Abstract:
An educational institution (also referred as a university) is structurally modeled using a university model graph. Such a model helps compare educational institutions at various levels—university level, department level, faculty member level, or student level. One of the requirements of comparison is to normalize the similarities and identify and elaborate the differences across multiple educational institutions. A way to achieve this is to model the educational institutions using comparable elements; specifically, the university model graph allows for such comparison as multiple educational institutions are modeled based on the same set of concepts and notions. A system and method for comparing educational institutions based on their respective university model graphs is discussed.

Description:
[0001]    1. A reference is made to the applicants&#39; earlier Indian patent application titled “System and Method for an Influence based Structural Analysis of a University” with the application number 1269/CHE2010 filed on 6 May 2010. 
         [0002]    2. A reference is made to another of the applicants&#39; earlier Indian patent application titled “System and Method for Constructing a University Model Graph” with an application number 1809/CHE/2010 and filing date of 28 June, 2010. 
         [0003]    3. A reference is made to yet another of the applicants&#39; earlier Indian patent application titled “System and Method for University Model Graph based Visualization” with the application number 1848/CHE/2010 dated 30 Jun. 2010. 
         [0004]    4. A reference is made to yet another of the applicants&#39; earlier Indian patent application titled “System and Method for What-If Analysis of a University based on University Model Graph” that is under filing process. 
       FIELD OF THE INVENTION 
       [0005]    The present invention relates to the analysis of the information about a university in general, and more particularly, the analysis of the university based on the structural representations. Still more particularly, the present invention relates to a system and method for comparing multiple universities based on the model graphs associated with the universities. 
       BACKGROUND OF THE INVENTION 
       [0006]    An Educational Institution (EI) (also referred as University) comprises of a variety of entities: students, faculty members, departments, divisions, labs, libraries, special interest groups, etc. University portals provide information about the universities and act as a window to the external world. A typical portal of a university provides information related to (a) Goals, Objectives, Historical Information, and Significant Milestones, of the university; (b) Profile of the Labs, Departments, and Divisions; (c) Profile of the Faculty Members; (d) Significant Achievements; (e) Admission Procedures; (f) Information for Students; (g) Library; (h) On- and Off-Campus Facilities; (i) Research; (j) External Collaborations; (k) Information for Collaborators; (l) News and Events; (m) Alumni; and (n) Information Resources. Several of the educational institutions differ at various levels: Number of entities, number of entity instances for an entity, and the amount of inter-dependence among entities and entity-instances. From a prospective student perspective, it is useful and important to know about (a) which university to choose; and (b) why. Prospective students need to know about the various strengths and weaknesses of a university, and more importantly, how these strengths and weaknesses compare across the other universities. Similarly, a funding agency would like to know about the various universities at a comparable level before taking a decision on funding. And so is the case with prospective faculty members who are looking at the various universities to build their academic career. 
       DESCRIPTION OF RELATED ART 
       [0007]    United States Patent Application 20100153324 titled “Providing Recommendations using Information Determined for Domains of Interest” by Downs; Oliver B.; (Redmond, Wash.); Sandoval; Michael; (Kirkland, Wash.); Branzan; Claudiu Alin; (Timisoara, RO); lovanov; Vlad Mircea; (Arad, RO); Khalsa; Sopurkh Singh; (Bellevue, Wash.) (filed on Dec. 11, 2009) describes techniques for determining and using information related to domains of interest, such as by automatically analyzing documents and other information related to a domain in order to automatically determine relationships between particular terms within the domain. 
         [0008]    United States Patent Application 20090214117 titled “Handwriting Symbol Recognition Accuracy using Speech Input” by Ma; Lei; (Beijing, CN); Shi; Yu; (Beijing, CN); Soong; Frank Kao-ping; (Warren, N.J.) (filed on Feb. 26, 2008 and assigned to Microsoft Corporation, One Microsoft Way, Redmond, Wash. 98052, US) describes an approach wherein handwriting data and speech data corresponding to mathematical symbols are received and processed (including being recognized) into respective graphs. A fusion mechanism uses the speech graph to enhance the handwriting graph, e.g., to better distinguish between similar handwritten symbols that are often misrecognized. 
