Final presentation ewg-dss collab-net paper-thessaloniki
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Project EWG-DSS Collab-Net - A social Network Perspective of DSS-Research Collaboration in Europe.
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Final presentation ewg-dss collab-net paper-thessaloniki
- 1. EWG-DSS Collab-Net A Social Network Perspective of DSS-Research Collaboration in Europe EWG-DSS Collab-NetEWG-DSS Collab-Net -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 2. EWG-DSS Collab-NetEWG-DSS Collab-Net A Social Network Analysis for the EWG-DSS. An initiative of the EWG-DSS Coordination Board since 2008 (v.1)(v.1) A joint-development involving: ALLALL the EWG-DSS Group Members and External Researchers • Collaborators Version 2:Collaborators Version 2: Fátima Dargam, Rita Ribeiro, Pascale Zaraté, Isabelle Linden, Shaofeng Liu David Dardenne, Alexandre Rademaker • Collaborators Version 1:Collaborators Version 1: Fátima Dargam, Rita Ribeiro, Pascale Zaraté, Rahma Bouaziz, Tiago Simas, Shaofeng Liu -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe 2
- 3. Aims of the EWG-DSSAims of the EWG-DSS Encourage the exchange of information among Decision Systems researchers. Facilitate international cooperation research and projects. Promote the interest on Decision Systems in the scientific community by organizing dedicated workshops, seminars, mini-conferences, etc. Disseminate high quality research in DSS by editing Special and Contributed Issues in relevant Scientific Journals. Enforce the networking among its Members and among the DSS communities available. http://ewgdss.wordpress.com 3 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 4. over 170 Membersover 170 Members Ana Respício; Antonio Rodrigues; M.Eugênia Captivo;Ana Respício; Antonio Rodrigues; M.Eugênia Captivo; Tei Barnhart; Alexandre Gachet;Tei Barnhart; Alexandre Gachet; Antonio Martinez; Albert Angehrn; Alessio Ishizaka; Ana Maria Rosa Borges; Arijit BhattAntonio Martinez; Albert Angehrn; Alessio Ishizaka; Ana Maria Rosa Borges; Arijit Bhatt acharya; Barbo Back; Bertrand Mareschal; Boris Delibasic ; C. Makropoulos; Jacquesacharya; Barbo Back; Bertrand Mareschal; Boris Delibasic ; C. Makropoulos; Jacques Calmet; Carlos Antunes; Carlos Bana e Costa; Christer Carlsson; Asis Kr. ChattopadhCalmet; Carlos Antunes; Carlos Bana e Costa; Christer Carlsson; Asis Kr. Chattopadh yay; Caludia Loebbecke; Csaba Csaki; Dobrila Petrovic; Dorien De Tombe; Dirk Kenis;yay; Caludia Loebbecke; Csaba Csaki; Dobrila Petrovic; Dorien De Tombe; Dirk Kenis; Andreas Edelmayer ; Eduardo Natividade Jesus; Fatima Dargam; Fréféric Adam; ChristAndreas Edelmayer ; Eduardo Natividade Jesus; Fatima Dargam; Fréféric Adam; Christ ophe Fagot; Franck Tetard; Frits Claassen; Frieder Stolzenburg; Peter Gelleri; Gilles Coophe Fagot; Franck Tetard; Frits Claassen; Frieder Stolzenburg; Peter Gelleri; Gilles Co ppin; Inès Saad; Ilya Ashikhmin; Kwakkel Jan; J. Jassbi;Tawfik Jelassi; Jeremy Forth ; Joppin; Inès Saad; Ilya Ashikhmin; Kwakkel Jan; J. Jassbi;Tawfik Jelassi; Jeremy Forth ; Jo rge Souza; Joao Lourenco; Johannes Leitner; Jean Pierre Brans; Jochen Pfalzgraf; Jorgerge Souza; Joao Lourenco; Johannes Leitner; Jean Pierre Brans; Jochen Pfalzgraf; Jorge Pinho de Sousa; Jose Vincente Segura; Li Ching Ma; Lourdes Canos; Ladislav Lukas; MPinho de Sousa; Jose Vincente Segura; Li Ching Ma; Lourdes Canos; Ladislav Lukas; M arija Najika; Marko Bohannec; Philip Powel; Michael Bruhn Barfod; Miklos Biros; Mikaelarija Najika; Marko Bohannec; Philip Powel; Michael Bruhn Barfod; Miklos Biros; Mikael Mihalevich; Jose Maria Moreno Jimenez; Maria Theiner; Natalio Krasnogor; Nikolaos F.Mihalevich; Jose Maria Moreno Jimenez; Maria Theiner; Natalio Krasnogor; Nikolaos F. Matsatsinis; Olaf Herden; Paul Hasenohr; Paulo Leonco; Peter Keenan; Philippe Lenca;Matsatsinis; Olaf Herden; Paul Hasenohr; Paulo Leonco; Peter Keenan; Philippe Lenca; Pierre Kunsch; Suzanne Pinson; Jean Charles Pomerol; Paulo Ramos; Rita Ribeiro; CaroPierre Kunsch; Suzanne Pinson; Jean Charles Pomerol; Paulo Ramos; Rita Ribeiro; Caro line Rieder; Rudolf Vetschera; Camille Rosenthal Sabroux; Susanne Stadler; Frantisekline Rieder; Rudolf Vetschera; Camille Rosenthal Sabroux; Susanne Stadler; Frantisek Sudzina; Maria Theiner; Thomas Soboll; Alexis Tsoukias; W. Walker; Yi Yang; PascaleSudzina; Maria Theiner; Thomas Soboll; Alexis Tsoukias; W. Walker; Yi Yang; Pascale Zaraté; Shaofeng Liu; Jason Papathanasiou; Jorge Hernández; J. Clímaco; Dragana BecZaraté; Shaofeng Liu; Jason Papathanasiou; Jorge Hernández; J. Clímaco; Dragana Bec 4 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 5. Main Topics of Research within theMain Topics of Research within the GroupGroup • Collaborative Decision Making (CDM)Collaborative Decision Making (CDM) • GDSS: Group-DM; NDSS: Negotiation-DM;GDSS: Group-DM; NDSS: Negotiation-DM; • Distributed Models of Decision MakingDistributed Models of Decision Making • Applications in Collaborative Decision Making & AnalysisApplications in Collaborative Decision Making & Analysis • Models for Decision Analysis (DA) in Group Decision MakingModels for Decision Analysis (DA) in Group Decision Making - Evaluation of the Collaboration Levels for DM & DA- Evaluation of the Collaboration Levels for DM & DA - Facilitation of Group Coordination and Group Communication- Facilitation of Group Coordination and Group Communication • Knowledge Management & Context supporting Decision MakingKnowledge Management & Context supporting Decision Making • Knowledge Management as a Collaboration ModelKnowledge Management as a Collaboration Model - Knowledge-intensive Collaborative Models- Knowledge-intensive Collaborative Models - Context-based Decision Systems- Context-based Decision Systems • Network & Web-based SystemsNetwork & Web-based Systems • Network-based Collaborative Decision MakingNetwork-based Collaborative Decision Making • New Methodologies and Technology for GDSSNew Methodologies and Technology for GDSS • Aggregation & Fuzzy Algorithms for Decision MakingAggregation & Fuzzy Algorithms for Decision Making • Knowledge-Based (Intelligent) Decision SystemsKnowledge-Based (Intelligent) Decision Systems • Applied Decision Support Systems (including MIS)Applied Decision Support Systems (including MIS) 5 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 6. A SocialA Social NetworkNetwork AnalysisAnalysis for EWG-DSSfor EWG-DSS Motivation / Objectives: Evaluate the group’s collaboration dynamics since its foundation (1989). Encourage new research and promote further collaboration among its members in common projects and joint- publications. 