Learning heterogeneous subgraph representations for team discovery

The team discovery task is concerned with finding a group of experts from a collaboration network who would collectively cover a desirable set of skills. Most prior work for team discovery either adopt graph-based or neural mapping approaches. Graph-based approaches are computationally intractable often leading to sub-optimal team selection. Neural mapping approaches have better performance, however, are still limited as they learn individual representations for skills and experts and are often prone to overfitting given the sparsity of collaboration networks. Thus, we define the team discovery task as one of learning subgraph representations from a heterogeneous collaboration network where the subgraphs represent teams which are then used to identify relevant teams for a given set of skills. As such, our approach captures local (node interactions with each team) and global (subgraph interactions between teams) characteristics of the representation network and allows us to easily map between any homogeneous and heterogeneous subgraphs in the network to effectively discover teams. Our experiments over two real-world datasets from different domains, namely DBLP bibliographic dataset with 10,647 papers and IMDB with 4882 movies, illustrate that our approach outperforms the state-of-the-art baselines on a range of ranking and quality metrics. More specifically, in terms of ranking metrics, we are superior to the best baseline by approximately 15 % on the DBLP dataset and by approximately 20 % on the IMDB dataset. Further, our findings illustrate that our approach consistently shows a robust performance improvement over the baselines

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

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

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure

Improving Quality of the Solution for the Team Formation Problem in Social Networks Using SCAN Variant and Evolutionary Computation

Social Network Analysis helps to visualize and understand the roles and relationships that ease or impede the collaboration and sharing of the information and knowledge in an organization. In this research work, we will focus on the Team Formation Problem (TFP) which is an open problem where we need to identify an ideal team, with members of complementary talent or skills, to solve any given task. Current research suggests that TFP solutions have been attempted with evolutionary computation approach using Cultural Algorithms (CA) and Genetic Algorithms (GA). However, SCAN (Structural Clustering Algorithm for Networks) variants such as WSCAN (Weighted Structural Clustering Algorithm for Networks) demonstrate a high capability to find solutions for another type of network problems. In this thesis, we first propose to use WSCAN-TFP algorithm to deal with the problem of team formation in social networks, and we our findings indicate that WSCAN-TFP algorithm worked faster than the evolutionary algorithms counterparts but was of lower performance compared to CAs and GAs. Next, we propose two hybrid solutions by combining GA and CA with a modified WSCAN-TFP algorithm. To test the performance of our proposed approaches, we define multiple quality criteria based on communication cost (CC), average fitness score (AFS) and average processing time. We used big datasets from DBLP nodes network with sizes 50K and 100K. The results show that our proposed methods HGA and HCA can find the near-optimal solutions faster with minimum communication cost with the improvement of $\approx 66\%$ and $\approx 57\%$ in average fitness in comparison to existing GA and CA methods respectively

Temporal Neural Team Formation with Negative Sampling

Predicting future successful teams of experts who can synergistically work in concert with each other and en masse cover a set of required skills of a degree necessary for the achievement of the desired outcome is challenging due to several reasons, including 1) the magnitude of the pool of plausible expert candidates with diverse backgrounds and skills, and 2) the drift and variability of collaborative ties of experts and their level of expertise in each area in time. Prior works in team formation have neglected the fact that experts’ skill, interests, and collaborative ties change over time. We can categorize previous works in team formation based on their method of optimization: 1) search-based, where the search for the optimum team is carried over all the subgraphs of expert networks or via integer programming, however, these works overlooked the temporal nature of human collaborations. 2) learning-based, where machine learning approaches are used to learn the distributions of experts and skills in the context of successful teams in the history to predict almost surely successful teams in the future. However, they also fail to recognize the possible drift and variability of experts’ skills, interest, and collaborative ties in time and its impact on the prediction of future successful teams. Moreover, neural models are prone to overfitting when training data suffers from the long-tail phenomenon, i.e., few experts have a lot of successful collaborations and the majority have participated sparingly. To overcome the aforementioned problems, i) we propose a streaming scenario training strategy for neural models to help the model in the prediction of future successful teams of experts, where instead of shuffling our datasets, we train the models in an orderly manner, to grasp the changes in experts’ skills, interests, and collaborations, and ii) we propose an optimization objective that leverages both successful and virtually unsuccessful teams via various negative sampling heuristics, and iii) we conduct experiments on four large-scale benchmark datasets with varying distribution of skills and members namely, dblp, imdb, uspt, and github. Finally, we empirically demonstrate how our proposed objective functions and training method, outperform the state-of-the-art approaches in terms of effectiveness and efficiency