Almost all academic studies include a literature review section. This section is of significance in terms of presenting the value of the suggested method of the researcher and making comparisons. Due to the increasing number of academic papers and the emergence of various directories and indices, the time spent for finding the related previous studies is an important period for the researcher, which consumes a significant amount of time. By means of the suggested method, researchers can access various types of featured publications related to the keyword from different years from a single address. The system also helps to reveal an exemplary and guiding literature review among the found publications by conducting a text generation. The system uses the TF-IDF method for keyword-based publication search and “Template-Based Text Generation” method for the text generation algorithm. In the study, the largest open-access journal platform, TÜBİTAK Dergipark and SOBIAD Citation Index were used as the data set. As a result of the conducted tests, a method that supports the literature review process, even helping to the writing of literature review, was suggested. Along with the fact that there has not been an equivalent of the suggested study, the comparisons for success, “Text Generation” and “Literature Review” were independently calculated and presented.
{"title":"Text Generation with Diversified Source Literature Review","authors":"A. Müngen, Emre Dogan, Mehmet Kaya","doi":"10.1145/3341161.3343510","DOIUrl":"https://doi.org/10.1145/3341161.3343510","url":null,"abstract":"Almost all academic studies include a literature review section. This section is of significance in terms of presenting the value of the suggested method of the researcher and making comparisons. Due to the increasing number of academic papers and the emergence of various directories and indices, the time spent for finding the related previous studies is an important period for the researcher, which consumes a significant amount of time. By means of the suggested method, researchers can access various types of featured publications related to the keyword from different years from a single address. The system also helps to reveal an exemplary and guiding literature review among the found publications by conducting a text generation. The system uses the TF-IDF method for keyword-based publication search and “Template-Based Text Generation” method for the text generation algorithm. In the study, the largest open-access journal platform, TÜBİTAK Dergipark and SOBIAD Citation Index were used as the data set. As a result of the conducted tests, a method that supports the literature review process, even helping to the writing of literature review, was suggested. Along with the fact that there has not been an equivalent of the suggested study, the comparisons for success, “Text Generation” and “Literature Review” were independently calculated and presented.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126307790","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}
Arpita Chandra, Z. Borbora, P. Kumaraguru, J. Srivastava
Social dynamics are based on human needs for trust, support, resource sharing, irrespective of whether they operate in real life or in a virtual setting. Massively multiplayer online role-playing games (MMORPGS) serve as enablers of leisurely social activity and are important tools for social interactions. Past research has shown that socially dense gaming environments like MMORPGs can be used to study important social phenomena, which may operate in real life, too. We describe the process of social exploration to entail the following components 1) finding the balance between personal and social time 2) making choice between a large number of weak ties or few strong social ties. 3) finding a social group. In general, these are the major determinants of an individual's social life. This paper looks into the phenomenon of social exploration in an activity based online social environment. We study this process through the lens of the following research questions, 1) What are the different social behavior types? 2) Is there a change in a player's social behavior over time? 3) Are certain social behaviors more stable than the others? 4) Can longitudinal research of player behavior help shed light on the social dynamics and processes in the network? We use an unsupervised machine learning approach to come up with 4 different social behavior types - Lone Wolf, Pack Wolf of Small Pack, Pack Wolf of a Large Pack and Social Butterfly. The types represent the degree of socialization of players in the game. Our research reveals that social behaviors change with time. While lone wolf and pack wolf of small pack are more stable social behaviors, pack wolf of large pack and social butterflies are more transient. We also observe that players progressively move from large groups with weak social ties to settle in small groups with stronger ties.
