Pavla Drázdilová, K. Slaninová, J. Martinovič, Gamila Obadi, V. Snás̃el
The growth of eLearning systems popularity motivates researchers to study these systems intensively. Users of eLearning systems form social networks through the different activities performed by them (sending emails, reading study materials, chat, taking tests, etc.). This paper focuses on searching of latent social networks from eLearning systems data. This data consists of students activity records where latent ties among actors are embedded. The social network studied in this paper is represented by groups of students who have similar contacts, and interact in similar social circles, where the interest in performing similar tasks among users determines the groups with similar interactions. Different methods of data clustering analysis were applied to these groups and the findings show the existence of latent ties among the group members. The second part of this paper focuses on social network visualization. Graphical representation of social network can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relationships and determine the amount of independent groups in a given network.
{"title":"Creation of Students' Activities from Learning Management System and their Analysis","authors":"Pavla Drázdilová, K. Slaninová, J. Martinovič, Gamila Obadi, V. Snás̃el","doi":"10.1109/CASON.2009.34","DOIUrl":"https://doi.org/10.1109/CASON.2009.34","url":null,"abstract":"The growth of eLearning systems popularity motivates researchers to study these systems intensively. Users of eLearning systems form social networks through the different activities performed by them (sending emails, reading study materials, chat, taking tests, etc.). This paper focuses on searching of latent social networks from eLearning systems data. This data consists of students activity records where latent ties among actors are embedded. The social network studied in this paper is represented by groups of students who have similar contacts, and interact in similar social circles, where the interest in performing similar tasks among users determines the groups with similar interactions. Different methods of data clustering analysis were applied to these groups and the findings show the existence of latent ties among the group members. The second part of this paper focuses on social network visualization. Graphical representation of social network can describe its structure very efficiently. It can enable social network analysts to determine the network degree of connectivity. Analysts can easily determine individuals with a small or large amount of relationships and determine the amount of independent groups in a given network.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126610590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Some social communities evident their own unique internal structure. In the paper we consider social communities composed of several cohesive subgroups which we call compound communities. For such communities, an extended generalized blockmodeling is proposed, taking into account the structure of compound communities and relations with external actors. Using the extension, the community protection approach is proposed and used in detection of spam directed towards an e-mail local society.
{"title":"Extended Generalized Blockmodeling for Compound Communities and External Actors","authors":"R. Brendel, H. Krawczyk","doi":"10.1109/CASoN.2009.32","DOIUrl":"https://doi.org/10.1109/CASoN.2009.32","url":null,"abstract":"Some social communities evident their own unique internal structure. In the paper we consider social communities composed of several cohesive subgroups which we call compound communities. For such communities, an extended generalized blockmodeling is proposed, taking into account the structure of compound communities and relations with external actors. Using the extension, the community protection approach is proposed and used in detection of spam directed towards an e-mail local society.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124332745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The e-learning databases content description methods, for presentation and distribution, were introduced in many works (as in. IMS and Common Cartridge action), for standardisation (SCORM) and distribution platforms for authoring systems (MOODLE, MAMS). The personalisation processes of training units structure finding is still an investigations area.The specifications like IMS LD supported by OUML languages allow the courses structure modelling, leaving not solved ontology of courseware and presentation standards. The contribution presents an approach allowing controlling the course construction by a directed multi-graph, supported by a current knowledge of the course user. The decisions are based on a fuzzy logic measures, describing the user’s knowledge. The authors elaborated the e-learning applications development platform (called Multimedia Applications Management Shell – MAMS) provided with various tools that simplify the e-learning unit’s development and the applications controlling processes implementation.
{"title":"The Graph Descriptors of E-content Unit Organisation and Controlling Features","authors":"J. Piecha, M. Bernaś","doi":"10.1109/CASoN.2009.24","DOIUrl":"https://doi.org/10.1109/CASoN.2009.24","url":null,"abstract":"The e-learning databases content description methods, for presentation and distribution, were introduced in many works (as in. IMS and Common Cartridge action), for standardisation (SCORM) and distribution platforms for authoring systems (MOODLE, MAMS). The personalisation processes of training units structure finding is still an investigations area.The specifications like IMS LD supported by OUML languages allow the courses structure modelling, leaving not solved ontology of courseware and presentation standards. The contribution presents an approach allowing controlling the course construction by a directed multi-graph, supported by a current knowledge of the course user. The decisions are based on a fuzzy logic measures, describing the user’s knowledge. The authors elaborated the e-learning applications development platform (called Multimedia Applications Management Shell – MAMS) provided with various tools that simplify the e-learning unit’s development and the applications controlling processes implementation.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126892633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ubiquitous connectivity today allows many users to remain connected regardless of location with various kinds of communities. This paper studies challenges in building trusted communities that encompass both new users as well as users already possessing credentials from other well known connectivity providers, federations, content providers and social networks. We postulate that trusted communities are initially created as a means to access some services, but become enriched with user created services. We present an architecture aimed at managing the complexity of service composition, access as well as guarantees of authenticity. Since users possess multiple credentials from various identity providers, we address this in our architecture from the service access perspective. In addition, our model explicitly takes into account cases where users may temporarily be granted access to a community’s services based on recommendations from existing members.