6 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 7. NodesNodes: authors; publications; projects; research areas Ties / Relations:Ties / Relations: collaborations; joint-projects; Journal-editions; ... • Distances among the members of the group. • Major and minor areas of research concentration & interaction in the group. • New tendencies & working areas. • New opportunities for cooperation. EWG-DSS Collab-Net V.1EWG-DSS Collab-Net V.1 7 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 8. Version 1 MethodologyVersion 1 Methodology Weighted Graphs Methods Software Frameworks: NWB Network Workbench PAJEK Network 8 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 9. The EWG-DSS Network V.1.0 : Input from 70 EWG-DSS members; Period of 19 years [1989 – 2008]; 1350 Publications; 34 extracted Topics of Research Areas EWG-DSS Collab-Net V.1EWG-DSS Collab-Net V.1 9 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 10. 70 Authors / Members of the EWG-DSS70 Authors / Members of the EWG-DSS Ai Author Name Ai Author Name Ai Author Name A1 Arijit Bhattacharya A25 Fréféric Adam A49 Pascale Zaraté A2 Adla Abdelkader A26 Frieder Stolzenburg A50 Peter Gelleri A3 Albert A. Angehrn A27 Frits Claassen A51 Peter Keenan A4 Alessio Ishizaka A28 Ilya Ashikhmin A52 Philip Powel A5 Alexis Tsoukias A29 Inès Saad A53 Philippe Lenca A6 Ana Respício A30 J.Jassbi A54 Pierre Kunsch A7 Antonio Jimenez Martinez A31 Jacques Calmet A55 Rita Ribeiro A8 Asis Kr. Chattopadhyay A32 Jean Charles Pomerol A56 Rudolf Vetschera A9 Bertrand Mareschal A33 Jean Pierre Brans A57 Sanja Petrovic A10 Bojan Srdjevic A34 João Carlos Lourenço A58 Suzanne Pinson A11 Boris Delibasic A35 Jochen Pfalzgraf A59 Tawfik Jelassi A12 Caludia Loebbecke A36 Johannes Leitner A60 Thanasis Spyridakos A13 Camille Rosenthal Sabroux A37 Jorge Freire de Sousa A61 Yi Yang A14 Carlos Antunes A38 Jorge Pinho de Sousa A62 Thomas Soboll A15 Carlos Bana e Costa A39 Jose Maria Moreno Jimenez A63 BAZZANA Flavio A16 Christer Carlsson A40 Ladislav Lukas A64 Guilan Kong A17 Csaba Csaki A41 Li Ching Ma A65 Jason Papathanasiou A18 Dirk Kenis A42 Luís Cândido Dias A66 Mikael Mihalevich A19 Dobrila Petrovic A43 Marko Bohannec A67 Taghezout Noria A20 Dorien De Tombe A44 Michael Bruhn Barfod A68 Warren Elliott Walker A21 Eduardo Manuel Natividade Jesus A45 Miklos Biros A69 José Vicente segura Heras A22 Fatima Dargam A46 Natalio Krasnogor A70 Antonio Rodrigues A23 Franck Tetard A47 Nguyen Dinh Pham A24 Frantisek Sudzina A48 Olaf Herden 10
- 11. # Research Topic # Research Topic 1 Business Models 18 Knowledge Management 2 Collaboration Dynamics 19 Multi-Agent Systems 3 Cooperative Decision Support Systems 20 Multiple Criteria Decision Aiding 4 Decision Analysis 21 Management Learning and Decision Making 5 Decision Aiding Process 22 Network 6 Data Mining 23 Operations research 7 Decision Support Systems 24 Preference analysis 8 Evaluation 25 Performance Evaluation 9 E-Business 26 Preference Modelling 10 Entreprise resource Planning 27 Production Planning and Scheduling 11 Expert Systems 28 Supply Chain Management 12 Economic Theory 29 Sustainable Development 13 Fuzzy Sets 30 Social Networks 14 Group Decision and Negotiation 31 Simulation Systems 15 Information Retrieval 32 Systems Software Evaluation and Selection 16 Information Systems 33 Virtual Communities 17 Information and Telecommunication Technology 34 Context 34 Topics of Research34 Topics of Research extracted from the 1350 Publicationsextracted from the 1350 Publications:: 11 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 12. Visualization of the Publ_AP network, graphically represented in PAJEK. Visualization related by the collaboration in publications. The graph represents how the authors’ nodes are connected among themselves, with relation to their publication- collaboration. EWG-DSS Collab-Net V.1EWG-DSS Collab-Net V.1 12 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 13. Visualization of the Publ_PT represented in PAJEK, with separate components, showing the relationships among the publications and topics of research 1350 Publications 34 Topics EWG-DSS Collab-Net V.1EWG-DSS Collab-Net V.1 13 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 14. NWB Radial Graph Visualization of the Authors_AT network, showing the collaboration among the authors, with relation to their common research topics. A65 - Jason Papathanasiou A9 - Bertrand Mareschal EWG-DSS Collab-Net V.1EWG-DSS Collab-Net V.1 14 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 15. The collaboration relationships have shown: 1. How the members relate to each other in terms of topics of research; 2. What are the most relevant topics of research within in the group; 3. The relevant statistical data concerning our publications. EWG-DSS Collab-Net V.1EWG-DSS Collab-Net V.1 15 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 16. Further analysis of this EWG-DSS Collab-Net Version 1 project was also developed as a case-study of a Master Thesis (Dardenne, 2012) from David Dardenne, supervised by Prof. Isabelle Linden from FUNDP in Belgium, in cooperation with the EWG-DSS. EWG-DSS Collab-Net V.1EWG-DSS Collab-Net V.1 16 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 17. Dardenne’s Master ThesisDardenne’s Master Thesis (Dardenne, 2012)(Dardenne, 2012) In Dardenne’s study, the usual measures on the graph and on its nodes, as well as the measures of centrality and applications of communities detection methods were used to respond to questions like: “Which authors were the most collaborative?”; “Among the several connected components, were there some communities found?; and “Was there any concentration of authors in the network?”. 17 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 18. EWG-DSS – Collab-Net (Dardenne, 2012) Intermediate ResultsIntermediate Results Methods used: • Measures on the graph and on its nodes • Applications of communities detection methods • Measures of centrality EUROXXV-Vilnius2012 Network R(A,P) Authors x Publications Relation A_AP Authors x Authors Implementation with NodeXL Expl. a publication with 3 authors, creation of 3 relations: • Author 1 x Author 2 • Author 1 x Author 3 • Author 2 x Author 3 When 1 single author creation of 1 relation • Author x Author 18
