A unified framework for effective team formation in social networks

Given a project, the team formation problem (TFP) aims to find a team of experts with all the required skills and minimize the team’s communication cost. The TFP has emerged as a prominent research topic due to its substantial influence on team performance and the subsequent impact on organizational success. Previous research mainly concentrates on proposing heuristic or metaheuristic algorithms, yet fails to consider the historical experience of solving previous TFP. In addition, most existing learning-based approaches rely on complex multistage workflows, rather than learning the entire process end-to-end. This article proposes an end-to-end deep reinforcement learning approach that focuses on effectively reusing previous experience to tackle the TFP. First, we apply the Markov decision process to model the team formation process, in which the state is primarily characterized by the currently formed team and the skill requirements of the project. Second, we propose a self-updating deep reinforcement learning (SURL) model, in which the social network information serves as the input for the static encoding module, while the skills’ experts and the skill requirements of the project act as the inputs for the dynamic encoding module. Next, a novel deep reinforcement learning algorithm is designed to train the model by fully leveraging the historical experience of team formation. Finally, comprehensive experiments with real-world datasets demonstrate that our proposed model outperforms several commonly used optimization algorithms in terms of the average communication cost.

<p>The growth of social networks in modern information systems has enabled the collaboration of experts at an unprecedented scale. Given a social network and a task consisting of a set of required skills, Team Formation (TF) aims at finding a team of experts who can cover the required skills and can communicate in an effective manner. However, this definition has been interpreted as the problem of finding teams with minimum communication cost which neglects two aspect of team formation in real life. The first is that in reality experts are multi-skilled, hence communication cost cannot be a fixed value and should vary according to the channels employed. The second ignored aspect is disregarding teams with high expertise level who can still satisfy the required communication level. To tackle above mentioned issues, I introduce a dynamic formof communication for multi-facet relationships and use it to devise a novel approach called Chemistry Oriented Team Formation (ChemoTF) based on two new metrics; Chemistry Level and Expertise Level. Chemistry Level measures scale of communication required by the task andExpertise Level measures the overall expertise among potential teams filtered by Chemistry Level. Moreover, I adopt a personnel cost metric to filter costly teams. The experimental results on the corpus compiled for this purpose suggests that ChemoTF returns communicative and cost-effective teams with the highest expertise level compared to state-of-the-art algorithms. The corpus itself is a valuable output which contains comprehensive scholarly information in the field of computer science.</p>

<p>The growth of social networks in modern information systems has enabled the collaboration of experts at an unprecedented scale. Given a social network and a task consisting of a set of required skills, Team Formation (TF) aims at finding a team of experts who can cover the required skills and can communicate in an effective manner. However, this definition has been interpreted as the problem of finding teams with minimum communication cost which neglects two aspect of team formation in real life. The first is that in reality experts are multi-skilled, hence communication cost cannot be a fixed value and should vary according to the channels employed. The second ignored aspect is disregarding teams with high expertise level who can still satisfy the required communication level. To tackle above mentioned issues, I introduce a dynamic formof communication for multi-facet relationships and use it to devise a novel approach called Chemistry Oriented Team Formation (ChemoTF) based on two new metrics; Chemistry Level and Expertise Level. Chemistry Level measures scale of communication required by the task andExpertise Level measures the overall expertise among potential teams filtered by Chemistry Level. Moreover, I adopt a personnel cost metric to filter costly teams. The experimental results on the corpus compiled for this purpose suggests that ChemoTF returns communicative and cost-effective teams with the highest expertise level compared to state-of-the-art algorithms. The corpus itself is a valuable output which contains comprehensive scholarly information in the field of computer science.</p>

Many heuristic algorithms have been proposed in the literature to solve the team formation problem. The researchers considered a project as a set of skills selected randomly from the given pool of skills. But this leads to a skewed distribution of skills in the projects with many skills having very few experts, which we term as rare skills. In this work, we create a realistic bench-mark dataset for this problem. In general, any project/task in the industry can be seen to have a good mix of popular as well as rare skills. We first conduct an empirical study of the distribution of popular skills vs rare skills in the well-known DBLP (Digital Bibliography & Library Project) data set. The distribution of popularity of skills is shown to satisfy a power law with a heavy tail, indicating the presence of a large number of skills with very few experts and a small number of highly popular skills. We build a realistic a benchmark dataset using stratified random sampling to form tasks with various distributions of popular and rare skills. The classical team formation algorithms are evaluated using this new benchmark dataset. The evaluation is done with respect to the available communication costs in the literature as well as the execution time incurred by the algorithms. It has been observed from the experiments that all the measures show lower values of communication cost for tasks having higher proportion of popular skills.