         [0009]    United States Patent Application 20090324107 titled “Systems and Methods for Image Recognition using Graph-Based Pattern Matching” by Walch; Mark A.; (Woodbridge, Va.) (filed on Jun. 25, 2009 and assigned to Gannon Technologies Group, LLC McLean, VA) describes a method for creating a modeling structure for classifying objects in an image based on the graphs of the isolated objects. 
         [0010]    “Graph Comparison Using Fine Structure Analysis” by Macindoe; O. and Richards; W. (appeared in the Proceedings of IEEE SocCom10, #244, 2010) describes techniques for comparing two graphs by comparing earthmovers&#39; distances between sub-graphs within the comparable graphs. 
         [0011]    “Empirical Comparison of Algorithms for Network Community Detection” by Leskovec; Jure, Lang; Kevin, and Mahoney; Michael (appeared in the Proceedings of the ACM WWW International conference on World Wide Web (WWW), 2010) describes comparison of two graphs that represent a large network of communities (millions of nodes) wherein the nodes represent entities and the edges, the interactions between them. 
         [0012]    “Extension and Empirical Comparison of Graph-Kernels for the Analysis of Protein Active Sites” by Fober; Thomas, Mernberger; Marco, Melnikov; Vitalik, Moritz; Ralph, and Hullermeier; Eyke (appeared in the Proceedings of the Workshop “Knowledge Discovery, Data Mining and Machine Learning 2009”, September 2009) addresses a key problem in graph-based structure analysis of defining a measure of similarity that enables a meaningful comparison of such structures. 
         [0013]    The known systems do not address the issue of comparing multiple educational institutions based on a comprehensive modeling of these educational institutions at various levels in order to be able to compare at multiple levels. The present invention provides for a system and method for comparing universities based on their university model graphs. 
       SUMMARY OF THE INVENTION 
       [0014]    The primary objective of the invention is to achieve comparing of educational institutions at various levels based on a university model graph (UMG) associated with each of these educational institutions. 
         [0015]    One aspect of the present invention is to compare the educational institutions at UMG level. 
         [0016]    Another aspect of the invention is to compare the educational institutions at abstract node level wherein an abstract node of a UMG stands for an entity associated with an educational institution. 
         [0017]    Yet another aspect of the invention is to compare the educational institutions at node level wherein a node of a UMG stands for an entity instance of an entity associated with an educational institution. 
         [0018]    Another aspect of the invention is to compare the educational institutions at sub-graph level wherein a sub-graph is a set of entities and entity instances associated with an educational institution. 
         [0019]    Yet another aspect of the invention is to compare the educational institutions based on the base scores (also referred as assessments) associated with the corresponding UMGs. 
         [0020]    Another aspect of the invention is to compare the educational institutions based on the influence values associated with the corresponding UMGs. 
         [0021]    Yet another aspect of the invention is to normalize the models associated with multiple UMGs. 
         [0022]    Another aspect of the invention is to depict the comparison results based on a plot of assessment of the nodes associated with a UMG of an educational institution with respect to the various entities of the educational institution. 
         [0023]    Yet another aspect of the invention is to depict the comparison results based on a plot of influence value of the edges associated with a UMG of an educational institution with respect to the various entities of the educational institution. 
         [0024]    Another aspect of the invention is to depict the comparison results based on clustering of assessments of the various nodes associated with a UMG. 
         [0025]    Yet another aspect of the invention is to depict the comparison results based on clustering of influence values of the various nodes associated with a UMG. 
         [0026]    Another aspect of the invention is to depict the comparison results based on a plot of assessments with respect to the two educational institutions being compared. 
         [0027]    Yet another aspect of the invention is to depict the comparison results based on a plot of influence values with respect to the two educational institutions being compared. 
         [0028]    In a preferred embodiment the present invention provides a system for the comparison of a plurality of universities based on a plurality of university model graphs (UMGs) of said plurality of universities to generate a plurality of comparison results based on a plurality of assessments, a plurality of influence values, and a plurality of models contained in a plurality of university model graph databases associated with said plurality of university model graphs to help in the comparative analysis of said plurality of universities, 
         [0000]    a university of said plurality of universities having a plurality of entities and a plurality of entity-instances,
 
