- freely available
Information 2019, 10(6), 220; https://doi.org/10.3390/info10060220
2.1. Collaboration and Social Context
2.2. Exponential Random Graph Model
- is the probability of the entire graph being conditional on parameters represented by ;
- is a normalizing constant;
- is a vector of parameters associated with the graph statistics; and
- is a vector of the graph statistics.
3. Related Work
3.1. Recommending Collaborators
3.2. Recommending Collaborators Based on Social Context
3.3. RSs Based on the ERGM
4. Methodology Used
4.1. Phase One: Estimating Weights
- Historical collaboration data: Historical collaboration data play an important role in building and testing context-aware RSs. Historical collaboration data are data related to the collaborations of a group of researchers in a particular scientific community from a previous time period. These data include historical collaboration networks, research areas of individuals in the collaboration network, and centrality scores for these individuals. The observed collaboration network is a historical collaboration network.
- Selecting parameters: Contextual parameters that match the theories about collaboration factors must be selected. For example, because it is assumed that researchers choose to collaborate with similar and influential researchers, the following parameters are selected: research areas; social context parameters used to measure influence, such as degree centrality; betweenness centrality; and eigenvector centrality. These parameters represent different actor attributes. In addition, standard parameters corresponding to network topology can be included . Each parameter corresponds to a network configuration, which in turn corresponds to a network theory.
- Estimating: Estimating can involve systematically searching through possible parameter values until the right estimate is achieved. The outputs are the estimated weights for the chosen parameters. These values are validated through evaluation.
- Evaluating: The estimated parameters are evaluated using goodness of fit (GOF), which is a statistical approach for assessing how well estimated parameters fit the observed data using a t-ratio . This method is included in the ERGM package and involves a simulation of networks using estimated parameters and summary statistics. The statistics of simulated networks are compared with the actual network using a t-ratio.
4.2. Phase Two: Making Recommendations
- is a variable that shows whether a given user and collaborator have similar research areas;
- is the weight for the given factor ; and
- is the value of context factor for potential collaborator
- Scopus: Scopus is one of largest abstract and citation databases for peer-reviewed literature, including scientific journals, books, and conference proceedings. Publications from two years (2013, 2014) were collected for faculty members associated with the College of Computer and Information Sciences (CCIS) that are indexed by Scopus.
- College annual report: The college annual report details the main activities and achievements of students and faculty members each year. This information includes a list of the different types of publications for each faculty member indexed in Scopus and in other citation databases.
- Faculty websites: Every faculty has a webpage hosted on the university server that includes information about each faculty member and their teaching and research activities.
- Research_Match, which demonstrates the significance that similar research areas have on collaboration;
- Degree_Activity, which illustrates the significance that degree centrality has on collaboration;
- Betweenness_Activity, which indicates the significance of betweenness centrality;
- Eigenvector_Activity, which identifies the significance that eigenvector centrality has on collaboration;
- Edges, which is a network topology parameter in ERGM; and
- Alternative Triangulation (AT), which is a network topology parameter that represents transitivity. This parameter demonstrates the significance that a common collaborator has on collaboration.
- Group 1—Old: Old users are faculty members who collaborated in 2013 and 2014.
- Group 2—New: New users consist of new members who joined the CCIS network in 2014.
- Scenario 1—ERGM: This scenario uses weights for contextual factors calculated using the ERGM.
- Scenario 2—Equal: This scenario considers equal weights for all contextual factors.
- Scenario 3—Random: This scenario uses random weights for contextual factors.
- true positives (tp): These are the correctly predicted collaborators.
- true negatives (tn): These are the correctly predicted negative values.
- false positives (fp): These occur when a collaborator is predicted but the actual data show this prediction to be false.
- false negatives (fn): These occur when the RS fails to produce an accurate prediction.
6.1. Old Users
6.2. New Users
7. Comparison with Other Methods
8. Discussion and Research Limitations
Conflicts of Interest
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|Closeness centrality||Measure of relative node i distances to the n other nodes.|
is the length of the path between i and j.
|Betweenness Centrality||Measure of extent to which a node lies between other nodes in the network.|
is the number of shortest paths between i and j.
is the number of shortest paths between i and j that k lies on.
|Eigenvector Centrality||Measure of node centrality that takes into account neighbors’ centralities.|
|Kats Centrality||Measures node influence within a network.|
|Alternative Triangulation (AT)||1.1855||0.015||*|
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