The Profile of Mood States (POMS) and its variations have been used in many real world contexts to assess individuals behavior and measure mood. Social Networks such as Twitter and Facebook are considered precious research sources of collecting user mood measurements. In particular, we are inspired in this paper, by recent work on the prediction of the stock market movement from attributes representing the public mood collected from Twitter. In this paper, we build a new prediction model for the same stock market problem based on single models combination. Our proposed approach to build such model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. We implement our approach using Ant Colony Optimization algorithm and we use customized Bayesian Classifiers as single models. We compare our approach against the best Bayesian single model, model learned from all the available data, bagging and boosting algorithms. Test results indicate that the proposed model for stock market prediction performs better than those derived by alternatives approaches.
{"title":"Ant colony based approach to predict stock market movement from mood collected on Twitter","authors":"S. Bouktif, M. Awad","doi":"10.1145/2492517.2500282","DOIUrl":"https://doi.org/10.1145/2492517.2500282","url":null,"abstract":"The Profile of Mood States (POMS) and its variations have been used in many real world contexts to assess individuals behavior and measure mood. Social Networks such as Twitter and Facebook are considered precious research sources of collecting user mood measurements. In particular, we are inspired in this paper, by recent work on the prediction of the stock market movement from attributes representing the public mood collected from Twitter. In this paper, we build a new prediction model for the same stock market problem based on single models combination. Our proposed approach to build such model is simultaneously promoting performance and interpretability. By interpretability, we mean the ability of a model to explain its predictions. We implement our approach using Ant Colony Optimization algorithm and we use customized Bayesian Classifiers as single models. We compare our approach against the best Bayesian single model, model learned from all the available data, bagging and boosting algorithms. Test results indicate that the proposed model for stock market prediction performs better than those derived by alternatives approaches.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114606909","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}
Information propagation in a microblog network aims to identify a set of seed users for propagating a target message to as many interested users as possible. This problem differs from the traditional influence maximization in two major ways: it has a content-rich target message for propagation and it treats each link in the network as communication on certain topics and emphasizes the topic relevance of such communication in propagating the target message. In realistic situations, however, the topics associated with a link are not explicitly expressed but are hidden in the microblogs previously exchanged through the link. In this paper, we present a topic-aware solution to information propagation in a microblog network. We first model the latent topic structure of the network using observed microblog messages published in the network. We then present two methods for estimating the propagation probability based on the topic relevance between a link and the target message. Once the propagation probability is estimated, we adopt the standard greedy algorithm for influence maximization to find seed users. This approach is topic-aware in that the target message finds its way of propagation according to its topic relevance to the latent topic structure in the network. Experiments conducted on real Twitter datasets suggest that the proposed methods are able to select right seed users.
{"title":"Information propagation in microblog networks","authors":"Chenyi Zhang, Jianling Sun, Ke Wang","doi":"10.1145/2492517.2492608","DOIUrl":"https://doi.org/10.1145/2492517.2492608","url":null,"abstract":"Information propagation in a microblog network aims to identify a set of seed users for propagating a target message to as many interested users as possible. This problem differs from the traditional influence maximization in two major ways: it has a content-rich target message for propagation and it treats each link in the network as communication on certain topics and emphasizes the topic relevance of such communication in propagating the target message. In realistic situations, however, the topics associated with a link are not explicitly expressed but are hidden in the microblogs previously exchanged through the link. In this paper, we present a topic-aware solution to information propagation in a microblog network. We first model the latent topic structure of the network using observed microblog messages published in the network. We then present two methods for estimating the propagation probability based on the topic relevance between a link and the target message. Once the propagation probability is estimated, we adopt the standard greedy algorithm for influence maximization to find seed users. This approach is topic-aware in that the target message finds its way of propagation according to its topic relevance to the latent topic structure in the network. Experiments conducted on real Twitter datasets suggest that the proposed methods are able to select right seed users.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128509782","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}
Yang Yang, N. Chawla, P. Basu, Bhaskar Prabhala, T. L. Porta
The understanding of how humans move is a long-standing challenge in the natural science. An important question is, to what degree is human behavior predictable? The ability to foresee the mobility of humans is crucial from predicting the spread of human to urban planning. Previous research has focused on predicting individual mobility behavior, such as the next location prediction problem. In this paper we study the human mobility behaviors from the perspective of network science. In the human mobility network, there will be a link between two humans if they are physically proximal to each other. We perform both microscopic and macroscopic explorations on the human mobility patterns. From the microscopic perspective, our objective is to answer whether two humans will be in proximity of each other or not. While from the macroscopic perspective, we are interested in whether we can infer the future topology of the human mobility network. In this paper we explore both problems by using link prediction technology, our methodology is demonstrated to have a greater degree of precision in predicting future mobility topology.