{"title":"Finding Your Social Space: Empirical Study of Social Exploration in Multiplayer Online Games","authors":"Arpita Chandra, Z. Borbora, P. Kumaraguru, J. Srivastava","doi":"10.1145/3341161.3345333","DOIUrl":"https://doi.org/10.1145/3341161.3345333","url":null,"abstract":"Social dynamics are based on human needs for trust, support, resource sharing, irrespective of whether they operate in real life or in a virtual setting. Massively multiplayer online role-playing games (MMORPGS) serve as enablers of leisurely social activity and are important tools for social interactions. Past research has shown that socially dense gaming environments like MMORPGs can be used to study important social phenomena, which may operate in real life, too. We describe the process of social exploration to entail the following components 1) finding the balance between personal and social time 2) making choice between a large number of weak ties or few strong social ties. 3) finding a social group. In general, these are the major determinants of an individual's social life. This paper looks into the phenomenon of social exploration in an activity based online social environment. We study this process through the lens of the following research questions, 1) What are the different social behavior types? 2) Is there a change in a player's social behavior over time? 3) Are certain social behaviors more stable than the others? 4) Can longitudinal research of player behavior help shed light on the social dynamics and processes in the network? We use an unsupervised machine learning approach to come up with 4 different social behavior types - Lone Wolf, Pack Wolf of Small Pack, Pack Wolf of a Large Pack and Social Butterfly. The types represent the degree of socialization of players in the game. Our research reveals that social behaviors change with time. While lone wolf and pack wolf of small pack are more stable social behaviors, pack wolf of large pack and social butterflies are more transient. We also observe that players progressively move from large groups with weak social ties to settle in small groups with stronger ties.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130457310","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}
Seyed Amin Mirlohi Falavarjani, E. Bagheri, Ssu Yu Zoe Chou, J. Jovanović, A. Ghorbani
Research in social network analytics has already extensively explored how engagement on online social networks can lead to observable effects on users' real-world behavior (e.g., changing exercising patterns or dietary habits), and their psychological states. The objective of our work in this paper is to investigate the flip-side and examine whether engaging in or disengaging from real-world activities would reflect itself in users' affective processes such as anger, anxiety, and sadness, as expressed in users' posts on online social media. We have collected data from Foursquare and Twitter and found that engaging in or disengaging from a real-world activity, such as frequenting at bars or stopping going to a gym, have direct impact on the users' affective processes. In particular, we report that engaging in a routine real-world activity leads to expressing less emotional content online, whereas the reverse is observed when users abandon a regular real-world activity.
{"title":"On the Causal Relation between Users' Real-World Activities and their Affective Processes","authors":"Seyed Amin Mirlohi Falavarjani, E. Bagheri, Ssu Yu Zoe Chou, J. Jovanović, A. Ghorbani","doi":"10.1145/3341161.3342918","DOIUrl":"https://doi.org/10.1145/3341161.3342918","url":null,"abstract":"Research in social network analytics has already extensively explored how engagement on online social networks can lead to observable effects on users' real-world behavior (e.g., changing exercising patterns or dietary habits), and their psychological states. The objective of our work in this paper is to investigate the flip-side and examine whether engaging in or disengaging from real-world activities would reflect itself in users' affective processes such as anger, anxiety, and sadness, as expressed in users' posts on online social media. We have collected data from Foursquare and Twitter and found that engaging in or disengaging from a real-world activity, such as frequenting at bars or stopping going to a gym, have direct impact on the users' affective processes. In particular, we report that engaging in a routine real-world activity leads to expressing less emotional content online, whereas the reverse is observed when users abandon a regular real-world activity.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130571928","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}
Community detection looks for groups of nodes in networks, mainly using network topological, link-based features, not taking into account features associated with each node. Clustering algorithms, on the other hand, look for groups of objects using features describing each object. Recently, link features and node attributes have been combined to improve community detection. Community detection methods can be designed to identify communities that are disjoint or overlapping, crisp or soft and static or dynamic. In this paper, we propose a dynamic community detection method for finding soft overlapping groups in temporal networks with node attributes. Our approach is based on a non-negative matrix factorization model that uses automatic relevance determination to detect the number of communities. Preliminary results on toy and artificial networks, are promising. To the extent of our knowledge, a dynamic approach that includes link and node information, for soft overlapping community detection, has not been proposed before.