wherein each of said plurality of entity-instances is an instance of an entity of said plurality of entities, and a university model graph of said plurality of university model graphs associated with said university having a plurality of university models of said plurality of models, a plurality of abstract nodes, a plurality of nodes, a plurality of abstract edges, a plurality of semi-abstract edges, and a plurality of edges,
 
with each abstract node of said plurality of abstract nodes corresponding to an entity of said plurality of entities,
 
each node of said plurality of nodes corresponding to an entity-instance of said plurality of entity-instances, and
 
each abstract node of said plurality of abstract nodes is associated with a model of said plurality of university models, and
 
a node of said plurality of nodes is connected to an abstract node of said plurality of abstract nodes through an abstract edge of said plurality of abstract edges, wherein said node represents an instance of an entity associated with said abstract node and said node is associated with an instantiated model and an assessment, wherein said instantiated model is based on a model associated with said abstract node, and said assessment is computed based on said instantiated model and is a value between 0 and 1,
 
a source abstract node of said plurality of abstract nodes is connected to a destination abstract node of said plurality of abstract nodes by a directed abstract edge of said plurality of abstract edges and said directed abstract edge is associated with an entity influence value of said plurality of influence values, wherein said entity influence value is a value between −1 and +1;
 
a source node of said plurality of nodes is connected to a destination node of said plurality of nodes by a directed edge of said plurality of edges and said directed edge is associated with an influence value of said plurality influence values, wherein said influence value is a value between −1 and +1;
 
a source node of said plurality of nodes is connected to a destination abstract node of said plurality of abstract nodes by a directed semi-abstract edge of said plurality of semi-abstract edges and said directed semi-abstract edge is associated with an entity-instance-entity-influence value of said plurality influence values, wherein said entity-instance-entity-influence value is a value between −1 and +1; and
 
a source abstract node of said plurality of abstract nodes is connected to a destination node of said plurality of nodes by a directed semi-abstract edge of said plurality of semi-abstract edges and said directed semi-abstract edge is associated with an entity-entity-instance-influence value of said plurality influence values, wherein said entity-entity-instance-influence value is a value between −1 and +1,
 
said system comprising,
       means for normalizing of said plurality of models to result in a plurality of normalized models;   means for obtaining of said plurality of assessments and said plurality of influence values based on said plurality of normalized models;   means for comparing of said plurality of universities to generate a comparison result of said plurality of comparison results based on said plurality of university model graphs; and   means for displaying of said comparison result,   wherein said means for generating of said comparison result further comprises of:   means for comparing of said plurality of university model graphs at said plurality of universities level to determine said comparison result;   means for obtaining of an entity of a university of said plurality of universities;   means for comparing of said plurality of university model graphs at said entity level to determine said comparison result;   means for obtaining of an entity-instance of an entity of a university of said plurality of universities;   means for comparing of said plurality of university model graphs at said entity-instance level to determine said comparison result;   means for obtaining of a plurality of sub-graph elements, wherein a sub-graph element of said plurality of sub-graph elements is an entity of a university of said plurality of universities or an entity-instance of an entity of a university of said plurality of universities;   means for comparing of said plurality of university model graphs at said plurality of sub-graph elements level to determine said comparison result;   means for obtaining of a plurality of elements, wherein an element of said plurality of elements is an entity of a university of said plurality of universities or an entity-instance of an entity of a university of said plurality of universities;   means for comparing of said plurality of university model graphs based on said plurality of assessments and said plurality of elements to determine said comparison result; and   means for comparing of said plurality of university model graphs based on said plurality of influence values and said plurality of elements to determine said comparison result.       
 
       (REFER FIG. 1, FIG. 1A, FIG. 1B, FIG. 2, and FIG. 3) 
       [0000]    
       
         
 
       
     
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0044]      FIG. 1  provides an overview of EI Comparison System. 
           [0045]      FIG. 1A  provides an illustrative University Model Graph. 
           [0046]      FIG. 1B  provides the elements of University Model Graph. 
           [0047]      FIG. 2  provides a Partial List of Entities of a University. 
           [0048]      FIG. 3  provides various Kinds of Comparison of two educational institutions. 
           [0049]      FIG. 4  provides an Approach for Comparison at UMG level. 
           [0050]      FIG. 4A  provides additional information on Approach for Comparison at UMG level. 
           [0051]      FIG. 5  provides an Approach for Comparison at Abstract Node level. 
           [0052]      FIG. 6  provides an Approach for Comparison at Node level. 
           [0053]      FIG. 7  provides an Approach for Comparison at Sub-Graph level. 
           [0054]      FIG. 8  provides an Approach for Comparison based on Base Scores and Influence Values. 
           [0055]      FIG. 9  provides an Approach for Parametric Model Normalization. 
           [0056]      FIG. 9A  provides an Approach for Hierarchical Model Normalization. 
           [0057]      FIG. 9B  provides an Approach for Activity based Model Normalization. 
           [0058]      FIG. 10  provides an Approach for Depiction of Comparison Results. 
           [0059]      FIG. 10A  provides a second Approach for Depiction of Comparison Results. 
           [0060]      FIG. 10B  provides a third Approach for Depiction of Comparison Results. 
           [0061]      FIG. 10C  provides a fourth Approach for Depiction of Comparison Results. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0062]    The figures of the drawings illustrate the system and method steps of the present invention. The steps also indicate the provisions of respective means for the system functionalities. 
         [0063]      FIG. 1  provides an overview of EI Comparison System. The system ( 100 ) allows for comparison of two or more universities and the means for the overall system functionality is as follows: 
         [0000]    Means for obtaining of, say, two universities to be compared,
 
