Mobile devices are becoming the integral access point of accessing the Electronic Health Records (EHR). This creates the need to enforce some level of reliability in terms of services accessibility time. However, supporting real-time access and services synchronization in highly distributed mobile environments can be challenging due to the fact that mobile devices rely on wireless communication mediums which can be unstable due to the mobility of the healthcare professionals. As an ongoing joint research with the City Hospital in Saskatoon, Canada, we focus on providing real-time accessibility of the medical record in the mobile environment. We propose a cloud-hosted middleware which performs macro activities such as medical services composition, data hoarding, and medical data events management. The evaluation of the framework, called Med App, shows that medical data dissemination can be achieved in a low-latency fashion.
{"title":"Efficient mobile services consumption in mHealth","authors":"Richard K. Lomotey, R. Deters","doi":"10.1145/2492517.2500279","DOIUrl":"https://doi.org/10.1145/2492517.2500279","url":null,"abstract":"Mobile devices are becoming the integral access point of accessing the Electronic Health Records (EHR). This creates the need to enforce some level of reliability in terms of services accessibility time. However, supporting real-time access and services synchronization in highly distributed mobile environments can be challenging due to the fact that mobile devices rely on wireless communication mediums which can be unstable due to the mobility of the healthcare professionals. As an ongoing joint research with the City Hospital in Saskatoon, Canada, we focus on providing real-time accessibility of the medical record in the mobile environment. We propose a cloud-hosted middleware which performs macro activities such as medical services composition, data hoarding, and medical data events management. The evaluation of the framework, called Med App, shows that medical data dissemination can be achieved in a low-latency fashion.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"103 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":"116298036","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}
Communities are vehicles for efficiently disseminating news, rumors, and opinions in human social networks. Modeling information diffusion through a network can enable us to reach a superior functional understanding of the effect of network structures such as communities on information propagation. The intrinsic assumption is that form follows function-rational actors exercise social choice mechanisms to join communities that best serve their information needs. Particle Swarm Optimization (PSO) was originally designed to simulate aggregate social behavior; our proposed diffusion model, PSODM (Particle Swarm Optimization Diffusion Model) models information flow in a network by creating particle swarms for local network neighborhoods that optimize a continuous version of Holland's hyperplane-defined objective functions. In this paper, we show how our approach differs from prior modeling work in the area and demonstrate that it outperforms existing model-based community detection methods on several social network datasets.
{"title":"Modeling information diffusion and community membership using stochastic optimization","authors":"Alireza Hajibagheri, A. Hamzeh, G. Sukthankar","doi":"10.1145/2492517.2492545","DOIUrl":"https://doi.org/10.1145/2492517.2492545","url":null,"abstract":"Communities are vehicles for efficiently disseminating news, rumors, and opinions in human social networks. Modeling information diffusion through a network can enable us to reach a superior functional understanding of the effect of network structures such as communities on information propagation. The intrinsic assumption is that form follows function-rational actors exercise social choice mechanisms to join communities that best serve their information needs. Particle Swarm Optimization (PSO) was originally designed to simulate aggregate social behavior; our proposed diffusion model, PSODM (Particle Swarm Optimization Diffusion Model) models information flow in a network by creating particle swarms for local network neighborhoods that optimize a continuous version of Holland's hyperplane-defined objective functions. In this paper, we show how our approach differs from prior modeling work in the area and demonstrate that it outperforms existing model-based community detection methods on several social network datasets.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"193 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":"116496877","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}
In this paper, we propose a new SPNR model and identify the concrete propagation relationships and obtain the spreading threshold. We evaluate the proposed model with simulations and compare the simulation results with real data on Sina Weibo, the largest micro-blogging tool in China. The results show that the new model is effective for capturing the rumor spreading in real social networks. To obtain effective rumor control strategy, we further analyze the key factors that affect the maximum value of steady state, the point of decline, and the life cycle of a rumor. These results help us develop new rumor control strategies.