- 19. EWG-DSS – Collab-Net (Dardenne, 2012) (a) Measures on the graph and nodesnodes Network Software used: Template NodeXL There are 73 connected components but 32 of them are single-vertex connected components; it means connected components with only one vertex There are 782 authors There are 3201 interactions between authors; interactions mean common publication for two authors One of the connected components counts 2505 edges; so, we can conclude that there is a big connected component. In this connected components, 527 authors are implicated 239 publications have been written by only one author EUROXXV-Vilnius2012 19
- 20. EWG-DSS – Collab-Net (Dardenne, 2012) (b) CommunitiesCommunities detection Software used: Template NodeXL Graph: A(AP) Grouped by connected components Layout algorithm: Harel-Koren Fast Multiscale Type: undirected The connected component that counts 2505 edges EUROXXV-Vilnius2012 20
- 21. EWG-DSS – Collab-Net (Dardenne, 2012) (b) ApplicationsApplications of communities detection methods Software used: Template NodeXL Graph: A(AP) Grouped by clustering method GNM Layout algorithm: Harel-Koren Fast Multiscale Type: undirected EUROXXV-Vilnius2012 21
- 22. EWG-DSS – Collab-Net (Dardenne, 2012) (c) Measures of centrality(c) Measures of centrality • Degree centrality NodeXL doesn’t give the degree centrality but it gives a global information on the graph about the degree distribution and gives the degree for each vertex. Here is the top 20 of the authors according to their degree level. Software used: Template NodeXL Graph: A(AP) Control panel: “Graph metrics” ribbon -> calculate metrics Type: undirected EUROXXV-Vilnius2012 22
- 23. EWG-DSS – Collab-Net (Dardenne, 2012) (c) Measures of centrality(c) Measures of centrality • Betweeness centrality Here is the top 20 of the authors according to their betweeness centrality level. Software used: Template NodeXL Graph: A(AP) Control panel: “Graph metrics” ribbon -> calculate metrics Type: undirected EUROXXV-Vilnius2012 23
- 24. EWG-DSS – Collab-Net (Dardenne, 2012) (c) Measures of centrality(c) Measures of centrality • Eigenvector centrality Here is the top 20 of the authors according to their eigenvector centrality level. Software used: Template NodeXL Graph: A(AP) Control panel: “Graph metrics” ribbon -> calculate metrics Type: undirected EUROXXV-Vilnius2012 Cev (i) = 1 λ Aij Cev ( j) j ∑ 24
- 25. Dardenne’s Master ThesisDardenne’s Master Thesis (Dardenne, 2012)(Dardenne, 2012) Dardenne has introduced in his study the relation A x A (Authors x Authors), in which authors are linked by their common publications. This way, the represented network could identify 782782 authors out of the original 7070 authors and members of the EWG-DSS, who contributed with the 1350 publications to start up this project. Dardenne’s analysis has brought us one step furtherDardenne’s analysis has brought us one step further on reaching our main goal.on reaching our main goal. 25 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 26. EWG-DSS-Collab-Net V.2 extends the original implementation in many ways. It will consider: 1) a hybrid (manual and automatic) methodology of input data collection,using also web mining of electronic databases to automatically detect relationships of members; 2) a refined model of the publication relationship structure, taking into account “author-title-journal/conference-multiple keywords-multiple topics”; 3) as well as a more refined model of the collaboration relationship structure, which includes workshop/conference publications, informal work meetings, event co-organisations, scientific committees/boards, book/journal editorials, etc. EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 26 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 27. EWG-DSS-Collab-Net V.2 collaboration analysis: co-authorships and co-citations to further illustrate the dynamics of EWG-DSS publications overtime. The analysis features, among other characteristics: (a) the number and percentage of multi-author papers and co-authors in comparison with single-author papers; (b) number and percentage of co-citations; (c) identification of publications that are closely related to a given topic, as well as the authors involved. EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 27 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 28. EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 28 Data Input EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 ImplementationImplementation Data Validation Data Structure Model Network Repository Network Analysis Visualisation Web-Interface Dissemination -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 29. EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 hybrid methodology of input data collection (manual and automatic), including web mining of publications electronic databases: DBLP Computer Science Bibliography; Academic Google; Google Scholar; Microsoft Academic Search; Private Publications URL; etc… 29 Data Input -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 30. EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 Data Validation will take into account the various scripts and crawlers codes to capture and filter the relevant input information from the chosen input web-environments. It will also cater for the validation of the publications input data (including knowledge areas, keywords) and authors’ information, as well as for its normalization. 