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.

In this paper, we propose an algorithm for selecting a team of experts from a social network, which is represented by a weighted undirected graph. Each node in the graph represents an individual who has one or more skills. Different individuals can have the same skill but they might differ in their level of expertise (e.g., novice, medium, or expert). The communication cost between two individuals (nodes) is represented by the weight of the common edge (or path). The objective is to create an effective team that can carry out a specific task. The task is represented as a set of skills with specific level of expertise for each skill. An effective solution should suggest a set of individuals (nodes) who collectively meet the level of skillfulness required and at the same time guarantee low communication cost. We implemented the proposed algorithm and ran many simulation experiments to measure the cost effectiveness of the proposed algorithm.

A parallel team formation approach using crowd intelligence from social network

The overwhelming usage of social and collaboration networks provides the opportunity to analyze evolution of relationships among individuals, like celebrities or co-authors. Discovering such phenomenon in large complex networks is non-trivial due to their large sizes. In this situation, the aggregation functions used in OLAP, are useful to analyze the summarized data. OLAP has successfully proven its worth on multidimensional or complex networks. However, existing aggregations in the current OLAP systems do not produce versatile results in case of social and collaboration networks. This happens because said type of networks have structural connectivity/links among nodes, which cannot be considered by OLAP during its execution. In this situation, a useful discovery in terms of identifying pairs of nodes whose relationships is emerging in recent time, is missed. Such discovery of pairs of nodes is important for various applications such as targeted marketing, future joint partnerships and predicting future correspondence to name a few. In this study, we call such pairs as Rising_Pairs and propose an aggregation function for performing OLAP on network data whose historical information is maintained over a period of times. Using structural information, Rising_Pairs, our proposed aggregation function, discovers the strongly coupled pairs in a network data by emphasizing their recent interactions and attribute similarities. In this way, useful information related to strongly coupled pairs in a network is identified. To verify the effectiveness of our proposal, we implemented it on various types of real-world networks like Facebook, Digital Bibliography and Library Project (DBLP) and Global Positioning System (GPS) trajectory datasets and observed interesting patterns.

Managing the wicked problem of transdisciplinary team formation in socio-ecological systems

Trust is an important factor, particularly in viral/social advertising, and computing trust scores for individual users of a social network is crucial for several applications in the advertising research and practice. However, research on trust in the advertising field has been limited, and the application of computational trust to advertising research using big data is rare. Addressing the gap in the research literature, this study proposed and empirically tested a new social media analytics method, the Trust Scores in Social Media (TSM) algorithm, for measuring individual users' trust levels in a social network. TSM proposes the concept of negatively reinforced trust scores and introduces two complementary measures of trust, trustingness, and trustworthiness. Another unique and important element in the TSM algorithm is the incorporation of trust-decision involvement to adjust trust scores depending on the level of trust-decision involvement of different networks. Using small survey data and big data from social networks, this study demonstrated the effectiveness of the TSM algorithm. Various applications of the TSM algorithm to viral/social advertising research and practice are also discussed.

Widespread use of social network has changed people’s daily life as well as the way of emergency management.As a useful tool for information dissemination, communication and collaboration, social network plays an important role in the process of emergency management: mitigation, preparedness, communications, response and recovery. Emergency problem solving nowadays often need experts from various domain areas such as medicine, nuclear, chemistry, information technology and so on. It’s difficult and costly for local emergency management databases to be well prepared since emergency disasters are small probability events. This research captures the advantage of social network to tackle such issues. Expertise on online social network is available and trustworthy with supervision of crowds. Therefore, we propose a method though the integration of social position analysis and expertise level analysis to profile online individuals. Social position analysis captures the importance or prominence of an individual in social network, and expertise level analysis measures one’s expertise relevance and expertise level. Expert finding on online social can also capture one’s interest in the specific emergency disaster. We also give an empirical analysis in a selected small group. Outperformed individuals can be identified. More work is needed to validate the method on the specified social network in the future.

We consider the team formation problem in open collaborative projects existing in large community setting such as the Open Source Software (OSS) community. Given a query specifying a set of required skills for an open project and an upper bound of team size, the goal is to find a team that maximizes the Degree of Acquaintance (DoA) and covers all the required skills in the query. We define the DoA in terms of the team graph connectivity and edge weights, corresponding to the local Clustering Coefficient for each team member and the strength of social ties between the team members, respectively. We perform a statistical analysis on historical data to show the importance of the connectivity and social tie strength to the overall productivity of the teams in open projects. We show that the problem defined is NP-hard and present three algorithms, namely, PSTA, STA and NFA, to solve the problem. We experiment the algorithms on a dataset from the OSS community. The results show the effectiveness of the proposed algorithms to find a well acquainted teams satisfying a given query.