{"title":"A new rumor propagation model and control strategy on social networks","authors":"Yuanyuan Bao, Chengqi Yi, Y. Xue, Yingfei Dong","doi":"10.1145/2492517.2492599","DOIUrl":"https://doi.org/10.1145/2492517.2492599","url":null,"abstract":"In this paper, we propose a new SPNR model and identify the concrete propagation relationships and obtain the spreading threshold. We evaluate the proposed model with simulations and compare the simulation results with real data on Sina Weibo, the largest micro-blogging tool in China. The results show that the new model is effective for capturing the rumor spreading in real social networks. To obtain effective rumor control strategy, we further analyze the key factors that affect the maximum value of steady state, the point of decline, and the life cycle of a rumor. These results help us develop new rumor control strategies.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"33 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":"117066883","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 examine whether the prominence of individuals in different social networks is determined by their position in their local network or by how the community to which they belong relates to other communities. To this end, we introduce two new measures of centrality, both based on communities in the network: local and community centrality. Community centrality is a novel concept that we introduce to describe how central one's community is within the whole network. We introduce an algorithm to estimate the distance between communities and use it to find the centrality of communities. Using data from several social networks, we show that community centrality is able to capture the importance of communities in the whole network. We then conduct a detailed study of different social networks and determine how various global measures of prominence relate to structural centrality measures.Our measures deconstruct global centrality along local and community dimensions. In some cases, prominence is determined almost exclusively by local information, while in others a mix of local and community centrality matters. Our methodology is a step toward understanding of the processes that contribute to an actor's prominence in a network.
{"title":"Deconstructing centrality: Thinking locally and ranking globally in networks","authors":"Sibel Adali, Xiaohui Lu, M. Magdon-Ismail","doi":"10.1145/2492517.2492531","DOIUrl":"https://doi.org/10.1145/2492517.2492531","url":null,"abstract":"We examine whether the prominence of individuals in different social networks is determined by their position in their local network or by how the community to which they belong relates to other communities. To this end, we introduce two new measures of centrality, both based on communities in the network: local and community centrality. Community centrality is a novel concept that we introduce to describe how central one's community is within the whole network. We introduce an algorithm to estimate the distance between communities and use it to find the centrality of communities. Using data from several social networks, we show that community centrality is able to capture the importance of communities in the whole network. We then conduct a detailed study of different social networks and determine how various global measures of prominence relate to structural centrality measures.Our measures deconstruct global centrality along local and community dimensions. In some cases, prominence is determined almost exclusively by local information, while in others a mix of local and community centrality matters. Our methodology is a step toward understanding of the processes that contribute to an actor's prominence in a network.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"65 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":"116115963","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}
Alexander Semenov, Alexander G. Nikolaev, J. Veijalainen
This paper describes traces of user activity around a alleged online social network profile of a Boston Marathon bombing suspect, after the tragedy occurred. The analyzed data, collected with the help of an automatic social media monitoring software, includes the perpetrator's page saved at the time the bombing suspects' names were made public, and the subsequently appearing comments left on that page by other users. The analyses suggest that a timely protection of online media records of a criminal could help prevent a large-scale public spread of communication exchange pertaining to the suspects/criminals' ideas, messages, and connections.
{"title":"Online activity traces around a “Boston bomber”","authors":"Alexander Semenov, Alexander G. Nikolaev, J. Veijalainen","doi":"10.1145/2492517.2500316","DOIUrl":"https://doi.org/10.1145/2492517.2500316","url":null,"abstract":"This paper describes traces of user activity around a alleged online social network profile of a Boston Marathon bombing suspect, after the tragedy occurred. The analyzed data, collected with the help of an automatic social media monitoring software, includes the perpetrator's page saved at the time the bombing suspects' names were made public, and the subsequently appearing comments left on that page by other users. The analyses suggest that a timely protection of online media records of a criminal could help prevent a large-scale public spread of communication exchange pertaining to the suspects/criminals' ideas, messages, and connections.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"3 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":"116789256","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}
Tilman Göhnert, A. Harrer, Tobias Hecking, H. Hoppe
In this paper we introduce the concept of a web-based analytics workbench to support researchers of social networks in their analytic processes. Making explicit these processes allows for sound design, re-use, and automated execution using an authoring system for visual representations of these analytic workflows. The workbench is implemented according to a flexible technical framework in which external and newly-defined analytic components can be integrated and used in conjunction with other analytic components. As a showcase we discuss a complex analytic process.
