Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062609
R. Loveland, Noah Kaplan
In many industrial applications, data comes in the form of an unlabeled stream, likely containing classes that a user has not seen before. In these cases, a user generally cares about four things: classification, class discovery, notification of events in certain classes, and the amount of data they need to label. In this work we present Anomalous/ Relevant Event Detection (A/RED), an active learning system that operates upon imbalanced data streams to find new classes and classify incoming events. A/RED is unique in that it takes into account user preference for the relevance of classes. An A/RED query involves asking for a label and a binary relevance label. A relevant class is queried more often, and as a result, the classifier performs better for these instances.
{"title":"Anomalous/Relevant Event Detection (A/RED): Active Machine Learning for Finding Rare Events","authors":"R. Loveland, Noah Kaplan","doi":"10.1109/SNAMS58071.2022.10062609","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062609","url":null,"abstract":"In many industrial applications, data comes in the form of an unlabeled stream, likely containing classes that a user has not seen before. In these cases, a user generally cares about four things: classification, class discovery, notification of events in certain classes, and the amount of data they need to label. In this work we present Anomalous/ Relevant Event Detection (A/RED), an active learning system that operates upon imbalanced data streams to find new classes and classify incoming events. A/RED is unique in that it takes into account user preference for the relevance of classes. An A/RED query involves asking for a label and a binary relevance label. A relevant class is queried more often, and as a result, the classifier performs better for these instances.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115672654","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}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062760
Kamalkumar R. Macwan, Abdessamad Imine, M. Rusinowitch
Social recommendation is an advanced service of social networking platforms that is provided to their users. Social recommendation uses profiles and connections to generate personalized suggestions of contents, advertisements, people, pages, or interest groups. Since individual sensitive information is possibly involved in elaborating a recommendation, it may be inferred by an adversary in some situations. In this work, we design a differentially private setting to prevent social recommendations from disclosing sensitive information. Our recommendation system targets users of online social networks by leveraging their attributes and relationships. Unlike other approaches, we rely on both profile similarity and homophily properties. Therefore, our system estimates the frequency of friends who share some attribute values and applies non-negative matrix factorization to derive recommendations such as hobbies, movies, etc. We demonstrate the effectiveness of the proposed approach through experiments on real-world datasets and evaluation according to utility measures.
{"title":"Privacy Preserving Recommendations for Social Networks","authors":"Kamalkumar R. Macwan, Abdessamad Imine, M. Rusinowitch","doi":"10.1109/SNAMS58071.2022.10062760","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062760","url":null,"abstract":"Social recommendation is an advanced service of social networking platforms that is provided to their users. Social recommendation uses profiles and connections to generate personalized suggestions of contents, advertisements, people, pages, or interest groups. Since individual sensitive information is possibly involved in elaborating a recommendation, it may be inferred by an adversary in some situations. In this work, we design a differentially private setting to prevent social recommendations from disclosing sensitive information. Our recommendation system targets users of online social networks by leveraging their attributes and relationships. Unlike other approaches, we rely on both profile similarity and homophily properties. Therefore, our system estimates the frequency of friends who share some attribute values and applies non-negative matrix factorization to derive recommendations such as hobbies, movies, etc. We demonstrate the effectiveness of the proposed approach through experiments on real-world datasets and evaluation according to utility measures.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125076485","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}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062505
Hassan Mistareehi, D. Manivannan, H. Salameh
Vehicular Ad hoc NETworks (VANETs) are going to help in deploying Intelligent Transportation Systems (ITS). Various schemes proposed in the literature use vehicles equipped with On Board Units (OBUs) to collect events such as weather conditions, collision prevention, and many others to notify drivers about these events. However, several existing schemes don't consider safety message collection in areas with a low density of vehicles. These areas also could have bad road conditions (e.g., icy roads) and may have poor connectivity. Therefore, in this paper, we improve safety message collection and notify the drivers about these incidents in advance, so they can take proper actions. In addition, the security and privacy of vehicles are achieved. We also improve the privacy-preserving of vehicles.