means for normalizing of the models associated with the two universities,
 
obtaining of the required level of comparison,
 
means for comparing of the two universities at the requested level, and
 
means for displaying of the comparison results.
 
         [0064]    The system takes a comparison request as input and generates comparison results based on the database comprising of UMG data for University  1  ( 110 ) and University  2  ( 120 ). Note that the system also is useful for comparing the multiple UMG snapshots of a single university to clearly bring out the progress of the university over a period of time. 
         [0065]      FIG. 1   a  depicts an illustrative University Model Graph.  140  describes UMG as consisting of two main components: Entity Graph ( 142 ) and Entity-Instance Graph ( 144 ). Entity graph consists of entities of the university as its nodes and an abstract edge ( 146 ) or abstract link is a directed edge that connects two entities of the entity graph. Note that edge and link are used interchangeably. The weight associated with this abstract edge is the influence factor or influence value indicating nature and quantum of influence of the source entity on the destination entity. Again, influence factor and influence value are used interchangeably. Similarly, the nodes in the entity-instance graph are the entity instances and the edge ( 148 ) or the link between two entity-instances is a directed edge and the weight associated with the edge indicates the nature and quantum of influence of the source entity-instance on the destination entity-instance. 
         [0066]      FIG. 1   b  provides the elements of a University Model Graph. The fundamental elements are nodes and edges. There are two kinds of nodes: Abstract nodes ( 160  and  162 ) and Nodes ( 164  and  166 ); There are three kinds of directed edges or links: Abstract links ( 168 ), links ( 170  and  172 ), and semi-abstract links ( 174  and  176 ). As part of the modeling, the abstract nodes are mapped onto entities and nodes are mapped onto the instances of the entities; Each node is associated with an entity-specific instantiated model and a node score that is a value between 0 and 1 is based on the entity-specific instantiated model; This score is called as Base Score; the weight associated with an abstract link corresponds to an entity influence value (EI-Value), the weight associated with a semi-abstract link corresponds to either an entity-entity-instance influence value (EIEI-Value) or an entity-instance-entity influence value (IEEI-Value), and finally, the weight associated with a link corresponds to an entity-instance influence value (I-Value). Note that edges and links are used interchangeably. Further, each entity is associated with a model and an instance of an entity is associated with a base score and an instantiated model, wherein the base score is computed based on the associated instantiated model and denotes the assessment of the entity instance. The weight associated with a directed edge indicates the nature and quantum of influence of the source node on the destination node and is a value between −1 and +1; This weight is called as Influence Factor. 
         [0067]      FIG. 2  depicts a partial list of entities of a university. Note that a deep domain analysis would uncover several more entities and also their relationship with the other entities ( 200 ). For example, RESEARCH STUDENT is a STUDENT who is a part of a DEPARTMENT and works with a FACULTY MEMBER in a LABORATORY using some EQUIPMENT, the DEPARTMENT LIBRARY, and the LIBRARY. 
         [0068]      FIG. 3  provides various Kinds of Comparison of two educational institutions. 
         [0069]    Means and the kinds of Comparisons of two UMGs-UMG 1  of EI 1  and UMG 2  of EI 2  ( 300 ):
   1. C 1 —Comparison at UMG level: Means for comparing of at universities level; In this case, the two UMGs are compared holistically to provide summarized comparison of the two corresponding EIs;   2. C 2 —Comparison at Abstract node level: Means for comparing at an entity level; Given an abstract node (or equivalently, an Entity), provide the summarized comparison at the entity level for the two corresponding EIs;   3. C 3 —Comparison at Node level: Means for comparing at an entity-instance level; Given a node (or equivalently, an entity-instance), provide the summarized comparison at entity-instance level for the two corresponding EIs;   4. C 4 —Comparison at Sub-Graph level: Means for comparing at a sub-graph (comprising of a set of sub-graph elements) level; Given a set of entities and entity-instances, compare the two sub-graphs from the two UMGs to provide detailed comparison for the two corresponding EIs;   5. C 5 —Means for comparison based on base scores (also referred as assessments); Comparison based only on the base score (also referred as assessment) of the nodes of the two UMGs; and   6. C 6 —Means for comparison based on influence values; Comparison based only on the influence value of the edges of the two UMGs.   
 