30 Data Validation -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 31. EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 31 Data InputData Input Data ValidationData Validation : Scripts and Crawlers capture and filter Input Information EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 Data Structure Model - Network RepositoryData Structure Model - Network Repository 30 % Authors 20 % Authors 20 % Authors 20 % Authors 5 % Authors 5 % Authors 100 % Authors’ Publications validated and normalized -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 32. EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 32 Considerations about the structure of the data Ontologies Which ready-made ontologies to adopt? bibobibo (Bibliographic Ontology Specification)(Bibliographic Ontology Specification) ;; foaffoaf (FOAF Vocabulary Specification)(FOAF Vocabulary Specification);; owlowl (OWL Web Ontology Language )(OWL Web Ontology Language );; skosskos (SKOS Core Vocabulary Specification)(SKOS Core Vocabulary Specification);; …… Which Network Structure shall we use? Why? Data Structure Model Network Repository -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 33. Data ModelData Model 33 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 34. EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 34 Social Network Environments for Version 2 (Analysis Metrics - relevant for our NetworkAnalysis Metrics - relevant for our Network) Table with the comparison of the Analysis Functions of the Network Environments used in the EWG-DSS Collab-Net up to now. Source: (Dardenne, 2012) Network Analysis Visualisation -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 35. EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 35 Network Visualisation for users (EWG-DSS members) Table with the comparison of the Visualisation Functions of the Network Environments used in the EWG-DSS Collab-Net up to now. Source: (Dardenne, 2012) Web-Interface Dissemination -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 36. Concluding RemarksConcluding Remarks What we still have to do: • Include missing input data (up to the current date); • Encourage the isolated nodes of absent connections to become, at a first stage, nodes of “weak connections” within the net; • Reduce / eliminate the isolated nodes; • Make it available on the Internet for the use of the EWG-DSS Members; • Bring the external collaborators (co-authors) to the EWG-DSS. 36 EWG-DSS Collab-Net V.2EWG-DSS Collab-Net V.2 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 37. What do we need?What do we need? To proceed we need YOUR support!To proceed we need YOUR support! • ParticipationParticipation • Data: Research production in joint-workData: Research production in joint-work • Support with development forceSupport with development force • FeedbackFeedback 37 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
- 38. Thanks for your attention! 38 -DSS Thessaloniki-2013 A Social Network Perspective of DSS-Research Collaboration in Europe
Editor's Notes
- Since 2008 the EWG-DSS Coordination Board has been conducting a study about research interests of the group members, with the intention to draw a knowledge map on Decision Systems within the group. This paper intends to present the status of our study on one hand, and also to request the EWG-DSS members’ feedback for further collaboration on the other, with the confidence that this project will continue encouraging new research and academic cooperation within the DSS community. This presentation revisits the work done on the analysis of the social-academic network, created for the EURO Working Group on Decision Support Systems (EWG-DSS), in order to represent the various relationships that link academically its 140 members. We show how the group’s collaboration dynamics have evolved since its foundation in 1989, up to the present moment: a period of over 20 years. This project revisits the current areas of research of the professionals involved, as a by-product. Moreover, the social-academic network has already shown to encourage new research and promote further collaboration among the academic members of the group in common projects and joint-publications. In this work further network analysis is presented, so that some concluding factors about the group could be better explained.
- Since 2008 the EWG-DSS Coordination Board has been conducting a study about research interests of the group members, with the intention to draw a knowledge map on Decision Systems within the group. This paper intends to present the status of our study on one hand, and also to request the EWG-DSS members’ feedback for further collaboration on the other, with the confidence that this project will continue encouraging new research and academic cooperation within the DSS community. This presentation revisits the work done on the analysis of the social-academic network, created for the EURO Working Group on Decision Support Systems (EWG-DSS), in order to represent the various relationships that link academically its 140 members. We show how the group’s collaboration dynamics have evolved since its foundation in 1989, up to the present moment: a period of over 20 years. This project revisits the current areas of research of the professionals involved, as a by-product. Moreover, the social-academic network has already shown to encourage new research and promote further collaboration among the academic members of the group in common projects and joint-publications. In this work further network analysis is presented, so that some concluding factors about the group could be better explained.
- The EWG-DSS is a Working Group on Decision Support Systems within EURO, the Association of the European Operational Research Societies. The main purpose of the EWG-DSS is to establish a platform for encouraging state-of-the-art high quality research and collaboration work within the DSS community. Other aims of the EWG-DSS are to: Encourage the exchange of information among practitioners, end-users, and researchers in the area of Decision Systems. Enforce the networking among the DSS communities available and facilitate activities that are essential for the start-up of international cooperation research and projects. Facilitate professional academic and industrial opportunities for its members. Favour the development of innovative models, methods and tools in the field Decision Support and related areas. Actively promote the interest on Decision Systems in the scientific community by organizing dedicated workshops, seminars, mini-conferences and conference streams in major conferences, as well as editing special and contributed issues in relevant scientific journals.
- More than 140 Members in 2011. many of us working on Decision Making since 1989. The number of EWG-DSS members has substantially grown along the years. Now we are over 140 members coming from various nationalities. There has also been established quiet a few well-qualified research co-operations within the group members, which have generated valuable contributions to the DSS field in journal publications. Since its creation, the EWG-DSS has held annual Meetings in various European countries, and has taken active part in the EURO Conferences on decision-making related subjects.