{"title":"A Secure and Improved Safety Message Collection with Increased Privacy-Preserving Algorithm for VANETs","authors":"Hassan Mistareehi, D. Manivannan, H. Salameh","doi":"10.1109/SNAMS58071.2022.10062505","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062505","url":null,"abstract":"Vehicular Ad hoc NETworks (VANETs) are going to help in deploying Intelligent Transportation Systems (ITS). Various schemes proposed in the literature use vehicles equipped with On Board Units (OBUs) to collect events such as weather conditions, collision prevention, and many others to notify drivers about these events. However, several existing schemes don't consider safety message collection in areas with a low density of vehicles. These areas also could have bad road conditions (e.g., icy roads) and may have poor connectivity. Therefore, in this paper, we improve safety message collection and notify the drivers about these incidents in advance, so they can take proper actions. In addition, the security and privacy of vehicles are achieved. We also improve the privacy-preserving of vehicles.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130063433","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}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062846
Sahar Bakhtar, Hovhannes A. Harutyunyan
Recent years have witnessed the rapid growth of social network services and consequently, research problems investigated in this area. Community detection is one of the most important problems in social networks. A good community can be defined as a group of vertices that are highly connected and loosely connected to the vertices outside the group. Community detection includes exploring the community partitioning in social networks. Regarding the fact that social networks are huge, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. This problem can be defined as the detection of a community for a given node by using local information. Many networks contain both positive and negative relations. A community in signed networks is defined as a group of nodes that are densely connected by positive links within the community and negative links between communities. In this paper, considering the problem of local community detection in signed networks, a new fast algorithm, noted as $Alg_{SP}$, is developed to identify a dense community for a given node in signed networks. Experimental results show that the proposed algorithm can detect the ground-truth communities independently from the starting nodes.
{"title":"A Fast Local Community Detection Algorithm in Signed Social Networks","authors":"Sahar Bakhtar, Hovhannes A. Harutyunyan","doi":"10.1109/SNAMS58071.2022.10062846","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062846","url":null,"abstract":"Recent years have witnessed the rapid growth of social network services and consequently, research problems investigated in this area. Community detection is one of the most important problems in social networks. A good community can be defined as a group of vertices that are highly connected and loosely connected to the vertices outside the group. Community detection includes exploring the community partitioning in social networks. Regarding the fact that social networks are huge, having complete information about the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. This problem can be defined as the detection of a community for a given node by using local information. Many networks contain both positive and negative relations. A community in signed networks is defined as a group of nodes that are densely connected by positive links within the community and negative links between communities. In this paper, considering the problem of local community detection in signed networks, a new fast algorithm, noted as $Alg_{SP}$, is developed to identify a dense community for a given node in signed networks. Experimental results show that the proposed algorithm can detect the ground-truth communities independently from the starting nodes.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125978335","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}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062757
Jamil Razmak, W. Farhan, Ghaleb A. El Refae
We conducted a literature review to draw a short roadmap of both the drivers and challenges related to social networking in the banking industry. We extracted from the literature simple models based on drivers and challenges, on which banking policy makers, technologists, and researchers can build in the future. Our findings show that various trends in social networking drivers and challenges can have either a negative or a positive impact on the banking industry. Banks must use different tools, such as force-field analysis, to compare between these drivers and challenges. The use of modern technologies, such as cloud computing and AI aligning, along with managerial actions such as hiring specialized technologists and adopting marketing strategies through social networking, will help the banking industry maximize its opportunities and minimize its challenges.
{"title":"A Roadmap of Social Networking Drivers and Challenges in the Era of Digital Banking","authors":"Jamil Razmak, W. Farhan, Ghaleb A. El Refae","doi":"10.1109/SNAMS58071.2022.10062757","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062757","url":null,"abstract":"We conducted a literature review to draw a short roadmap of both the drivers and challenges related to social networking in the banking industry. We extracted from the literature simple models based on drivers and challenges, on which banking policy makers, technologists, and researchers can build in the future. Our findings show that various trends in social networking drivers and challenges can have either a negative or a positive impact on the banking industry. Banks must use different tools, such as force-field analysis, to compare between these drivers and challenges. The use of modern technologies, such as cloud computing and AI aligning, along with managerial actions such as hiring specialized technologists and adopting marketing strategies through social networking, will help the banking industry maximize its opportunities and minimize its challenges.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129245571","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}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062637
S. Shioda, Takahito Konishi
It is well known that most of tweets are retweeted only a few times at most, while very few tweets get a very large number of retweets. This concentration of retweet is caused by a so-called Matthew effect of Twitter; original tweets that have been retweeted more often are more likely to be retweeted further. In this paper, we quantify the Matthew effect of Twitter by using the model, under which the probability that an original tweet (say, tweet A) is retweeted is proportional to a given function $f(i)$, where $i$ denotes the number of retweets that tweet A has received so far. We assume that $f(i)$ is a non-decreasing function of $i$. The proposed model, a simple extension of the Yule process, is analytically tractable and the expression of the distribution of the number of retweets that an original tweet receives can be explicitly obtained. We show that by assuming $f(i)=a+i^{delta}$ and $delta$ is around 0.8, the distribution of the number of retweets based on the proposed model is well consistent with the actual distribution.