         [0076]      FIG. 4  provides an Approach for Comparison at UMG level. 
         [0077]    Means and an Approach for C 1 —Comparison at UMG Level ( 400 ): 
         [0000]    Step 1: Input: UMG 1  associated with EI 1  and UMG 2  associated with EI 2 ;
       Output: Result of Comparison;
 
Step 2: For each node N 1 J in UMG 1 , identify the corresponding node N 2 J in UMG 2 ;
 
Step 3: Compute the following with respect to N 1 J and N 2 J:
   BS 1 J and BS 2 J—the base scores (assessments);   InNI 1 J and InNI 2 J—the aggregate of the incoming negative influences;   OutNI 1 J and OutNI 2 J—the aggregate of the outgoing negative influences;   InPI 1 J and InPI 2 J—the aggregate of the incoming positive influences;   OutPI 1 J and OutPI 2 J—the aggregate of the outgoing positive influences;
 
Step 4:  410  depicts a node N 1 J of UMG 1  and  420  depicts a node N 2 J of UMG 2 ;
   As depicted in  430 , the assessment and influence values associated with N 1 J and N 2 J are combined.       
 
         [0085]      FIG. 4A  provides additional information on Approach for Comparison at UMG level. 
         [0086]    Means and an Approach for C 1 —Comparison at UMG Level (Contd.) ( 450 ): 
         [0000]    Step 41: Consider a CNODE with the following info:
       BS 1  and BS 2 —Consolidated base scores based on UMG 1  and UMG 2  respectively;   InNI 1  and InNI 2 —Consolidated values of UMG 1  and UMG 2  respectively;   OutNI 1  and OutNI 2 —Consolidated values of UMG 1  and UMG 2  respectively;   InPI 1  and InPI 2 —Consolidated values of UMG 1  and UMG 2  respectively;   OutPI 1  and OutPI 2 —Consolidated values of UMG 1  and UMG 2  respectively;   Add BS 1 J to BS 1  and BS 2 J to BS 2 ;   Add InNI 1 J to InNI 1  and InNI 2 J to InNI 2 ;   Add OutNI 1 J to OutNI 1  and OutNI 2 J to OutNI 2 ;   Add InPI 1 J to InPI 1  and InPI 2 J to InPI 2 ;   Add OutPI 1 J to OutPI 1  and OutPI 2 J to OutPI 2 ;     460  depicts the CNODE;
 
Step 5: Means for determining of non-matching nodes for comparison at university level;
   For each of non-matching node N 1 J of UMG 1 ,   Repeat Step 4 to create CNMNODE 1 ;   For each of non-matching node N 2 J of UMG 2 ,   Repeat Step 4 to create CNMNODE 2 ;     470  depicts CNMNODE 1  that is a consolidation of the nodes that are a part of UMG 1  but are missing in UMG 2 ;     480  depicts CNMNODE 2  that is a consolidation of the nodes that are a part of UMG 2  but are missing in UMG 1 ;       
 
       Step 6: Display CNODE, CNMNODE 1 , and CNMNODE 2 ; 
     Step 7: END. 
       [0104]      FIG. 5  provides an Approach for Comparison at Abstract Node level. 
         [0105]    Means and an Approach for C 2 —Comparison at Entity Level ( 500 ): 
         [0000]    Step 1: Input—An abstract node AN (Entity);
       Input—UMG 1  associated with EI 1  and UMG 2  associated with EI 2 ;   Output—Result of Comparison;
 
Approach: Compute E-5 Tuple with respect to UMG 1  and UMG 2 ;
 
Step 2: For each instance node of AN based on UMG 1 ,
   Determine BS, InNI, OutNI, InPI, and OutPI;       
 
         [0109]    Means for determining of top-ranked elements and consolidated abstract node (CAN); 
         [0000]    Step 3: Cluster BS associated with all the instances;
       Select the most populated cluster;   Determine the centroid of the most populated cluster;   Set the centroid as BS 1 ;   Similarly, cluster all InNI&#39;s and set the centroid of the most popular cluster as InNI 1 ;   Similarly, compute OutNI 1 , InPI 1 , and OutPI 1 ;
 