{"title":"Link prediction in human mobility networks","authors":"Yang Yang, N. Chawla, P. Basu, Bhaskar Prabhala, T. L. Porta","doi":"10.1145/2492517.2492656","DOIUrl":"https://doi.org/10.1145/2492517.2492656","url":null,"abstract":"The understanding of how humans move is a long-standing challenge in the natural science. An important question is, to what degree is human behavior predictable? The ability to foresee the mobility of humans is crucial from predicting the spread of human to urban planning. Previous research has focused on predicting individual mobility behavior, such as the next location prediction problem. In this paper we study the human mobility behaviors from the perspective of network science. In the human mobility network, there will be a link between two humans if they are physically proximal to each other. We perform both microscopic and macroscopic explorations on the human mobility patterns. From the microscopic perspective, our objective is to answer whether two humans will be in proximity of each other or not. While from the macroscopic perspective, we are interested in whether we can infer the future topology of the human mobility network. In this paper we explore both problems by using link prediction technology, our methodology is demonstrated to have a greater degree of precision in predicting future mobility topology.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128680947","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}
Nowadays, most people care about their personal health, no matter mental or physical health in their daily life. They sustain and improve their health status with exercise, diet control, adopt good sleep habit, keep natural patterns on sleeping and bowel movement. These people need a tool for monitor and record long-termly their own health status. On the other hand, some people do not see they need to change their health related lifestyle to improve their health status until they are diagnosed with diseases. While he/she is sick, he/she also need to write down their health diary he/herself or the caregiver (most of them are the disadvantaged in their family) for the physician to monitor the illness. In this paper we proposed a social network service named HowCare, a caregiver based social support online community, with a personal health cloud archive and its unique designs with “HealthRank” algorithm to match caregiver's social network with correlated illness situation they face to. The aim of HowCare are, to help people keep their own health data on the cloud and allows patients or caregiver with the same disease to interact with each other, and through the social network and telehealth design, it will influence the patient's willingness to accept healthier life and improve health status.
{"title":"Howcare: A personal health cloud archive and care-partners' community","authors":"Liang-Cheng Huang, Wei-Chung Liu, S. Chou","doi":"10.1145/2492517.2500237","DOIUrl":"https://doi.org/10.1145/2492517.2500237","url":null,"abstract":"Nowadays, most people care about their personal health, no matter mental or physical health in their daily life. They sustain and improve their health status with exercise, diet control, adopt good sleep habit, keep natural patterns on sleeping and bowel movement. These people need a tool for monitor and record long-termly their own health status. On the other hand, some people do not see they need to change their health related lifestyle to improve their health status until they are diagnosed with diseases. While he/she is sick, he/she also need to write down their health diary he/herself or the caregiver (most of them are the disadvantaged in their family) for the physician to monitor the illness. In this paper we proposed a social network service named HowCare, a caregiver based social support online community, with a personal health cloud archive and its unique designs with “HealthRank” algorithm to match caregiver's social network with correlated illness situation they face to. The aim of HowCare are, to help people keep their own health data on the cloud and allows patients or caregiver with the same disease to interact with each other, and through the social network and telehealth design, it will influence the patient's willingness to accept healthier life and improve health status.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128520287","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 current practice of designing microfluidic Lab-on-a-Chip (LoCs) limits reusing designs and makes sharing tasks among researchers difficult. One way to achieve that objective is to borrow best practices from engineering. Also it takes a lot of skills to design LoCs. Design-by-assembly in which a LoC can be designed by configuring, laying out subsystems can help new researchers to develop custom chips. Flexible, reusable, and rapid-prototyping-feasible LoC designs can be achieved by fabricated modular microfluidic blocks. However, challenging problems still persist, which limit the usefulness of prefabricated blocks. We propose software microfluidic modules (SoftMABs) based design technique to solve issues fabricated modules face. By configuring SoftMABs, integrating them, the new assembly of SoftMABs can form a 3D LoC design ready to be prototyped. The proposed method can make designing a complex LoC less challenging, and collaborating among laboratories easier. We created SoftMABs and designed a custom microfluidic chip by assembling SoftMABs like LEGOs, dragging-and-dropping them. Later we reconfigured them - by replacing a SoftMAB with another module - to make a new LoC. We believe this computer-aided method is an interesting and useful LoC design technique.