- Since 2008 the EWG-DSS Coordination Board has been conducting a study about research interests of the group members, with the intention to draw a knowledge map on Decision Systems within the group. This project targets mainly to disseminate the research work of the EWG-DSS members. As a primary step, a social structure for the group was developed, using the group members as its defined population. The output of this step devised an academic-social network analysis, which identified the collaboration relationship that exists among the group members, as well as how the group’s dynamics has evolved since its foundation in 1989. It also revisited the current areas of research of the professionals involved, as a by-product.
- Social networks are usually implemented to represent different real-life communities and play an important role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals . The power of social network analysis stems from its difference from traditional social scientific studies. Such analysis produces an alternate view, which focuses on how the structure of ties affects individuals (persons, organizations, states) and their relationships, rather than treating them as discrete units of analysis. In other words, the attributes of individuals are less important than their relationships and ties with other actors within the network. The current work focuses on the research collaboration of the group members within the central area of Decision Support Systems and its co-related topics. The main motivation for analyzing the EURO Working Group on DSS is to represent and analyse the various relationships that academically link over 105 members. For this purpose we developed an academic collaborative network to enable us to evaluate the group’s collaboration dynamics since its foundation in 1989, up to the present moment. As a by-product, we hope to promote further collaboration among the academic-members of the group in common projects and joint-publications. Further, this paper contributes to the evaluation of social informal relationships among the members of the group.
- For the acquisition of the academic production used in the network, all the members of the EWG-DSS group were requested by the coordination members to submit to this project the relevant information, concerning their list of main publications since 1989, stating for each of them the authors names, title, journal/conference/..., editors, year of publication, as well as its main area(s) of research. Another important requested information for the network, was the indication of research cooperation with some other member(s) of the EWG-DSS group, if any. As a result, 70 members have replied to us with their corresponding data. From the information received, a total of 1350 publications were taken into consideration for this case-study. The publications involved at least one of the 70 authors, members of the EWG-DSS. Only international publications of the EWG-DSS members were considered. Outside collaborators of the publications, not members of the EWG-DSS, were not included in the network. The list of 1350 publications was compiled into an Excel file, for further being used in the network pre-formatting process, described in the sequel.
- The Social-Academic Network Building Methodology Our network comprises the data gathered from the EWG-DSS members, from 1989 (group foundation) until 2008. To construct and analyze the social academic network, five main steps were carried out: 1. Acquisition Process: Collecting the input data in a matrix format (MS-Excel files), which could relate authors and their papers, as well as the papers classified into topics; 2. Extraction Process: Creating the input files with nodes and labels to enable them to be manipulated by the network tools: PAJEK and NWB; 3. Transformation Process: Using Jaccard similarity measure, [Rocha et al 2005] we constructed a set of weighted networks by combining matrices including authors, publications and research topics; 4. Weighted Network graphical analysis: using PAJEK and NBW graphical tools we analyze the main characteristics of the EWG-DSS group; and 5. Weighted Network statistics: using PAJEK and NBW statistical tools we discuss the main aspects of the EWG-DSS academic network.
- After identifying the relevant key-information from the members, illustrated in Table 3, and determining the relationship matrices among authors x papers and papers x topics, as shown in Table 2 and Table 4, we have composed the initial matrices that were needed to construct the specified weighted networks. For accomplishing this step, we have used a simple matrix multiplication process to combine the information of both Boolean matrices including the authors and their publications and their respective research topics, S(P,T) and R(A,P), via their input weighted networks. The set of weighted networks were created using input graphs for the network simulation tools, by applying the extended Jaccard similarity measure, as suggested in [Rocha et al 2005]. This process was implemented for both relations R(A,P), (Authors x Publications) and S(P,T), (Publications x Topics). More details about this implementation can be found in [Dargam, Ribeiro, Zaraté, 2010]. Nevertheless, we reproduce in Appendix B the graphics which illustrate the input matrixes and their respective outputs (see Graphic 1, Graphic 2 and Graphic 3 in Appendix B). Graphical Representation and Analysis of the Network The output networks files “Authors_AP”; Publ_AP; Publ_PT; Authors_AT” and Topics_AT, obtained via the description of Appendix B and also detailed in [Dargam, Ribeiro, Zaraté, 2010], were used to produce the graphical representations of the EWG-DSS social academic network. By constructing the set of weighted networks for the EWG-DSS group, we were able to visualize and analyze them with the chosen network frameworks PAJEK [PAJEK] and NWB [NWB]. The PAJEK framework is from design dedicated to large network analysis, whereas the NWB Network Workbench is a framework for pre-processing, modelling and analysing small networks. Both of them are MS-Windows-based programs designed for network analysis and visualization. In the sequel of this section, we describe some relevant statistic and metric analysis, which were obtained via the adopted network frameworks: NWB Network Workbench and Pajek Network Tool.
- In Figure 4 below, we observe the same publications network Publ_AP, now visualized by the network framework PAJEK. In this graphical representation, although still difficult to be analysed, it is now possible to better visualize the various existing clusters of publications that are somehow related to each other via joint-cooperations of groups of authors, working within specific research areas. The layouts used in both Figures 4 and 5, within the PAJEK network framework, are both based on Kamada-Kawai visualizations of the Publ_AP network. In Figure 4, the Energy Kamada-Kawai algorithm is applied with the parameter “Fix one in the middle”, whereas in Figure 5, the parameter used was “Separate Components”, as it can be clearly observed. Even though in both graph representations, we have activated the option “no labels”, so that more of the shape of the graph could be printed without showing the 1350 publication labels, we have to admit that analyzing such a number of nodes in one only network becomes a hard task, whose main difficulty resides on the visualization of the proper characteristics for taking the relevant conclusions.