Step 4: For each instance node of AN based on UMG 2 ,
   Determine BS, InNI, OutNI, InPI, and OutPI;
 
Step 5: As in Step 3, cluster and compute BS 2 , InNI 2 , OutNI 2 , InPI 2 , and OutPI 2 ;
 
Step 6: Display CAN (Comparison of AN) ( 520 ) comprising
   BS 1 , InNI 1 , OutNI 1 , InPI 1 , and OutPI 1 , and   BS 2 , InNI 1 , OutnI 2 , InPI 2 , and OutpI 2 ;       
 
       Step 7: END. 
       [0118]      FIG. 6  provides an Approach for Comparison at Node level. 
         [0119]    Means and an Approach for C 3 —Comparison at Entity-Instance Level ( 600 ): 
         [0000]    Step 1: Input—A node N (Entity-Instance);
       Input—UMG 1  associated with EI 1  and UMG 2  associated with EI 2 ;   Output—Result of Comparison;
 
Approach: Compute EI-5-Tuple for N with respect to UMG 1  and UMG 2 ;
 
Step 2: Compute the set of incoming negative influence values of N of UMG 1 ;
       
 
         [0122]    Means for determining of top-ranked elements and consolidated node (CN); 
         [0000]    Step 3: Cluster the set and determine the centroid of the most populated cluster;
       Set the centroid cInNI 1 ;   Similarly, compute cOutNI 1  based on the set of Outgoing negative influence values;   And, compute cInPI 2  and cOutPI 1 ;   Obtain base score BS 1  of N;
 
Step 4: Repeat Steps 2 and 3 to compute cInNI 2 , cOutNI 2 , cInPI 2 , cOutPI 2 , and BS 2 ;
     620  depicts CN (Comparison of N) containing the various of the cluster centroids;
 
Step 5: Display the results based on CN;
       
 
       Step 6: END. 
       [0128]      FIG. 7  provides an Approach Comparison at Sub-Graph level. 
         [0129]    Means and an Approach for C 4 —Comparison at Sub-Graph Level ( 700 ): 
         [0000]    Step 1: Input—A Sub-Graph in terms of a set S of entities and entity-instances;
       Input—UMG 1  associated with EI 1  and UMG 2  associated with EI 2 ;   Output—Comparison Result;
 
Step 2: For each entity-instance N of S of UMG 1 ,
   Compute EI-5-Tuple;   For each entity of AN of S of UMG 1 ,   Compute E-5-Tuple;
 
Step 3: For each AN of S of UMG 1 ,
   Determine entity-instances that are an instance of AN;   Compute clustered centroid based cEi-5-Tuple based on the entity-instances;   Combine cEI-5-Tuple and E-5-Tuple to generate updated E-5-Tuple;   At this stage, there are entities with their updated 5-tuples;
 
Step 4: Combine the entities in a hierarchical manner and compute the updated 5-tuples;
   At this stage, there are distinct entities (that are not related hierarchically) with the updated 5-tuples;
 
Step 5: Repeat Steps 2 and 3 with respect to UMG 2 ;
 
Step 6: Display the results:
     720  and  740  depict a hierarchically combined entities (abstract nodes);   Note that each of these denote 5-tuples associated with the two UMGs under consideration;   On the other hand,  760  depicts an entity with 5-tuples that does not have a corresponding entity in UMG 2 . Similarly,  780  depicts an entity that does not have a corresponding entity in UMG 1 .       
 
       Step 7: END. 
       [0143]      FIG. 8  provides an Approach Comparison based on Base Scores and Influence Values. 
         [0144]    Means and an Approach for C 5 —Comparison Based on Base Scores and Influence Values ( 800 ):
   Step 1: Input—A Set S of entities/entity-instances; Note that S can be the set of all entities and entity-instances;
       Input—UMG 1  associated with EI 1  and UMG 2  associated with EI 2 ;   Output—Result of comparison;   
       Step 2: Determine the set of base scores SBS 1  based on S and UMG 1 ;
       Determine the set of I-values SIV 1  based on S and UMG 1 ;   As an illustration, I-value for an entity is computed as follows:   
       