{"title":"Lab-on-a-Chip turns soft: Computer-aided, software-enabled microfluidics design","authors":"A. K. Soe, M. Fielding, S. Nahavandi","doi":"10.1145/2492517.2500230","DOIUrl":"https://doi.org/10.1145/2492517.2500230","url":null,"abstract":"The current practice of designing microfluidic Lab-on-a-Chip (LoCs) limits reusing designs and makes sharing tasks among researchers difficult. One way to achieve that objective is to borrow best practices from engineering. Also it takes a lot of skills to design LoCs. Design-by-assembly in which a LoC can be designed by configuring, laying out subsystems can help new researchers to develop custom chips. Flexible, reusable, and rapid-prototyping-feasible LoC designs can be achieved by fabricated modular microfluidic blocks. However, challenging problems still persist, which limit the usefulness of prefabricated blocks. We propose software microfluidic modules (SoftMABs) based design technique to solve issues fabricated modules face. By configuring SoftMABs, integrating them, the new assembly of SoftMABs can form a 3D LoC design ready to be prototyped. The proposed method can make designing a complex LoC less challenging, and collaborating among laboratories easier. We created SoftMABs and designed a custom microfluidic chip by assembling SoftMABs like LEGOs, dragging-and-dropping them. Later we reconfigured them - by replacing a SoftMAB with another module - to make a new LoC. We believe this computer-aided method is an interesting and useful LoC design technique.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130517991","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}
We show that the language used by U.S. presidential candidates over the past twenty years has an underlying temporal structure associated with electoral success, with the most influential language used by incumbents in their second campaign and the least by losers in a first-cycle open campaign. Influential language is characterized by increased positivity, complete absence of negativity, increased abstraction, and lack of reference to the opposing candidate(s). The way in which language use changes suggests that it is the result of changing self-perception rather than a deliberate strategy. This has implications for the language of influence as deployed by violent extremist groups, suggesting that both success at convincing an audience to participate in violent extremism and the presence of competing groups trying to make similar arguments improve the quality of the influencing language they use.
{"title":"Improving the language of influence","authors":"D. Skillicorn, C. Leuprecht","doi":"10.1145/2492517.2500285","DOIUrl":"https://doi.org/10.1145/2492517.2500285","url":null,"abstract":"We show that the language used by U.S. presidential candidates over the past twenty years has an underlying temporal structure associated with electoral success, with the most influential language used by incumbents in their second campaign and the least by losers in a first-cycle open campaign. Influential language is characterized by increased positivity, complete absence of negativity, increased abstraction, and lack of reference to the opposing candidate(s). The way in which language use changes suggests that it is the result of changing self-perception rather than a deliberate strategy. This has implications for the language of influence as deployed by violent extremist groups, suggesting that both success at convincing an audience to participate in violent extremism and the presence of competing groups trying to make similar arguments improve the quality of the influencing language they use.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129834791","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}
Existing approaches to predicting tie strength between users involve either online social networks or location-based social networks. To date, few studies combined these networks to investigate the intensity of social relations between users. In this paper we analyzed tie strength defined as partners and acquaintances in two domains: a location-based social network and an online social network (Second Life). We compared user pairs in terms of their partnership and found significant differences between partners and acquaintances. Following these observations, we evaluated the social proximity of users via supervised and unsupervised learning algorithms and established that homophilic features were most valuable for the prediction of partnership.