{"title":"Quantifying Matthew Effect of Twitter","authors":"S. Shioda, Takahito Konishi","doi":"10.1109/SNAMS58071.2022.10062637","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062637","url":null,"abstract":"It is well known that most of tweets are retweeted only a few times at most, while very few tweets get a very large number of retweets. This concentration of retweet is caused by a so-called Matthew effect of Twitter; original tweets that have been retweeted more often are more likely to be retweeted further. In this paper, we quantify the Matthew effect of Twitter by using the model, under which the probability that an original tweet (say, tweet A) is retweeted is proportional to a given function $f(i)$, where $i$ denotes the number of retweets that tweet A has received so far. We assume that $f(i)$ is a non-decreasing function of $i$. The proposed model, a simple extension of the Yule process, is analytically tractable and the expression of the distribution of the number of retweets that an original tweet receives can be explicitly obtained. We show that by assuming $f(i)=a+i^{delta}$ and $delta$ is around 0.8, the distribution of the number of retweets based on the proposed model is well consistent with the actual distribution.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134180393","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}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062597
A. Bobic, Igor Jakovljevic, C. Gütl, Jean-Marie Le Goff, Andreas Wagner
Recommender systems play a pivotal role in various human-centered online systems by filtering out relevant information from large databases. However, most recommender systems consume explicit private user information such as exchanged messages and information between users and items such as likes and shares without exploring other latent factors. Past events have shown that this can have decremental consequences on users' privacy. One type of application where alternative solutions have not yet been investigated are messaging platforms in larger corporate environments. These applications would benefit from recommender systems that consume only anonymized implicit data to enable employees to discover new communities and people. As a first step in developing such a recommender system, this paper describes the construction and analysis of implicit social network data from the messaging platform Mattermost at CERN and the extraction of measures for indicating similarity between users and channels. Additionally, it describes the use of these measures to evaluate multiple existing collaborative filter-based recommender systems, where their performances are compared and evaluated against simple measures. The evaluation results indicate that combining clustering approaches and custom features extracted through our data analysis outperforms standard collaborative filtering techniques. These results will be used in the future to create a new custom recommender system for messaging at CERN that only uses anonymized and implicit data.
{"title":"Implicit User Network Analysis of Communication Platform Open Data for Channel Recommendation","authors":"A. Bobic, Igor Jakovljevic, C. Gütl, Jean-Marie Le Goff, Andreas Wagner","doi":"10.1109/SNAMS58071.2022.10062597","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062597","url":null,"abstract":"Recommender systems play a pivotal role in various human-centered online systems by filtering out relevant information from large databases. However, most recommender systems consume explicit private user information such as exchanged messages and information between users and items such as likes and shares without exploring other latent factors. Past events have shown that this can have decremental consequences on users' privacy. One type of application where alternative solutions have not yet been investigated are messaging platforms in larger corporate environments. These applications would benefit from recommender systems that consume only anonymized implicit data to enable employees to discover new communities and people. As a first step in developing such a recommender system, this paper describes the construction and analysis of implicit social network data from the messaging platform Mattermost at CERN and the extraction of measures for indicating similarity between users and channels. Additionally, it describes the use of these measures to evaluate multiple existing collaborative filter-based recommender systems, where their performances are compared and evaluated against simple measures. The evaluation results indicate that combining clustering approaches and custom features extracted through our data analysis outperforms standard collaborative filtering techniques. These results will be used in the future to create a new custom recommender system for messaging at CERN that only uses anonymized and implicit data.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115107673","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}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062500
Nehal Eleyan, Mariam Al Akasheh, Esraa Faisal Malik, O. Hujran
Data mining methods have been employed successfully in several industries, including education, where they are known as educational data mining methods. Educational data mining aims to extract in-depth knowledge from raw data to build automated systems that could be used in the educational sector. With the advancement of data mining technologies, it is now possible to mine educational data to enhance educational practices. This study, therefore, uses educational data mining techniques to predict the final grades of secondary school students. This study has employed several Machine Learning (ML) algorithms, such as classification trees, regression trees, logistic Regression, and Multiple Regression. In addition, the R programming language was used to develop the prediction models. The dataset used in this study was obtained from two secondary schools in Portugal. According to the findings, classification trees and logistic Regression fared better than regression trees and multiple Regression.