{"title":"An Architecture to Facilitate Membership and Service Management in Trusted Communities","authors":"Seppo Heikkinen, B. Silverajan","doi":"10.1109/CASoN.2009.16","DOIUrl":"https://doi.org/10.1109/CASoN.2009.16","url":null,"abstract":"Ubiquitous connectivity today allows many users to remain connected regardless of location with various kinds of communities. This paper studies challenges in building trusted communities that encompass both new users as well as users already possessing credentials from other well known connectivity providers, federations, content providers and social networks. We postulate that trusted communities are initially created as a means to access some services, but become enriched with user created services. We present an architecture aimed at managing the complexity of service composition, access as well as guarantees of authenticity. Since users possess multiple credentials from various identity providers, we address this in our architecture from the service access perspective. In addition, our model explicitly takes into account cases where users may temporarily be granted access to a community’s services based on recommendations from existing members.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123917530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Humans tend to interact, to share information and establish relationships and naturally form groups based on interests. This fact motivate research on models, abstractions and mechanisms in order to enable more transparent and flexible interactions between users and group based collaborations. We propose a model that deals with dynamic group membership and combines multiple forms of communication and sharing mechanisms inside each group unit.
{"title":"A Group-Based Model for Dynamic Communities","authors":"C. Morgado, J. Cunha, J. Custódio, Nuno Correia","doi":"10.1109/CASoN.2009.27","DOIUrl":"https://doi.org/10.1109/CASoN.2009.27","url":null,"abstract":"Humans tend to interact, to share information and establish relationships and naturally form groups based on interests. This fact motivate research on models, abstractions and mechanisms in order to enable more transparent and flexible interactions between users and group based collaborations. We propose a model that deals with dynamic group membership and combines multiple forms of communication and sharing mechanisms inside each group unit.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126414508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Opinion mining has tremendous potentials in extracting valuable information and experience from individuals on products and services. In particular, product features extraction and sentiment scoring on extracted features are fundamental steps. Opinion knowledge extraction often involves extensive application of natural language processing, manual labeling and machine learning methods.In this paper, we focus on developing fine-grained product feature extractions with minimal tailor build language models and labeling.A threshold-normalized sentence-level word model is proposed for opinion feature mining. The opinion feature extraction is then solved via matrix factorization technique. Evaluation on feature-entropies, sentence-entropies and human evaluation demonstrated the superiority of our approach. Highly relevant and fine-grained opinion features are extracted automatically.
{"title":"Sentence Factorization for Opinion Feature Mining","authors":"Chun-hung Li","doi":"10.1109/CASoN.2009.33","DOIUrl":"https://doi.org/10.1109/CASoN.2009.33","url":null,"abstract":"Opinion mining has tremendous potentials in extracting valuable information and experience from individuals on products and services. In particular, product features extraction and sentiment scoring on extracted features are fundamental steps. Opinion knowledge extraction often involves extensive application of natural language processing, manual labeling and machine learning methods.In this paper, we focus on developing fine-grained product feature extractions with minimal tailor build language models and labeling.A threshold-normalized sentence-level word model is proposed for opinion feature mining. The opinion feature extraction is then solved via matrix factorization technique. Evaluation on feature-entropies, sentence-entropies and human evaluation demonstrated the superiority of our approach. Highly relevant and fine-grained opinion features are extracted automatically.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122699392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Much structured data of scientific interest can be represented as networks, where sets of nodes or vertices are joined together in pairs by links or edges. Although these networks may belong to different research areas, there is one property that many of them do have in common: the network community structure, which means that there exists densely connected groups of vertices, with only sparser connections between groups. Identifying community structure in networks has attracted much research attention. However, most existing approaches require structure information of the graph in question to be completely accessible, which is impractical for some large networks, e.g., the World Wide Web (WWW). In this paper, we propose a community discovery algorithm for large networks that iteratively finds communities based on local information only. We compare our algorithm with previous global approaches to show its scalability. Experimental results on real world networks, such as the co-purchase network from Amazon, verify the feasibility and effectiveness of our approach.