{"title":"Acquaintance or partner? Predicting partnership in online and location-based social networks","authors":"Michael Steurer, C. Trattner","doi":"10.1145/2492517.2492562","DOIUrl":"https://doi.org/10.1145/2492517.2492562","url":null,"abstract":"Existing approaches to predicting tie strength between users involve either online social networks or location-based social networks. To date, few studies combined these networks to investigate the intensity of social relations between users. In this paper we analyzed tie strength defined as partners and acquaintances in two domains: a location-based social network and an online social network (Second Life). We compared user pairs in terms of their partnership and found significant differences between partners and acquaintances. Following these observations, we evaluated the social proximity of users via supervised and unsupervised learning algorithms and established that homophilic features were most valuable for the prediction of partnership.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125321611","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 Question Answering (CQA) service enables its users to exchange knowledge in the form of questions and answers. By allowing the users to contribute knowledge, CQA not only satisfies the question askers but also provides valuable references to other users with similar queries. Due to a large volume of questions, not all questions get fully answered. As a result, it can be useful to route a question to a potential answerer. In this paper, we present a question routing scheme which takes into account the answering, commenting and voting propensities of the users. Unlike prior work which focuses on routing a question to the most desirable expert, we focus on routing it to a group of users - who would be willing to collaborate and provide useful answers to that question. Through empirical evidence, we show that more answers and comments are desirable for improving the lasting value of a question-answer thread. As a result, our focus is on routing a question to a team of compatible users.We propose a recommendation model that takes into account the compatibility, topical expertise and availability of the users. Our experiments over a large real-world dataset shows the effectiveness of our approach over several baseline models.
{"title":"Routing questions for collaborative answering in Community Question Answering","authors":"Shuo Chang, Aditya Pal","doi":"10.1145/2492517.2492559","DOIUrl":"https://doi.org/10.1145/2492517.2492559","url":null,"abstract":"Community Question Answering (CQA) service enables its users to exchange knowledge in the form of questions and answers. By allowing the users to contribute knowledge, CQA not only satisfies the question askers but also provides valuable references to other users with similar queries. Due to a large volume of questions, not all questions get fully answered. As a result, it can be useful to route a question to a potential answerer. In this paper, we present a question routing scheme which takes into account the answering, commenting and voting propensities of the users. Unlike prior work which focuses on routing a question to the most desirable expert, we focus on routing it to a group of users - who would be willing to collaborate and provide useful answers to that question. Through empirical evidence, we show that more answers and comments are desirable for improving the lasting value of a question-answer thread. As a result, our focus is on routing a question to a team of compatible users.We propose a recommendation model that takes into account the compatibility, topical expertise and availability of the users. Our experiments over a large real-world dataset shows the effectiveness of our approach over several baseline models.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126079283","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 has emerged as an attractive topic due to the increasing need to understand and manage the networked data of tremendous magnitude. Networked data usually consists of links between the entities and the attributes for describing the entities. Various approaches have been proposed for detecting communities by utilizing the link information and/or attribute information. In this work, we study the problem of community detection for networked data with additional authorship information. By authorship, each entity in the network is authored by another type of entities (e.g., wiki pages are edited by users, products are purchased by customers), to which we refer as authors. Communities of entities are affected by their authors, e.g., two entities that are associated with the same author tend to belong to the same community. Therefore leveraging the authorship information would help us better detect the communities in the networked data. However, it also brings new challenges to community detection. The foremost question is how to model the correlation between communities and authorships. In this work, we address this question by proposing probabilistic models based on the popularity link model [1], which is demonstrated to yield encouraging results for community detection. We employ two methods for modeling the authorships: (i) the first one generates the authorships independently from links by community memberships and popularities of authors by analogy of the popularity link model; (ii) the second one models the links between entities based on authorships together with community memberships and popularities of nodes, which is an analog of previous author-topic model. Upon the basic models, we explore several extensions including (i) we model the community memberships of authors by that of their authored entities to reduce the number of redundant parameters; and (ii) we model the communities memberships of entities and/or authors by their attributes using a discriminative approach. We demonstrate the effectiveness of the proposed models by empirical studies.