{"title":"Predicting Student Performance Using Educational Data Mining","authors":"Nehal Eleyan, Mariam Al Akasheh, Esraa Faisal Malik, O. Hujran","doi":"10.1109/SNAMS58071.2022.10062500","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062500","url":null,"abstract":"Data mining methods have been employed successfully in several industries, including education, where they are known as educational data mining methods. Educational data mining aims to extract in-depth knowledge from raw data to build automated systems that could be used in the educational sector. With the advancement of data mining technologies, it is now possible to mine educational data to enhance educational practices. This study, therefore, uses educational data mining techniques to predict the final grades of secondary school students. This study has employed several Machine Learning (ML) algorithms, such as classification trees, regression trees, logistic Regression, and Multiple Regression. In addition, the R programming language was used to develop the prediction models. The dataset used in this study was obtained from two secondary schools in Portugal. According to the findings, classification trees and logistic Regression fared better than regression trees and multiple Regression.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124374799","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}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062664
D. Bernhauer
The use of deep convolutional neural networks is very common in computer graphics. With this, methods for exploiting knowledge in other fields are also developing. Finding plagiarism among student source codes is challenging, especially when students have the same assignment. In this case, we try to find differences between two semantically identical codes at the level of syntax, approach, or just style. This paper aims to visualize binary codes and verify if it is possible to detect plagiarism using deep convolution neural networks. Using the siamese network, we trained a neural network to evaluate the similarity between the two programs. The training data for our network are the ICPC competition submissions for which we can be confident of their authorship. The overall success rate of our model consistently reaches 75 to 80 % accuracy, which mainly shows that the visualization of inherently non-graphical entities (like source code) can be useful in the application of neural networks designed primarily for graphical purposes.
{"title":"Code Visualization for Plagiarism Detection","authors":"D. Bernhauer","doi":"10.1109/SNAMS58071.2022.10062664","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062664","url":null,"abstract":"The use of deep convolutional neural networks is very common in computer graphics. With this, methods for exploiting knowledge in other fields are also developing. Finding plagiarism among student source codes is challenging, especially when students have the same assignment. In this case, we try to find differences between two semantically identical codes at the level of syntax, approach, or just style. This paper aims to visualize binary codes and verify if it is possible to detect plagiarism using deep convolution neural networks. Using the siamese network, we trained a neural network to evaluate the similarity between the two programs. The training data for our network are the ICPC competition submissions for which we can be confident of their authorship. The overall success rate of our model consistently reaches 75 to 80 % accuracy, which mainly shows that the visualization of inherently non-graphical entities (like source code) can be useful in the application of neural networks designed primarily for graphical purposes.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132503810","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}
Pub Date : 2022-11-29DOI: 10.1109/SNAMS58071.2022.10062684
Orven E. Llantos, Sherwyn P. Florin, Van Michael S. Ranque
Pieces of literature discussing the process model in the learning management system are limited to student and teacher learning interactions. Including the learning interactions of principals and parents contributes to more detail of processes taking place during learning interactions on the platform. The study used process mining techniques and algorithms to extract the underlying processes that drive learning interactions in social learning management systems. The discovered processes for principals, teachers, students, and parents consequently show a precision value of 1, 0.542, 0.639, and 1, respectively. The preciseness of processes for each user group indicates an acceptable behavior (> 0.50) extracted from the event logs. On the other hand, social networks form from the processes that show the information flow of learning interactions from the principal to the students and parents, depicting everyone's effort for learning gain in favor of the student. This study's contribution expands beyond teacher-student interaction processes to include principals and parents, thereby generating a more concrete view of learning interaction in the social learning management system.
{"title":"A Process Mining Approach In Discovering Processes And Social Networks In My.Eskwela","authors":"Orven E. Llantos, Sherwyn P. Florin, Van Michael S. Ranque","doi":"10.1109/SNAMS58071.2022.10062684","DOIUrl":"https://doi.org/10.1109/SNAMS58071.2022.10062684","url":null,"abstract":"Pieces of literature discussing the process model in the learning management system are limited to student and teacher learning interactions. Including the learning interactions of principals and parents contributes to more detail of processes taking place during learning interactions on the platform. The study used process mining techniques and algorithms to extract the underlying processes that drive learning interactions in social learning management systems. The discovered processes for principals, teachers, students, and parents consequently show a precision value of 1, 0.542, 0.639, and 1, respectively. The preciseness of processes for each user group indicates an acceptable behavior (> 0.50) extracted from the event logs. On the other hand, social networks form from the processes that show the information flow of learning interactions from the principal to the students and parents, depicting everyone's effort for learning gain in favor of the student. This study's contribution expands beyond teacher-student interaction processes to include principals and parents, thereby generating a more concrete view of learning interaction in the social learning management system.","PeriodicalId":371668,"journal":{"name":"2022 Ninth International Conference on Social Networks Analysis, Management and Security (SNAMS)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134063948","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}
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