{"title":"Detecting Communities in Large Networks by Iterative Local Expansion","authors":"Jiyang Chen, Osmar R Zaiane, R. Goebel","doi":"10.1109/CASoN.2009.29","DOIUrl":"https://doi.org/10.1109/CASoN.2009.29","url":null,"abstract":"Much structured data of scientific interest can be represented as networks, where sets of nodes or vertices are joined together in pairs by links or edges. Although these networks may belong to different research areas, there is one property that many of them do have in common: the network community structure, which means that there exists densely connected groups of vertices, with only sparser connections between groups. Identifying community structure in networks has attracted much research attention. However, most existing approaches require structure information of the graph in question to be completely accessible, which is impractical for some large networks, e.g., the World Wide Web (WWW). In this paper, we propose a community discovery algorithm for large networks that iteratively finds communities based on local information only. We compare our algorithm with previous global approaches to show its scalability. Experimental results on real world networks, such as the co-purchase network from Amazon, verify the feasibility and effectiveness of our approach.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131664155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are many search engines in the web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Matrix Decomposition (Singular Value Decomposition (SVD) and Nonnegative Matrix Factorization (NMF)) can be good solution for the search results clustering.
{"title":"Using a Matrix Decomposition for Clustering Data","authors":"H. Abdulla, M. Polovincak, V. Snás̃el","doi":"10.1109/CASON.2009.11","DOIUrl":"https://doi.org/10.1109/CASON.2009.11","url":null,"abstract":"There are many search engines in the web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Matrix Decomposition (Singular Value Decomposition (SVD) and Nonnegative Matrix Factorization (NMF)) can be good solution for the search results clustering.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133892023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
W. C. Kammergruber, Maximilian Viermetz, Cai-Nicolas Ziegler
Tagging and social networks have come into increasing use in concert with the rise of collaborative and interactive on-line media. The focus of tagging is herein twofold: First of all the plain annotation of existing data by a governing instance in order to increase the semantic content of unstructured data, and secondly the application of such meta-information by a community or a group of like minded users. The information contained in such social tagging reflects the point of view and understanding of the community, presenting a valuable source of information for the discovery of community structure,content and intent. This paper proposes an approach aimed at the use of community based tagging to address problems in link prediction and the discovery of complex user groups in a fleeting and unstructured web-based environment. The ideas presented in this paper are applied to a real world scenario, and the results show a distinct opportunity in community detection and support. This result will be incorporated into emerging knowledge management systems within Siemens AG in the near future.
{"title":"Discovering Communities of Interest in a Tagged On-Line Environment","authors":"W. C. Kammergruber, Maximilian Viermetz, Cai-Nicolas Ziegler","doi":"10.1109/CASoN.2009.22","DOIUrl":"https://doi.org/10.1109/CASoN.2009.22","url":null,"abstract":"Tagging and social networks have come into increasing use in concert with the rise of collaborative and interactive on-line media. The focus of tagging is herein twofold: First of all the plain annotation of existing data by a governing instance in order to increase the semantic content of unstructured data, and secondly the application of such meta-information by a community or a group of like minded users. The information contained in such social tagging reflects the point of view and understanding of the community, presenting a valuable source of information for the discovery of community structure,content and intent. This paper proposes an approach aimed at the use of community based tagging to address problems in link prediction and the discovery of complex user groups in a fleeting and unstructured web-based environment. The ideas presented in this paper are applied to a real world scenario, and the results show a distinct opportunity in community detection and support. This result will be incorporated into emerging knowledge management systems within Siemens AG in the near future.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133525553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Some methods for object group identification applicable for social group identification are compared. We suppose that people are characterized by their actions, for example the deputies are characterized by their voting habits. We are interested in binary data analysis (e.g. the result of voting is yes or not). The dataset consisting of the roll-call votes records in the Russian parliament in 2004 was analyzed. Methods of hierarchical and fuzzy clustering, and Boolean factor analysis are applied. In the first case, we propose two-step analysis in which factor loadings (as result of factor analysis of objects) obtained in the first step are interpreted by cluster analysis in the second step. For the cluster number determination both traditional and modified coefficients are used. Further, we suggest using Hopfield-like neural network based Boolean factor analysis for this purpose. This proposed method gives the best results in the case of deputies grouping.
{"title":"Social Group Identification and Clustering","authors":"D. Húsek, H. Řezanková, J. Dvorský","doi":"10.1109/CASoN.2009.12","DOIUrl":"https://doi.org/10.1109/CASoN.2009.12","url":null,"abstract":"Some methods for object group identification applicable for social group identification are compared. We suppose that people are characterized by their actions, for example the deputies are characterized by their voting habits. We are interested in binary data analysis (e.g. the result of voting is yes or not). The dataset consisting of the roll-call votes records in the Russian parliament in 2004 was analyzed. Methods of hierarchical and fuzzy clustering, and Boolean factor analysis are applied. In the first case, we propose two-step analysis in which factor loadings (as result of factor analysis of objects) obtained in the first step are interpreted by cluster analysis in the second step. For the cluster number determination both traditional and modified coefficients are used. Further, we suggest using Hopfield-like neural network based Boolean factor analysis for this purpose. This proposed method gives the best results in the case of deputies grouping.","PeriodicalId":425748,"journal":{"name":"2009 International Conference on Computational Aspects of Social Networks","volume":"7 20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133566927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：