Team Formation In Social Networks Research Articles

A novel state space reduction algorithm for team formation in social networks.

Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels.

A unified framework for effective team formation in social networks

Collaboration networks are social networks in which nodes represent experts, and edges represent the interactions between them. Team Formation Problem (TFP) in Social Networks (SN) is to construct a group of individuals to work on complex tasks. Teams should satisfy the skill set required by the tasks and can collaborate effectively under multiple constraints. Although many algorithms have been proposed to confront the TFP, most of them optimize different criteria and various parameters (e.g. communication cost or expertise level). There is no unified framework to incorporate the most significant parameters towards formulating effective teams of experts. We propose a unified framework for the TFP in SN based on a multi-objective cultural algorithm that involves the integration of essential cost functions such as communication cost, expertise level, collective trust score, and geological proximity. Since these are conflicting objectives, we return a set of Pareto front of teams that are not dominated by other feasible teams with regards to any of the objectives. Moreover, we examine the temporal nature of both communication costs and expertise levels in our model and introduce a new method to formulate them. We introduce a profile similarity formula to express the trust score. We then discuss the importance of emotional index in TFP. Our model is tested on a benchmark table, which is generated with various criteria of social networks. Our model is then compared with NSGA II, Graph-Based and Exhaustive search.

Toward Efficient Team Formation for Crowdsourcing in Noncooperative Social Networks.

Crowdsourcing has become a popular service computing paradigm for requesters to integrate the ubiquitous human-intelligence services for tasks that are difficult for computers but trivial for humans. This paper focuses on crowdsourcing complex tasks by team formation in social networks (SNs) where a requester connects to a large number of workers. A good indicator of efficient team collaboration is the social connection among workers. Most previous social team formation approaches, however, either assume that the requester can maintain information of all workers and can directly communicate with them to build teams, or assume that the workers are cooperative and be willing to join the specific team built by the requester, both of which are impractical in many real situations. To this end, this paper first models each worker as a selfish entity, where the requester prefers to hire inexpensive workers that require less payment and workers prefer to join the profitable teams where they can gain high revenue. Within the noncooperative SNs, a distributed negotiation-based team formation mechanism is designed for the requester to decide which worker to hire and for the worker to decide which team to join and how much should be paid for his skill service provision. The proposed social team formation approach can always build collaborative teams by allowing team members to form a connected graph such that they can work together efficiently. Finally, we conduct a set of experiments on real dataset of workers to evaluate the effectiveness of our approach. The experimental results show that our approach can: 1) preserve considerable social welfare by comparing the benchmark centralized approaches and 2) form the profitable teams within less negotiation time by comparing the traditional distributed approaches, making our approach a more economic option for real-world applications.

Parameterized complexity of team formation in social networks

Given a task that requires some skills and a social network of individuals with different skills, the Team Formation problem asks to find a team of individuals that together can perform the task, while minimizing communication costs. Since the problem is NP-hard, we identify the source of intractability by analyzing its parameterized complexity with respect to parameters such as the total number of skills k, the team size l, the communication cost budget b, and the maximum vertex degree Δ. We show that the computational complexity strongly depends on the communication cost measure: when using the weight of a minimum spanning tree of the subgraph formed by the selected team, we obtain fixed-parameter tractability for example with respect to the parameter k. In contrast, when using the diameter as measure, the problem is intractable with respect to any single parameter; however, combining Δ with either b or l yields fixed-parameter tractability.

Team formation in social networks based on collective intelligence – an evolutionary approach

The tremendous growth of the social web has inspired research communities to discover social intelligence, which encompasses a wide spectrum of knowledge characterized by human interaction, communication and collaboration, thereby exploiting collective intelligence (CI) to support the successful existence of social communities on the Web. In this work, we address the team formation problem for generalized tasks where a set of experts is to be discovered from an expertise social network that can collaborate effectively to accomplish a given task. The concept of CI that emerges from these collaborations attempts to maximize the potential of the team of experts, rather than only aggregating individual potentials. Because the team formation problem is NP-hard, a genetic algorithm-based approach is applied to optimize computational collective intelligence in web-based social networks. To capture the essence of CI, a novel quantitative measure Collective Intelligence Index (CII) is proposed that takes two factors into account ---the "enhanced expertise score" and the "trust-based collaboration score". This measure relates to the social interactions among experts, reflecting various affiliations that form a network of experts that help to drive creativity by deepening engagements through collaboration and the exchange of ideas and expertise, thereby enriching and enhancing the knowledge base of experts. The presented model also captures the teams' dynamics by considering trust, which is essential to effective interactions between the experts. The computational experiments are performed on a synthetic dataset that bears close resemblance to real-world expertise networks, and the results clearly establish the effectiveness of our proposed model.