{"title":"A non-negative matrix factorization approach to update communities in temporal networks using node features","authors":"Renny Márquez, R. Weber, A. Carvalho","doi":"10.1145/3341161.3343677","DOIUrl":"https://doi.org/10.1145/3341161.3343677","url":null,"abstract":"Community detection looks for groups of nodes in networks, mainly using network topological, link-based features, not taking into account features associated with each node. Clustering algorithms, on the other hand, look for groups of objects using features describing each object. Recently, link features and node attributes have been combined to improve community detection. Community detection methods can be designed to identify communities that are disjoint or overlapping, crisp or soft and static or dynamic. In this paper, we propose a dynamic community detection method for finding soft overlapping groups in temporal networks with node attributes. Our approach is based on a non-negative matrix factorization model that uses automatic relevance determination to detect the number of communities. Preliminary results on toy and artificial networks, are promising. To the extent of our knowledge, a dynamic approach that includes link and node information, for soft overlapping community detection, has not been proposed before.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132456889","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}
Twitter provides important information for emergency responders in the rescue process during disasters. However, tweets containing relevant information are sparse and are usually hidden in a vast set of noisy contents. This leads to inherent challenges in generating suitable training data that are required for neural network models. In this paper, we study the problem of retrieving the infrastructure damage information from tweets generated from different location during crisis using the model actively trained on past but similar events. We combine RNN and GRU based model coupled with active learning that gets trained on most uncertain samples and captures the latent features of different data distribution. It reduces the uses of around 90% less training data, thereby significantly reducing the manual annotation efforts. We use the model pre-trained using active learning based approach to retrieve the infrastructure damage tweets originated from different regions. We obtain a minimum of 18% gain on F1-measure and considerably on other metrics over recent state-of-the-art IR techniques.
{"title":"Identifying Infrastructure Damage during Earthquake using Deep Active Learning","authors":"S. Priya, Saharsh Singh, Sourav Kumar Dandapat, Kripabandhu Ghosh, Joydeep Chandra","doi":"10.1145/3341161.3342955","DOIUrl":"https://doi.org/10.1145/3341161.3342955","url":null,"abstract":"Twitter provides important information for emergency responders in the rescue process during disasters. However, tweets containing relevant information are sparse and are usually hidden in a vast set of noisy contents. This leads to inherent challenges in generating suitable training data that are required for neural network models. In this paper, we study the problem of retrieving the infrastructure damage information from tweets generated from different location during crisis using the model actively trained on past but similar events. We combine RNN and GRU based model coupled with active learning that gets trained on most uncertain samples and captures the latent features of different data distribution. It reduces the uses of around 90% less training data, thereby significantly reducing the manual annotation efforts. We use the model pre-trained using active learning based approach to retrieve the infrastructure damage tweets originated from different regions. We obtain a minimum of 18% gain on F1-measure and considerably on other metrics over recent state-of-the-art IR techniques.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115227336","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 spread of information on Facebook and Twitter is much more efficient than on traditional social media platforms. For word-of-mouth (WOM) marketing, social media have become a rich information source for companies or scholars to design models to examine this repository and mine useful insights for marketing strategies. However, social media language is relatively short and contains special words and symbols. Most natural language processing (NLP) methods focus on processing formal sentences and are not well-suited to such short messages. In this study we propose a novel sentiment analysis framework based on deep learning models to extract sentiment from social media. We collect data from which we compile a dataset. After processing these special terms, we seek to establish a semantic dataset for further research. The extracted information will be useful for many future applications. The experimental data have been obtained by crawling several social media platforms.
{"title":"Deep Learning for Automated Sentiment Analysis of Social Media","authors":"L. Cheng, Song-Lin Tsai","doi":"10.1145/3341161.3344821","DOIUrl":"https://doi.org/10.1145/3341161.3344821","url":null,"abstract":"The spread of information on Facebook and Twitter is much more efficient than on traditional social media platforms. For word-of-mouth (WOM) marketing, social media have become a rich information source for companies or scholars to design models to examine this repository and mine useful insights for marketing strategies. However, social media language is relatively short and contains special words and symbols. Most natural language processing (NLP) methods focus on processing formal sentences and are not well-suited to such short messages. In this study we propose a novel sentiment analysis framework based on deep learning models to extract sentiment from social media. We collect data from which we compile a dataset. After processing these special terms, we seek to establish a semantic dataset for further research. The extracted information will be useful for many future applications. The experimental data have been obtained by crawling several social media platforms.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122482413","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}
Cybersecurity event detection is a crucial problem for mitigating effects on various aspects of society. Social media has become a notable source of indicators for detection of diverse events. Though previous social media based strategies for cyber-security event detection focus on mining certain event-related words, the dynamic and evolving nature of online discourse limits the performance of these approaches. Further, because these are typically unsupervised or weakly supervised learning strategies, they do not perform well in an environment of biased samples, noisy context, and informal language which is routine for online, user-generated content. This paper takes a supervised learning approach by proposing a novel multi-task learning based model. Our model can handle diverse structures in feature space by learning models for different types of potential high-profile targets simultaneously. For parameter optimization, we develop an efficient algorithm based on the alternating direction method of multipliers. Through extensive experiments on a real world Twitter dataset, we demonstrate that our approach consistently outperforms existing methods at encoding and identifying cyber-security incidents.
