this is a positive development. This work advances the theoretical understanding of AI\u27s growth in the HR sector in light of this reality. Articles and proceedings examined in this research reveal that different authors and academic institutions provide different perspectives on the problem

Networked Employment Discrimination

Employers often struggle to assess qualified applicants, particularly in contexts where they receive hundreds of applications for job openings. In an effort to increase efficiency and improve the process, many have begun employing new tools to sift through these applications, looking for signals that a candidate is “the best fit.” Some companies use tools that offer algorithmic assessments of workforce data to identify the variables that lead to stronger employee performance, or to high employee attrition rates, while others turn to third party ranking services to identify the top applicants in a labor pool. Still others eschew automated systems, but rely heavily on publicly available data to assess candidates beyond their applications. For example, some HR managers turn to LinkedIn to determine if a candidate knows other employees or to identify additional information about them or their networks. Although most companies do not intentionally engage in discriminatory hiring practices (particularly on the basis of protected classes), their reliance on automated systems, algorithms, and existing networks systematically benefits some at the expense of others, often without employers even recognizing the biases of such mechanisms. The intersection of hiring practices and the Big Data phenomenon has not produced inherently new challenges. While this paper addresses issues of privacy, fairness, transparency, accuracy, and inequality under the rubric of discrimination, it does not pivot solely around the legal definitions of discrimination under current federal anti-discrimination law. Rather, it describes a number of areas where issues of inherent bias intersect with, or come into conflict with, socio-cultural notions of fairness

Cluster Hire in a Network of Experts

Finding a group of experts is a natural way to perform a collection of tasks that need a set of diversified skills. This can be done by assigning skills to different experts with complementary expertise. This allows organizations and big institutes to efficiently hire a group of experts with different skill sets to deliver a series of required tasks to finish a set of projects. We are given a collection of projects, in which each of them needs a set of required skills. Performing each project brings a profit to the organization. We are also given a set of experts, each of them is equipped with a set of skills. To hire an expert, the organization should provide her with monetary cost (i.e., salary). Furthermore, we are given a certain amount of budget to hire experts. The goal is to hire a group of experts within the given budget to perform a subset of projects that maximize the total profit. This problem is called Cluster Hire and was introduced recently. We extend this problem by making the realistic assumption that there exists an underlying network among experts. This network is built based on past collaboration among experts. If two experts have past collaboration, they form a more collaborative and efficient team in the future. In addition to maximizing the total profit, we are also interested to find the most collaborative group of experts by minimizing the communication cost between them. We propose two greedy algorithms with different strategies to solve this problem. Extensive experiments on a real dataset show our proposed algorithms can find a group of experts that cover projects with high profit while experts can communicate with each other efficiently