 
         [0000]      (InPI+OutPI+InNI+OutNI)/(N1+N2+N3+N4);   Step 3: Cluster SBS 1  elements and rank the clusters based on their size;
       Cluster SIV 1  elements and rank the clusters based on their size;   
       Step 4: Repeat Steps 2 and 3 with respect to UMG 2 ;   Step 5: Display the comparison results based on a pre-defined top-ranked clusters:
         820  depicts three top-ranked clusters related to base scores: BS 11 , BS 12 , and BS 13  associated with UMG 1  and BS 21 , BS 22 , and BS 23  with UMG 2 .   Similarly,  840  is related to depicting of top-ranked clusters related to influence values: IV 11 , IV 12 , and IV 13  are associated with UMG 1  while IV 21 , IV 22 , and IV 23  with UMG 2 .   
       Step 6: END.     
         [0158]      FIG. 9  provides an Approach for Parametric Model Normalization. 
         [0159]    Means and an Approach for Model Normalization ( 900 ):
   1. There are three kinds of models: Parametric model, Hierarchical model, and Activity based model;   2. One of these three models is associated with every abstract node of UMG;   3. Model normalization is the process of equalizing the models of an abstract node of UMG 1  and the corresponding node of UMG 2 ;   4. The base scores (assessments) and Influence values are recomputed based on the normalized models to ensure that the comparisons are appropriate.   5. Consider Parametric model (PM):
       A PM consists of a set of parameters (SP);   Each parameter consists of a standard name (based on domain analysis) and a function;   It is assumed that as the parameter names are standard, the associated functions across UMGs are equivalent for the same parameter name;   
       
 
       Means and Steps Involved in PM Normalization: 
       [0168]    Step 1: Input—UMG 1  associated with EI 1  and UMG 2  associated with EI 2 ;
       Output—The normalized models of UMG 1  and UMG 2 ;
 
Step 2: Obtain a node/abstract node N 1  of UMG 1 ;
   Determine the corresponding node N 2  of UMG 2 ;
 
Step 3: Obtain PM 1  associated with N 1  and PM 2  associated with N 2 ;
 
Step 4: Let SP 1  be the set of parameters associated with PM 1 ; Similarly is SP 2 ;
 
Step 5: For each parameter P 1  of SP 1 ,
   Check if an equivalent parameter P 2  of SP 2  can be determined;   If Not, Remove P 1 ;
 
Step 6: Remove those parameters from SP 2  that did not match with any parameter of SP 1 ;
       
 
       Step 7: END. 
       [0173]      FIG. 9A  provides an Approach for Hierarchical Model Normalization. 
         [0174]    Means and an Approach for Model Normalization (Contd.) 
         [0175]    Means for Hierarchical Model Normalization ( 920 ):
   Step 1: Input—UMG 1  associated with EI 1  and UMG 2  associated with EI 2 ;
       Output—The normalized models of UMG 1  and UMG 2 ;   
       Step 2: Obtain a node/abstract node N 1  of UMG 1 ;
       Determine the corresponding node N 2  of UMG 2 ;   
       Step 3: Obtain HM 1  (a hierarchical model) associated with N 1  and HM 2  (a hierarchical model) associated with N 2 ;   Step 4: Let SN 1  be the set of nodes associated with HM 1 ; Similarly is SN 2 ;   Step 5: Obtain the root R 1  of HM 1 , and the root R 2  of HM 2 ;
       For each child node of HM 1 ,   Check if an equivalent child node of R 2  can be determined;   If Not, Remove the child node from HM 1 ;   
       Step 51: Remove those child nodes from R 2  that did not match with any of the child nodes of R 1 ;   Step 6: Repeat Step 5 for each of the non-root nodes of HM 1 ;   Step 7: For each of the leaf-nodes LN 1  of HM 1 ,
       Check if an equivalent leaf node of HM 2  can be determined;   If Not Remove LN 1  from HM 1 ;   If So,   Let LN 2  be the corresponding equivalent leaf-node of HM 2 ;   Determine PM 1  associated with LN 1  with SP 1  as the set of parameters;   Determine PM 2  associated with LN 2  with SP 2  as the set of parameters;   
       Step 71: For each parameter P 1  of SP 1 ,
       Check if an equivalent parameter P 2  of SP 2  can be determined;   If Not, Remove P 1 ;   
       Step 72: Remove those parameters from SP 2  that did not match with any parameter of SP 1 ;   Step 8: END.   
 