- In Figure 6, we can identify the clusters of publications, relative to the topics listed in Table 3. In this visualization of the Publ_PT network, it is clearly seen that almost 25% of the topics, relative to 8 larger sub-nets, concentrate the great majority of papers published among the EWG-DSS group members. The most used topics in publications, represented by the six uppermost clusters of Figure 6, refer to the papers of the applications areas of “Decision Support Systems” “Operations Research”, “Information and Technology”, “Expert Systems”, “Knowledge Management” and “Multiple Criteria Decision Making”, which seem to be the most exploited areas among the members of EWG-DSS, from the 34 topics listed in Table 3 of Appendix A. These results are corroborated in Figure 7, where the number of publications for each topic is depicted within a Histogram. Another interesting observation can also be made about the upper left two clusters since they are linked only by one paper, which corresponds to paper 149 and paper 240, which were authored by A25 (Frederic Adam) and A5 (Alexis Tsoukias). These two authors are quite known in the group by focusing their research mainly in decision support systems (A25 in topic T7) and operations research (A5 in topic T23). The other representations of separate components shown in Figure 6, refer to the smaller clusters of publications which relate to the other topics of interest within the EWG-DSS. Comments from Isabelle Linden: Regarding slide 13, - about the 2 "strange" points in the top left clusters. They come from two publications that (for a reason that I ignore) received two topics in version 1... while others received only one... This is the reason why they act as "bridges" between two clusters each one related to one topic... - Splitting those two groups in 2 provides... 35 clusters, for 34 topics! Indeed one of the paper is associated with no topic... -- I'm not sure that these observations are relevant research results... they are more likely incoding errors...
- In the representation of Figure 8, a Radial Graph visualization of the network Authors_AT produced using the NWB framework, we can visualize the 70 authors considered and the way they are interconnected to each other with relation to their main topics of research, taking two arbitrarily authors as central nodes represented by A9 (Bertrand Mareschal) and A65 (Jason Papathanasiou). It is relevant to notice that the darker connections, represented in the foreground, express the stronger connections among the authors and the nodes in focus. Using the NWB tool and the radial graph visualization option, it is very easy to dynamically change the nodes to be focused and in this way, analyze all the individual cases among all the authors, to get the exact picture of their cooperation relative to the many different topics within their areas of interest around Decision Making. Figure 8 illustrates just one single case analysis, also sometimes referred to as egonet, which shows the connection of two clusters via the nodes A65 (Jason Papathanasiou) and A9 (Bertrand Mareschal), stating the importance of weak ties, in this case represented by one single collaboration between the two stated authors, bridging two different areas of research within the network.
- The output of this step devised an academic-social network analysis, which identified the collaboration relationship that exists among the group members, as well as how the group’s dynamics has evolved since its foundation in 1989. It also revisited the current areas of research of the professionals involved, as a by-product. Preliminary results of this project were presented in the DSS Streams of the 2009 and 2010 EURO K Conferences in Bonn and Lisbon, respectively. Since the publication of those results, the EWG-DSS academic-social network analysis has already shown to encourage new research and promote further collaboration among the group members in common projects and joint-publications. The metrics of the network graphical representations helped us to build up a consistent basis for analyzing the network graphs that were generated via the input data available. As already well known from the literature, the basic characteristics of a social network are described by the following parameters: 1. Number of nodes; 2. Number of isolated nodes; 3. List of nodes attributes; 4. Number of edges (connections); 5. List of edges’ attributes; 6. Density of the graph; 7. Type of the graph (directed / not directed); 8. Type of the connections of the graph (weakly connected or not); 9. Number of weakly connected components (nodes); and 10. Number of nodes in the largest connected components. Based on the study conducted by Granovetter in the earlier developments of social networks [Granovetter, 1973], we believe that there is great potential for the weak connections of our EWG-DSS network still to be able to develop into strong ones. In Granovetter’s study, he has explained that information was far more likely to be “diffused” through weaker ties, than through already strong connections. We shall, however, be concerned with the absent ties, i.e. connections that are beyond the concept of weak ties. Although those connections are of no relevance to the network as a whole, they should be encouraged to become “weak ties”, in order to interact and gain importance within the network. In the particular case of the EWG-DSS network, absent connections are represented within the network files, by for example authors who exist in the network, are publishing actively within their areas of research, also with collaborators outside the scope of the group, nevertheless do not interact with other members of the EWG-DSS group, via joint collaborative research work. This issue was little exploited in this paper, but it is part of the project’s main objectives and will be worked out in the future.
- The output of this step devised an academic-social network analysis, which identified the collaboration relationship that exists among the group members, as well as how the group’s dynamics has evolved since its foundation in 1989. It also revisited the current areas of research of the professionals involved, as a by-product. Preliminary results of this project were presented in the DSS Streams of the 2009 and 2010 EURO K Conferences in Bonn and Lisbon, respectively. Since the publication of those results, the EWG-DSS academic-social network analysis has already shown to encourage new research and promote further collaboration among the group members in common projects and joint-publications. The metrics of the network graphical representations helped us to build up a consistent basis for analyzing the network graphs that were generated via the input data available. As already well known from the literature, the basic characteristics of a social network are described by the following parameters: 1. Number of nodes; 2. Number of isolated nodes; 3. List of nodes attributes; 4. Number of edges (connections); 5. List of edges’ attributes; 6. Density of the graph; 7. Type of the graph (directed / not directed); 8. Type of the connections of the graph (weakly connected or not); 9. Number of weakly connected components (nodes); and 10. Number of nodes in the largest connected components. Based on the study conducted by Granovetter in the earlier developments of social networks [Granovetter, 1973], we believe that there is great potential for the weak connections of our EWG-DSS network still to be able to develop into strong ones. In Granovetter’s study, he has explained that information was far more likely to be “diffused” through weaker ties, than through already strong connections. We shall, however, be concerned with the absent ties, i.e. connections that are beyond the concept of weak ties. Although those connections are of no relevance to the network as a whole, they should be encouraged to become “weak ties”, in order to interact and gain importance within the network. In the particular case of the EWG-DSS network, absent connections are represented within the network files, by for example authors who exist in the network, are publishing actively within their areas of research, also with collaborators outside the scope of the group, nevertheless do not interact with other members of the EWG-DSS group, via joint collaborative research work. This issue was little exploited in this paper, but it is part of the project’s main objectives and will be worked out in the future.
