3.9. MergedList class

The result of the rank aggregation process is an aggregate list that is stored in a MergedList object. MergedList contains a collection of MergedItems, typically sorted in decreasing score order. The score is assigned by a rank aggregation method.

Regarding the evaluation, the generated aggregate list is fed to the evaluate() function of an Evaluator. The results are written in a CSV file according to this document.

Implementation files
The MergedList class is defined in the src/MergedList.h header file; its member functions are implemented in src/MergedList.cpp. The implementations of the supported rank aggregation methods are stored in the ram directory. More specifically:

• src/ram/Agglomerative.cpp implements the Agglomerative Aggregation method of Chatterjee et al., 2018.
• src/ram/CombMNZ.cpp implements the CombMNZ linear combination methods as they are described in the paper of  Renda et al., 2003.
• src/ram/CombSUM.cpp implements the CombSUM linear combination methods (including Borda Count) as they are described in the paper of Renda et al., 2003.
• src/ram/CondorcetWinners.cpp implements one of the oldest approaches to rank aggregation, the Condorcet criterion method.
• src/ram/CopelandWinners.cpp implements the method of Copeland Winners which is a variant of the Condorcet Winners method.
• src/ram/CustomMethods.cpp contains two sample functions which facilitate the integration of custom rank aggregation methods.
• src/ram/DIBRA.cpp implements the Iterative Distance-Based Weighted method of Akritidis et al., 2022.
• src/ram/KemenyOptimal.cpp contains the brute force implementation of Kemeny optimal aggregation.
• src/ram/MC.cpp implements the four Markov Chains method of Dwork et al., 2001 and the MCT method of DeConde et al., 2006.
• src/ram/OutrankingApproach.cpp implements the Outranking Approach of Farah and Vanderpooten, 2007.
• src/ram/PrefRel.cpp implements the Preference Relations Weighted method of Desarkar et al., 2016.
• src/ram/RobustRA.cpp implements the Robust Rank Aggregation method (RRA) of Kolde et al., 2012.

For more details, please visit the Publications page, or follow the links in the Introduction page. A guide on how custom rank aggregation implementations can be integrated to FLAGR is given here.

Technical Details
The elements of MergedList are organized in two ways. More specifically:

• A hash table (hash_table member variable) with linked lists as chains (for collision resolution) is employed to support fast fusion of the individual InputLists. The contents of this hash table are MergedItem objects; the search keys are the unique identifiers of the associated InputItems (MergedItem inherits the members of InputItem).
• A typical array of MergedItem pointers (item_list member variable) that is used to sort the objects in decreasing score order.

The following interactive block diagram depicts the functionality of the MergedList class inside the entire architecture of FLAGR.