         [0200]      FIG. 9B  provides an Approach for Activity based Model Normalization. 
         [0201]    Means and an Approach for Model Normalization (Contd.) 
         [0202]    Means for Activity Based Model Normalization ( 940 ):
   Step 1: Input—UMG 1  associated with EI 1  and UMG 2  associated with EI 2 ;
       Output—The normalized models of UMG 1  and UMG 2 ;   
       Step 2: Obtain a node/abstract node N 1  of UMG 1 ;
       Determine the corresponding node N 2  of UMG 2 ;   
       Step 3: Obtain AM 1  (an activity based model) associated with N 1  and AM 2  (an activity based model) associated with N 2 ;   Step 4: Let SN 1  be the set of nodes associated with AM 1 ; Similarly is SN 2 ;   Step 5: Obtain the root R 1  of AM 1 , and the root R 2  of AM 2 ;
       For each child node of AM 1 ,   Check if an equivalent child node of R 2  can be determined;   If Not, Remove the child node from AM 1 ;   
       Step 51: Remove those child nodes from R 2  that did not match with any of the child nodes of R 1 ;   Step 6: Repeat Step 5 for each of the non-root nodes of AM 1 ;   Step 7: For each of the leaf-nodes LN 1  of AM 1 ,
       Check if an equivalent leaf node of AM 2  can be determined;   If Not Remove LN 1  from AM 1 ;   If So,   Let LN 2  be the corresponding equivalent leaf-node of AM 2 ;   Determine PM 1  associated with LN 1  with SP 1  as the set of parameters;   Determine PM 2  associated with LN 2  with SP 2  as the set of parameters;   
       Step 71: For each parameter P 1  of SP 1 ,
       Check if an equivalent parameter P 2  of SP 2  can be determined;   If Not, Remove P 1 ;   
       Step 72: Remove those parameters from SP 2  that did not match with any parameter of SP 1 ;   Step 8: END.   
 
         [0227]      FIG. 10  provides an Approach for Depiction of Comparison Results. 
         [0228]    The means and the display of comparison result is along two dimensions ( 1000 ): X-Axis corresponds to Entities and Y-Axis corresponds to Assessment (Base score) in one case and Influence Value in the other case. Note that assessments are a value between 0 and 1 while influence values are a value between −1 and +1. The results are shown for UMG 1  and UMG 2  separately, and  1005  depicts the variation in Assessment values for UMG 1  while  1010  shows the same for UMG 2  with respect to the various entities. Similarly,  1015  shows the variation in Influence Values with respect to the various entities for UMG 1  and  1020  for UMG 2 . 
         [0229]      FIG. 10A  provides a second Approach for Depiction of Comparison Results. 
         [0230]    The means and the display of comparison result involves the pair of values based on assessment and influence value with respect to the various entities ( 1030 ). The pairs are plotted with respect to UMG 1  and UMG 2 , and are clustered.  1035  shows an illustrative cluster while  1040  depicts a singleton for UMG 1 . Similarly,  1045  is an illustrative cluster and  1050  a singleton for UMG 2 . 
         [0231]      FIG. 10B  provides a third Approach for Depiction of Comparison Results. 
         [0232]    The means and the display of comparison result is along two dimensions ( 1060 ): X-Axis corresponds to UMG 1  while Y-Axis corresponds to UMG 2 . The assessment values for various are entities with respect to UMG 1  and UMG 2  are plotted. There four quadrants: Left-Bottom quadrant wherein the values close to (0,0) indicate that both UMG 1  and UMG 2  can improve greatly. Right-Top quadrant wherein the values close (1,1) depict that both UMG 1  and UMG 2  are best. The other two quadrants correspond to just one of the universities being best: Right-Bottom indicates that the UMG 1  is best while Left-Top indicates that the UMG 2  is best. 
         [0233]      FIG. 10C  provides a fourth Approach for Depiction of Comparison Results. 
         [0234]    The means and the display of comparison result is along two dimensions ( 1070 ): X-Axis corresponds to UMG 1  while Y-Axis corresponds to UMG 2 . The influence values for various are entities with respect to UMG 1  and UMG 2  are plotted. There four quadrants: Left-Bottom quadrant wherein the values close to (−1,−1) indicate that both UMG 1  and UMG 2  can improve greatly. Right-Top quadrant wherein the values close (1,1) depict that both UMG 1  and UMG 2  are best. The other two quadrants correspond to just one of the universities being best: Right-Bottom indicates that the UMG 1  is best while Left-Top indicates that the UMG 2  is best. 
         [0235]    Thus, a system and method for comparison of two or more universities based on their respective university model graphs is disclosed. Although the present invention has been described particularly with reference to the figures, it will be apparent to one of the ordinary skill in the art that the present invention may appear in any number of systems that provide for comparison based on influence based structural representation. It is further contemplated that many changes and modifications may be made by one of ordinary skill in the art without departing from the spirit and scope of the present invention.