- The output of this step devised an academic-social network analysis, which identified the collaboration relationship that exists among the group members, as well as how the group’s dynamics has evolved since its foundation in 1989. It also revisited the current areas of research of the professionals involved, as a by-product. Preliminary results of this project were presented in the DSS Streams of the 2009 and 2010 EURO K Conferences in Bonn and Lisbon, respectively. Since the publication of those results, the EWG-DSS academic-social network analysis has already shown to encourage new research and promote further collaboration among the group members in common projects and joint-publications. The metrics of the network graphical representations helped us to build up a consistent basis for analyzing the network graphs that were generated via the input data available. As already well known from the literature, the basic characteristics of a social network are described by the following parameters: 1. Number of nodes; 2. Number of isolated nodes; 3. List of nodes attributes; 4. Number of edges (connections); 5. List of edges’ attributes; 6. Density of the graph; 7. Type of the graph (directed / not directed); 8. Type of the connections of the graph (weakly connected or not); 9. Number of weakly connected components (nodes); and 10. Number of nodes in the largest connected components. Based on the study conducted by Granovetter in the earlier developments of social networks [Granovetter, 1973], we believe that there is great potential for the weak connections of our EWG-DSS network still to be able to develop into strong ones. In Granovetter’s study, he has explained that information was far more likely to be “diffused” through weaker ties, than through already strong connections. We shall, however, be concerned with the absent ties, i.e. connections that are beyond the concept of weak ties. Although those connections are of no relevance to the network as a whole, they should be encouraged to become “weak ties”, in order to interact and gain importance within the network. In the particular case of the EWG-DSS network, absent connections are represented within the network files, by for example authors who exist in the network, are publishing actively within their areas of research, also with collaborators outside the scope of the group, nevertheless do not interact with other members of the EWG-DSS group, via joint collaborative research work. This issue was little exploited in this paper, but it is part of the project’s main objectives and will be worked out in the future.
- Variations wrt version 1 V1 : similarity-> Jaccard Vinter : relation : coauthorship -> Weight = integers - Use of a new tool NodeXL The main goal of these intermediate study is not that much the results of the analysis, indeed the data set is outdated and inclomplete This intermediate study is mainly an exploratory study in order to enhance what we can expect from version 2. V1 focussed on clusters and radial vizualisation I’ll be quick on the general aspect of the network and focuss on a new topic : centrality measures
- On the same data set (1327 publications / 1989-2008) 70 authors -> 782 authors and co-authors (0->4 authors/publication)
- %% I don’t develop the discussion about the clusters if it is done in the first part of the talk %% Loops indicate publications with one single author links are thin or dense according to the number of common publication… its unclear on the slide… Just notice that the density of the links suggests the activity level of the cluster.
- Previously used software did not make it possible. With nodeXL it is possible: clustering (inside connexe component) %% Sets of papers can be related to each clusters %% Still open question : can we associate specific topics/keywords to these clusters?
- Centrality the ratio of other vertices to which it is immediatlely connected (=degree/n-1) Also interpreted as « the capacity to acquire every kind of information that passes through the network » In this study = the number of co-authors Observe that 1/ almost 600 authors have only 1 or 2 connections : they are the co-authors that are not actually members of the community 2/ Philip Powel has an impressive wide set of 98 co-authors in our (limited) set of papers… When concidering only the members of the community, Max degree was 9, and was held by Pascale Zaraté Average degree was only 1,14… Interpretation : a lot of authors-clusters have only one member in EWG-DSS %can I say what follows? % A more controversal interpretation is the following Until now, EWG-DSS is recognized as a good place to publish results regarding DSS… %%but not yet as a place to build collaborations %% The project will provide support to turn more the DDS group into a collaboration platform
- Betweeness centrality = % of shortest pathes on which the node appears Interpreted as The control that the node can play between two other nodes On our data set Ph. Powels is the only connection to the network of many of his co-authors. As they are numerous, unsurprisingly he has a high Betweeness centrality. It is the case for other authors with a high degree. More interresting is the case of people with a more modest centrality degree, having a high betweeness centrality: they are people acting as bridges between non isolated clusters in the community this is for example the case of Fatima Dargam
- Technicaly, the eigen vector centrality of a node is given by the corresponding coordinate of the eigen vector of the adjacency matrix associated with its maximal eigen value, This is expressed by the equation. Even for those who are not familiar with eigen values, the equation expresses that if you are strongly conected with nodes with a high Cev then you have a high Cev too… If you have thin links or if your neighbours have low Cev, then your Cev is also lower. We observe this effect in the table. Ph. Powell acts as an « attractor », he is the first and the three following ones are among his co-authors Due to the limited size of data set it is difficult to draw more specific conclusion out of this EVC but the new version would lead to more interpretable results
- The output of this step devised an academic-social network analysis, which identified the collaboration relationship that exists among the group members, as well as how the group’s dynamics has evolved since its foundation in 1989. It also revisited the current areas of research of the professionals involved, as a by-product. Preliminary results of this project were presented in the DSS Streams of the 2009 and 2010 EURO K Conferences in Bonn and Lisbon, respectively. Since the publication of those results, the EWG-DSS academic-social network analysis has already shown to encourage new research and promote further collaboration among the group members in common projects and joint-publications. The metrics of the network graphical representations helped us to build up a consistent basis for analyzing the network graphs that were generated via the input data available. As already well known from the literature, the basic characteristics of a social network are described by the following parameters: 1. Number of nodes; 2. Number of isolated nodes; 3. List of nodes attributes; 4. Number of edges (connections); 5. List of edges’ attributes; 6. Density of the graph; 7. Type of the graph (directed / not directed); 8. Type of the connections of the graph (weakly connected or not); 9. Number of weakly connected components (nodes); and 10. Number of nodes in the largest connected components. Based on the study conducted by Granovetter in the earlier developments of social networks [Granovetter, 1973], we believe that there is great potential for the weak connections of our EWG-DSS network still to be able to develop into strong ones. In Granovetter’s study, he has explained that information was far more likely to be “diffused” through weaker ties, than through already strong connections. We shall, however, be concerned with the absent ties, i.e. connections that are beyond the concept of weak ties. Although those connections are of no relevance to the network as a whole, they should be encouraged to become “weak ties”, in order to interact and gain importance within the network. In the particular case of the EWG-DSS network, absent connections are represented within the network files, by for example authors who exist in the network, are publishing actively within their areas of research, also with collaborators outside the scope of the group, nevertheless do not interact with other members of the EWG-DSS group, via joint collaborative research work. This issue was little exploited in this paper, but it is part of the project’s main objectives and will be worked out in the future.