{"title":"Feature Driven Learning Framework for Cybersecurity Event Detection","authors":"Taoran Ji, Xuchao Zhang, Nathan Self, Kaiqun Fu, Chang-Tien Lu, Naren Ramakrishnan","doi":"10.1145/3341161.3342871","DOIUrl":"https://doi.org/10.1145/3341161.3342871","url":null,"abstract":"Cybersecurity event detection is a crucial problem for mitigating effects on various aspects of society. Social media has become a notable source of indicators for detection of diverse events. Though previous social media based strategies for cyber-security event detection focus on mining certain event-related words, the dynamic and evolving nature of online discourse limits the performance of these approaches. Further, because these are typically unsupervised or weakly supervised learning strategies, they do not perform well in an environment of biased samples, noisy context, and informal language which is routine for online, user-generated content. This paper takes a supervised learning approach by proposing a novel multi-task learning based model. Our model can handle diverse structures in feature space by learning models for different types of potential high-profile targets simultaneously. For parameter optimization, we develop an efficient algorithm based on the alternating direction method of multipliers. Through extensive experiments on a real world Twitter dataset, we demonstrate that our approach consistently outperforms existing methods at encoding and identifying cyber-security incidents.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116497653","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}
Sandra Mitrovic, Laurent Lecoutere, Jochen De Weerdt
In many real-world networks, it is important to explicitly differentiate between positive and negative links, thus considering the observed networks as signed. To derive useful features, just as in the case of unsigned networks, representation learning can be used to learn meaningful representations of a network that characterize its underlying topology. Several methods for learning representations on signed networks have already been proposed but have not been systematically benchmarked together before. Hence, in this paper, we bridge this literature gap providing a quantitative and qualitative benchmark of the four most prominent representation learning methods for signed networks. Results on three different datasets for link sign prediction showcase the superiority of the StEM method over its competitors both from a predictive performance and runtime perspective.
{"title":"A Comparison of Methods for Link Sign Prediction with Signed Network Embeddings","authors":"Sandra Mitrovic, Laurent Lecoutere, Jochen De Weerdt","doi":"10.1145/3341161.3345335","DOIUrl":"https://doi.org/10.1145/3341161.3345335","url":null,"abstract":"In many real-world networks, it is important to explicitly differentiate between positive and negative links, thus considering the observed networks as signed. To derive useful features, just as in the case of unsigned networks, representation learning can be used to learn meaningful representations of a network that characterize its underlying topology. Several methods for learning representations on signed networks have already been proposed but have not been systematically benchmarked together before. Hence, in this paper, we bridge this literature gap providing a quantitative and qualitative benchmark of the four most prominent representation learning methods for signed networks. Results on three different datasets for link sign prediction showcase the superiority of the StEM method over its competitors both from a predictive performance and runtime perspective.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127596695","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}
Justin Song, Valerie Spicer, Andrew J. Park, Herbert H. Tsang, P. Brantingham
The structure of the urban setting determines the crime patterns. This research explores the street profile analysis which is a new method for analyzing crime in relation to street networks. Street profile analysis can be used to identify crime surges or heavy concentrations of crime along roadways. In this study, the street profile technique is combined with a discrete calculus approach to locate the boundaries of small criminal spaces in the City of Vancouver, British Columbia, Canada. This experimental technique utilizes open source property crime data from the Vancouver Police Department to analyze crime patterns within Vancouver. This computational crime analysis technique is described in detail and the utility of this technique explored. The new technique is a valuable tool for the intelligence and security informatics communities.
