## 3 Results for the Research Questions
### RQ4: What Are the Methods That Have Been Used for Expert Finding Systems?
#### 3.4.4 Network-Based Models  
Currently, referral webs and social networks are used as basic channels for finding experts' information [4]. Network-based models use graphs to represent the main elements of expertise retrieval models. These graphs can be built in two ways. First, graphs represent documents and candidate experts as nodes, and their relationships are viewed as edges. Second, nodes are used to represent candidate experts only, and edges are used to represent the relationships between the candidate experts [1]. There are many algorithms that are commonly used in network-based models, such as HITS and PageRank algorithms and random walk propagation algorithms [4]. HITS [120] and PageRank [121] were both suggested in 1998 and used in web searching to discover the importance of a web page. The algorithms' concepts and extended approaches are similar to the Internet in structure when using expert finding in expert networks. Candidate experts or documents can be seen as web pages on the Internet, where hyperlinks among web pages represent the candidate-candidate relationships and the candidate-document relationships of the expert finding model [1]. An additional effective ranking algorithm is the PageRank algorithm, which has been effectively used by Libra, Microsoft's scholar search engine [4]. The authors of reference [47] proposed a SWAT framework for team recommendation based on network-based models. SWAT is based on a model categorized into three parts--individuals, expertise areas, and social dimensions. The three parts are captured using the competence graph, the social graph, and the history graph, respectively. Also, the authors of reference [67] proposed a graph-based regularization method using Enhanced Bl and Co-Ranking. Enhanced BL is an enhanced model for expertise ranking. It integrates the co-authorship graph with query dependent community on top of a BL model. Moreover, the authors of reference [84] adapted the PageRank algorithm for online help seeking as an expertise rank algorithm in online forums. Their algorithm considered the reputation of users in Java discussion forums. PageRank has been adapted to expertise rank extended to ExpRank-COM to measure reputation. Additionally, the authors in reference [18] modified the PageRank algorithm to ExpertRank to assess an expert's authority so that it decreases the effect of certain biasing communication behaviour in online communities.  
Propagation models are also network-based, the authors of reference [24] used a graph-based method and developed the grouping-based Steiner algorithm (GrpSteiner) to find experts in social networks. Most of the generative probabilistic models and voting models cannot mine the hidden information that can be found in social networks and that cannot be mined from the documents, and they are 'one-step' relevance models [4]. The authors of reference [46] deployed entity linking, relatedness, and entity embeddings to create a novel WEM profile for academia experts. They built their model based on a weighted and labeled graph drawn from Wikipedia.