{"title":"Community detection by popularity based models for authored networked data","authors":"Tianbao Yang, Prakash Mandayam Comar, Linli Xu","doi":"10.1145/2492517.2492520","DOIUrl":"https://doi.org/10.1145/2492517.2492520","url":null,"abstract":"Community detection has emerged as an attractive topic due to the increasing need to understand and manage the networked data of tremendous magnitude. Networked data usually consists of links between the entities and the attributes for describing the entities. Various approaches have been proposed for detecting communities by utilizing the link information and/or attribute information. In this work, we study the problem of community detection for networked data with additional authorship information. By authorship, each entity in the network is authored by another type of entities (e.g., wiki pages are edited by users, products are purchased by customers), to which we refer as authors. Communities of entities are affected by their authors, e.g., two entities that are associated with the same author tend to belong to the same community. Therefore leveraging the authorship information would help us better detect the communities in the networked data. However, it also brings new challenges to community detection. The foremost question is how to model the correlation between communities and authorships. In this work, we address this question by proposing probabilistic models based on the popularity link model [1], which is demonstrated to yield encouraging results for community detection. We employ two methods for modeling the authorships: (i) the first one generates the authorships independently from links by community memberships and popularities of authors by analogy of the popularity link model; (ii) the second one models the links between entities based on authorships together with community memberships and popularities of nodes, which is an analog of previous author-topic model. Upon the basic models, we explore several extensions including (i) we model the community memberships of authors by that of their authored entities to reduce the number of redundant parameters; and (ii) we model the communities memberships of entities and/or authors by their attributes using a discriminative approach. We demonstrate the effectiveness of the proposed models by empirical studies.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127128112","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}
Shocks to regional, national and global systems stemming from natural hazards, acts of armed violence, terrorism and serious and organized crime have significant defence and security implications. Today, nations face an uncertain and complex security landscape in which threats impact/target the physical, social, economic and cyber domains. For example, acts of terrorism and organized crime are considered one of the greatest threats to national security. In the UK alone, the social and economic costs associated with organized crime are estimated between £20 and £40 billion per year (NCA, 2011:4). Threats to national security, such as that against critical infrastructures not only stem from man-made acts but also from natural hazards. Katrina (2005), Fukushima (2011) and Hurricane Sandy (2012) are examples that highlight the vulnerability of critical infrastructures to natural hazards and the crippling effect they have on the social and economic wellbeing of a community and a nation. With this dynamic and complex threat landscape, network analysis has emerged as a key enabler in supporting defence and security. With the advent of `big data' and increasing processing power, network analysis can reveal insights with regards to structural and dynamic properties thereby facilitating greater understanding of complex networks, their entities, interdependencies and vulnerabilities. This poster paper introduces relevant theoretical frameworks and applications of network analysis in support of the defence and security domain. This paper reflects the body of contributions by leading researchers to an upcoming book entitled: Networks and Network Analysis for Defence and Security, Springer Publishing.
{"title":"Networks and network analysis for defence and security","authors":"A. Masys","doi":"10.1145/2492517.2492602","DOIUrl":"https://doi.org/10.1145/2492517.2492602","url":null,"abstract":"Shocks to regional, national and global systems stemming from natural hazards, acts of armed violence, terrorism and serious and organized crime have significant defence and security implications. Today, nations face an uncertain and complex security landscape in which threats impact/target the physical, social, economic and cyber domains. For example, acts of terrorism and organized crime are considered one of the greatest threats to national security. In the UK alone, the social and economic costs associated with organized crime are estimated between £20 and £40 billion per year (NCA, 2011:4). Threats to national security, such as that against critical infrastructures not only stem from man-made acts but also from natural hazards. Katrina (2005), Fukushima (2011) and Hurricane Sandy (2012) are examples that highlight the vulnerability of critical infrastructures to natural hazards and the crippling effect they have on the social and economic wellbeing of a community and a nation. With this dynamic and complex threat landscape, network analysis has emerged as a key enabler in supporting defence and security. With the advent of `big data' and increasing processing power, network analysis can reveal insights with regards to structural and dynamic properties thereby facilitating greater understanding of complex networks, their entities, interdependencies and vulnerabilities. This poster paper introduces relevant theoretical frameworks and applications of network analysis in support of the defence and security domain. This paper reflects the body of contributions by leading researchers to an upcoming book entitled: Networks and Network Analysis for Defence and Security, Springer Publishing.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127366338","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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