Previous studies of team formation in multi‐agent systems have typically assumed that the agent social network underlying the agent organization is either not explicitly described or the social network is assumed to take on some regular structure such as a fully connected network or a hierarchy. However, recent studies have shown that real‐world networks have a rich and purposeful structure, with common properties being observed in many different types of networks. As multi‐agent systems continue to grow in size and complexity, the network structure of such systems will become increasing important for designing efficient, effective agent communities.We present a simple agent‐based computational model of team formation, and analyze the theoretical performance of team formation in two simple classes of networks (ring and star topologies). We then give empirical results for team formation in more complex networks under a variety of conditions. From these experiments, we conclude that a key factor in effective team formation is the underlying agent interaction topology that determines the direct interconnections among agents. Specifically, we identify the property of diversity support as a key factor in the effectiveness of network structures for team formation. Scale‐free networks, which were developed as a way to model real‐world networks, exhibit short average path lengths and hub‐like structures. We show that these properties, in turn, result in higher diversity support; as a result, scale‐free networks yield higher organizational efficiency than the other classes of networks we have studied.

Key Author Analysis in Research Professionals’ Relationship Network Using Citation Indices and Centrality

Recent decades have witnessed the rise of data deluge generated by heterogeneous sources, e.g., social networks, streaming, marketing services etc., which has naturally created a surge of interests in theory and applications of large-scale convex and non-convex optimization. For example, real-world instances of statistical learning problems such as deep learning, recommendation systems, etc. can generate sheer volumes of spatially/temporally diverse data (up to Petabytes of data in commercial applications) with millions of decision variables to be optimized. Such problems are often referred to as Big-data problems. Solving these problems by standard optimization methods demands intractable amount of centralized storage and computational resources which is infeasible and is the foremost purpose of parallel and decentralized algorithms developed in this thesis.This thesis consists of two parts: (I) Distributed Nonconvex Optimization and (II) Distributed Convex Optimization.In Part (I), we start by studying a winning paradigm in big-data optimization, Block Coordinate Descent (BCD) algorithm, which cease to be effective when problem dimensions grow overwhelmingly. In particular, we considered a general family of constrained non-convex composite large-scale problems defined on multicore computing machines equipped with shared memory. We design a hybrid deterministic/random parallel algorithm to efficiently solve such problems combining synergically Successive Convex Approximation (SCA) with greedy/random dimensionality reduction techniques. We provide theoretical and empirical results showing efficacy of the proposed scheme in face of huge-scale problems. The next step is to broaden the network setting to general mesh networks modeled as directed graphs, and propose a class of gradient-tracking based algorithms with global convergence guarantees to critical points of the problem. We further explore the geometry of the landscape of the non-convex problems to establish second-order guarantees and strengthen our convergence to local optimal solutions results to global optimal solutions for a wide range of Machine Learning problems.In Part (II), we focus on a family of distributed convex optimization problems defined over meshed networks. Relevant state-of-the-art algorithms often consider limited problem settings with pessimistic communication complexities with respect to the complexity of their centralized variants, which raises an important question: can one achieve the rate of centralized first-order methods over networks, and moreover, can one improve upon their communication costs by using higher-order local solvers? To answer these questions, we proposed an algorithm that utilizes surrogate objective functions in local solvers (hence going beyond first-order realms, such as proximal-gradient) coupled with a perturbed (push-sum) consensus mechanism that aims to track locally the gradient of the central objective function. The algorithm is proved to match the convergence rate of its centralized counterparts, up to multiplying network factors. When considering in particular, Empirical Risk Minimization (ERM) problems with statistically homogeneous data across the agents, our algorithm employing high-order surrogates provably achieves faster rates than what is achievable by first-order methods. Such improvements are made without exchanging any Hessian matrices over the network. Finally, we focus on the ill-conditioning issue impacting the efficiency of decentralized first-order methods over networks which rendered them impractical both in terms of computation and communication cost. A natural solution is to develop distributed second-order methods, but their requisite for Hessian information incurs substantial communication overheads on the network. To work around such exorbitant communication costs, we propose a “statistically informed” preconditioned cubic regularized Newton method which provably improves upon the rates of first-order methods. The proposed scheme does not require communication of Hessian information in the network, and yet, achieves the iteration complexity of centralized second-order methods up to the statistical precision. In addition, (second-order) approximate nature of the utilized surrogate functions, improves upon the per-iteration computational cost of our earlier proposed scheme in this setting.