- EWG-DSS-Collab-Net V.2 will extend the original implementation in many ways. It will consider: a hybrid methodology of input data collection (manual and automatic), using also web mining of electronic databases to automatically detect relationships of members; a refined model of the publication relationship structure, taking into account “author-title-journal/conference-multiple keywords-multiple topics”; as well as a more refined model of the collaboration relationship structure, which includes workshop/conference publications, informal work meetings, event co-organisations, scientific committees/boards, book/journal editorials, etc.
- Along with social network analysis statistics, EWG-DSS-Collab-Net V.2 will perform collaboration trend analysis, showing: co-authorships and co-citations to further illustrate the dynamics of EWG-DSS publications overtime. The analysis features, among other characteristics: (a) the number and percentage of multi-author papers and co-authors in comparison with single-author papers; (b) number and percentage of co-citations; (c) identification of publications that are closely related to a given topic, as well as the authors involved. Important features: The identification of publications closely related to a given topic will help us specially to find researchers who could be more appropriate to collaborate in reviewing papers for the annual EWG-DSS workshops and journal editions, as well as to find specifically skilled researchers among the members of the group to collaborate on projects. Most of all, the extended analysis of EWG-DSS Collab-Net V.2 plans to promote continued new research and collaboration among the academic members of the group and to attract new members for further fruitful collaboration.
- Implementation Data Input Data Validation Data Structure Model & Network Repository Network Analysis Dissemination – Visualization Web-Interface Along with social network analysis statistics, EWG-DSS-Collab-Net V.2 will perform collaboration trend analysis, showing: co-authorships and co-citations to further illustrate the dynamics of EWG-DSS publications overtime. The analysis features, among other characteristics: (a) the number and percentage of multi-author papers and co-authors in comparison with single-author papers; (b) number and percentage of co-citations; (c) identification of publications that are closely related to a given topic, as well as the authors involved. Important features: The identification of publications closely related to a given topic will help us specially to find researchers who could be more appropriate to collaborate in reviewing papers for the annual EWG-DSS workshops and journal editions, as well as to find specifically skilled researchers among the members of the group to collaborate on projects. Most of all, the extended analysis of EWG-DSS Collab-Net V.2 plans to promote continued new research and collaboration among the academic members of the group and to attract new members for further fruitful collaboration.
- Implementation Data Input Data Validation Data Structure Model & Network Repository Network Analysis Dissemination – Visualization Web-Interface Along with social network analysis statistics, EWG-DSS-Collab-Net V.2 will perform collaboration trend analysis, showing: co-authorships and co-citations to further illustrate the dynamics of EWG-DSS publications overtime. The analysis features, among other characteristics: (a) the number and percentage of multi-author papers and co-authors in comparison with single-author papers; (b) number and percentage of co-citations; (c) identification of publications that are closely related to a given topic, as well as the authors involved. Important features: The identification of publications closely related to a given topic will help us specially to find researchers who could be more appropriate to collaborate in reviewing papers for the annual EWG-DSS workshops and journal editions, as well as to find specifically skilled researchers among the members of the group to collaborate on projects. Most of all, the extended analysis of EWG-DSS Collab-Net V.2 plans to promote continued new research and collaboration among the academic members of the group and to attract new members for further fruitful collaboration.
- Which Social Network Environments to use? Which Analysis Metrics are relevant for our Network? Comments from Isabelle Linden: Regarding slide 34-35 - Ora is more oriented for dynamic analysis, that the reason why David did not focussed on it - UciNet-Netdraw has also to be concidered (this contribution was expected from Wei, this is the reason why I did not ask David to do it...)
- Which Network Visualisation will the users (EWG-DSS members) prefer to have?
- It was not a central objective of this paper to identify each one of the network clusters and analyze its results. But it is certainly the aim of the whole Social Network Analysis project, being led by the EURO Working Group on DSS, to perform a detailed analysis of each represented cluster of publications, authors and topics in a constructive way. In this paper we used the already developed network structure for the EURO Working Group on DSS, using 70 of over 105 current group members as its defined population of authors, together with their relationships of research collaborations and main topics of research, within the areas around Decision Making. We focused on identifying the main collaboration relationship that exists among the EWG-DSS members to show: 1. how the members of the group relate to each other, in terms of topics of research; 2. what are the most relevant topics of research within in the group as a whole; and 3. what are the relevant statistical data/knowledge concerning the amount of publications that circulate within the group, how the authors are tending to cooperate with each other and also how many external collaborators (co-authors not in EWG-DSS) exist and indirectly participate in the EWG-DSS.
- To proceed we need YOUR support! Participation Data: Research production in joint-work Support with development force Feedback On the way to accomplish the main aim of our EWG-DSS social-academic network analysis project, we have already covered most of the planned work. However, for performing the full analysis of the network, as well as for optimizing its relationships, our project still has some steps to cover, which we plan to fulfil within the next project-milestones. This project has established a powerful tool to identify the EWG-DSS group interaction on one hand, and also to provide a useful feedback for further collaboration in joint-research on the other. We hope and expect that this tool gives us enough grounds for extinguishing the existing “absent connections” among the members of the EWG-DSS network, by enabling us to identify them on one hand and encourage them to take part in new research and academic cooperation within the group, on the other. As further work, we should also consider expanding the period of the publications considered in the network analysis to the current year, as well as maximizing the number of authors to the limit number of members in the EWG-DSS group. Another important perspective would be to consider more specific areas and sub-areas of research topics and applications, as well as to assign more than one topic of research to the publications of the network analysis in order to better evaluate the academic interaction of the group. The authors are grateful to all the members of the EWG-DSS working group, for their important participation in this project. This work would not be possible to be carried out without the cooperation of all the EWG-DSS members, who have replied to us.