{"title":"Computational Method for Identifying the Boundaries of Crime with Street Profile and Discrete Calculus","authors":"Justin Song, Valerie Spicer, Andrew J. Park, Herbert H. Tsang, P. Brantingham","doi":"10.1145/3341161.3343537","DOIUrl":"https://doi.org/10.1145/3341161.3343537","url":null,"abstract":"The structure of the urban setting determines the crime patterns. This research explores the street profile analysis which is a new method for analyzing crime in relation to street networks. Street profile analysis can be used to identify crime surges or heavy concentrations of crime along roadways. In this study, the street profile technique is combined with a discrete calculus approach to locate the boundaries of small criminal spaces in the City of Vancouver, British Columbia, Canada. This experimental technique utilizes open source property crime data from the Vancouver Police Department to analyze crime patterns within Vancouver. This computational crime analysis technique is described in detail and the utility of this technique explored. The new technique is a valuable tool for the intelligence and security informatics communities.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116761422","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}
Hankyu Jang, Samuel Justice, P. Polgreen, Alberto Maria Segre, Daniel K. Sewell, S. Pemmaraju
This paper presents a high-fidelity agent-based simulation of the spread of methicillin-resistant Staphylococcus aureus (MRSA), a serious hospital acquired infection, within the dialysis unit at the University of Iowa Hospitals and Clinics (UIHC). The simulation is based on ten days of fine-grained healthcare worker (HCW) movement and interaction data collected from a sensor mote instrumentation of the dialysis unit by our research group in the fall of 2013. The simulation layers a detailed model of MRSA pathogen transfer, die-off, shedding, and infection on top of agent interactions obtained from data. The specific question this paper focuses on is whether there are simple, inexpensive architectural or process changes one can make in the dialysis unit to reduce the spread of MRSA? We evaluate two architectural changes of the nurses' station: (i) splitting the central nurses' station into two smaller distinct nurses' stations, and (ii) doubling the surface area of the nursing station. The first architectural change is modeled as a graph partitioning problem on a HCW contact network obtained from our HCW movement data. Somewhat counter-intuitively, our results suggest that the first architectural modification and the resulting reduction in HCW-HCW contacts has little to noeffect on the spread of MRSA and may in fact lead to an increase in MRSA infection counts in some cases. In contrast, the second modification leads to a substantial reduction - between 12% and 22% for simulations with different parameters - in the number of patients infected by MRSA. These results suggest that the dynamics of an environmentally mediated infection such as MRSA may be quite different from that of infections whose spread is not substantially affected by the environment (e.g., respiratory infections or influenza).
{"title":"Evaluating Architectural Changes to Alter Pathogen Dynamics in a Dialysis Unit","authors":"Hankyu Jang, Samuel Justice, P. Polgreen, Alberto Maria Segre, Daniel K. Sewell, S. Pemmaraju","doi":"10.1145/3341161.3343515","DOIUrl":"https://doi.org/10.1145/3341161.3343515","url":null,"abstract":"This paper presents a high-fidelity agent-based simulation of the spread of methicillin-resistant Staphylococcus aureus (MRSA), a serious hospital acquired infection, within the dialysis unit at the University of Iowa Hospitals and Clinics (UIHC). The simulation is based on ten days of fine-grained healthcare worker (HCW) movement and interaction data collected from a sensor mote instrumentation of the dialysis unit by our research group in the fall of 2013. The simulation layers a detailed model of MRSA pathogen transfer, die-off, shedding, and infection on top of agent interactions obtained from data. The specific question this paper focuses on is whether there are simple, inexpensive architectural or process changes one can make in the dialysis unit to reduce the spread of MRSA? We evaluate two architectural changes of the nurses' station: (i) splitting the central nurses' station into two smaller distinct nurses' stations, and (ii) doubling the surface area of the nursing station. The first architectural change is modeled as a graph partitioning problem on a HCW contact network obtained from our HCW movement data. Somewhat counter-intuitively, our results suggest that the first architectural modification and the resulting reduction in HCW-HCW contacts has little to noeffect on the spread of MRSA and may in fact lead to an increase in MRSA infection counts in some cases. In contrast, the second modification leads to a substantial reduction - between 12% and 22% for simulations with different parameters - in the number of patients infected by MRSA. These results suggest that the dynamics of an environmentally mediated infection such as MRSA may be quite different from that of infections whose spread is not substantially affected by the environment (e.g., respiratory infections or influenza).","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"382 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117158435","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：