Update README.md

Author: mahdis-saeedi

docs/survey/README.md

@@ -1,14 +1,14 @@
-## Computational Team Formation
+## Team Formation Strategies
 
 
 <div align="center">
   <img src="graph/figures/figs_draft/taxonomy.png" alt="Figure1: A taxonomy of the Team Formation methods." >
   <p align="center"><em>Figure1: A taxonomy of the computational Expert Team Formation methods.</em></p>
 </div>
 
-Team formation involves selecting experts with certain skills to form a successful task-oriented team. Team formation approaches can be distinguished based on the way optimizations are performed: i) search-based, where the search for an *almost surely* successful team (optimum team) is carried out over the subgraphs of an expert graph using subgraph optimization methods, or it is performed on subsets of experts as variants of the set cover problem [^1][^2][^3]  using operations research (OR) techniques including integer linear/nonlinear programming, and ii) learning-based, where machine learning approaches are used to learn the distributions of experts and skills in the context of previous (un)successful teams in order to draw future successful teams.
+Team formarion involves selecting skillful experts to form an *almost surely* successful team (optimum team) or refining the team composition in some domains like sports teams to maintain or improve performance. Computational team formation approaches can be distinguished based on the optimization techniques: i) search-based, where the search for a successful team is by finding the subgraphs of an expert graph using subgraph optimization methods, or it is performed on subsets of experts as variants of the set cover problem [^1][^2][^3]  using operations research (OR) techniques such as integer linear/nonlinear programming, and ii) learning-based, where machine learning approaches are used to learn the distributions of experts and skills in the context of previous (un)successful teams in order to recommend future successful teams.
 
-Within the search-based category, operations research, by and large, optimizes the mutually independent selection of experts, overlooking the organizational and collaborative ties among individuals [^1][^4][^5][^6]. However, graph-based methods rely on the premise that a team is inherently relational and is a property of the interaction among the experts and how effectively they can collaborate. Moreover, learning-based methods have been proposed to bring efficiency while enhancing efficacy due to their iterative and online learning procedure [^7][^8]. As seen in Figure 2, there is an overlap between team formation approaches. As an instance, graph-based and operations research-based methods, where optimization functions have been defined based on linear or nonlinear equation of Boolean variables representing edges on the expert graph [^9]. Additionally, learning-based methods utilize the expert graph to learn vector representations of skills using graph neural networks (GNNs), which helps reduce the complexity of neural models at the input layer [^12]. Specifically, graph neural networks [^10][^11] provide an effective and efficient general framework for solving graph analytics problems by converting a graph into a low-dimensional vector space while preserving its graph-structured information. Having demonstrated strong performances across a wide range of problems, including natural language processing [^13], knowledge graphs [^14] and recommender systems [^15], graph neural networks are gradually finding their application in Expert Team Formation [^12]. 
+Within the search-based category, operations research, optimizes the mutually independent selection of experts, overlooking the organizational and collaborative ties among individuals [^1][^4][^5][^6]. However, graph-based approaches view teams as inherently relational, emphasizing the importance of expert interactions and collaboration. Moreover, learning-based methods have been proposed to bring efficiency while enhancing efficacy due to their iterative and online learning procedure [^7][^8]. As seen in Figure 2, there is an overlap between team formation approaches. As an instance, graph-based and operations research-based methods, where optimization functions have been defined based on linear or nonlinear equation of Boolean variables representing edges on the expert graph [^9]. Additionally, learning-based methods utilize the expert graph to learn vector representations of skills using graph neural networks (GNNs), which helps reduce the complexity of neural models at the input layer [^12]. Specifically, graph neural networks [^10][^11] provide an effective and efficient general framework for solving graph analytics problems by converting a graph into a low-dimensional vector space while preserving its graph-structured information. Having demonstrated strong performances across a wide range of problems, including natural language processing [^13], knowledge graphs [^14] and recommender systems [^15], graph neural networks are gradually finding their application in Expert Team Formation [^12]. 
 
 <p align="center">
   <img src="graph/figures/figs_draft/venn.png" alt="Figure2: Overlapping Team Formation Approaches">