{"title":"A workbench to construct and re-use network analysis workflows - Concept, implementation, and example case","authors":"Tilman Göhnert, A. Harrer, Tobias Hecking, H. Hoppe","doi":"10.1145/2492517.2492596","DOIUrl":"https://doi.org/10.1145/2492517.2492596","url":null,"abstract":"In this paper we introduce the concept of a web-based analytics workbench to support researchers of social networks in their analytic processes. Making explicit these processes allows for sound design, re-use, and automated execution using an authoring system for visual representations of these analytic workflows. The workbench is implemented according to a flexible technical framework in which external and newly-defined analytic components can be integrated and used in conjunction with other analytic components. As a showcase we discuss a complex analytic process.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"52 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":"114848008","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}
Tomasz Kajdanowicz, Radosław Michalski, Katarzyna Musial, Przemyslaw Kazienko
In relational learning tasks such as within network classification the main problem arises from the inference of nodes' labels based on the the ground true labels of remaining nodes. The problem becomes even harder if the nodes from initial network do not have any labels assigned and they have to be acquired. However, labels of which nodes should be obtained in order to provide fair classification results? Active learning and inference is a practical framework to study this problem. The method for active learning and inference in within network classification based on node selection is proposed in the paper. Based on the structure of the network it is calculated the utility score for each node, the ranking is formulated and for selected nodes the labels are acquired. The paper examines several distinct proposals for utility scores and selection methods reporting their impact on collective classification results performed on various real-world networks.
{"title":"Active learning and inference method for within network classification","authors":"Tomasz Kajdanowicz, Radosław Michalski, Katarzyna Musial, Przemyslaw Kazienko","doi":"10.1145/2492517.2500259","DOIUrl":"https://doi.org/10.1145/2492517.2500259","url":null,"abstract":"In relational learning tasks such as within network classification the main problem arises from the inference of nodes' labels based on the the ground true labels of remaining nodes. The problem becomes even harder if the nodes from initial network do not have any labels assigned and they have to be acquired. However, labels of which nodes should be obtained in order to provide fair classification results? Active learning and inference is a practical framework to study this problem. The method for active learning and inference in within network classification based on node selection is proposed in the paper. Based on the structure of the network it is calculated the utility score for each node, the ranking is formulated and for selected nodes the labels are acquired. The paper examines several distinct proposals for utility scores and selection methods reporting their impact on collective classification results performed on various real-world networks.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"21 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":"123871543","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}
Hao Wu, C. Chelmis, V. Sorathia, Yinuo Zhang, O. Patri, V. Prasanna
To enhance human resource management and personalized information acquisition, employee ontology is used to model business concepts and relations between them for enterprises. In this paper, we propose an employee ontology that integrates user static properties from formal structures with dynamic interests and expertise extracted from informal communication signals. We mine user's interests at both personal and professional level from informal interactions on communication platforms at the workplace. We show how complex semantic queries enable granular analysis. At the microscopic level, enterprises can utilize the results to better understand how their employees work together to complete tasks or produce innovative ideas, identify experts and influential individuals. At the macroscopic level, conclusions can be drawn, among others, about collective behavior and expertise in varying granularities (i.e. single employee to the company as a whole).
{"title":"Enriching employee ontology for enterprises with knowledge discovery from social networks","authors":"Hao Wu, C. Chelmis, V. Sorathia, Yinuo Zhang, O. Patri, V. Prasanna","doi":"10.1145/2492517.2500253","DOIUrl":"https://doi.org/10.1145/2492517.2500253","url":null,"abstract":"To enhance human resource management and personalized information acquisition, employee ontology is used to model business concepts and relations between them for enterprises. In this paper, we propose an employee ontology that integrates user static properties from formal structures with dynamic interests and expertise extracted from informal communication signals. We mine user's interests at both personal and professional level from informal interactions on communication platforms at the workplace. We show how complex semantic queries enable granular analysis. At the microscopic level, enterprises can utilize the results to better understand how their employees work together to complete tasks or produce innovative ideas, identify experts and influential individuals. At the macroscopic level, conclusions can be drawn, among others, about collective behavior and expertise in varying granularities (i.e. single employee to the company as a whole).","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":"123989036","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}
Social interactions preceding and succeeding trust formation can be significant indicators of formation of trust in online social networks. In this research we analyze the social interaction trends that lead and follow formation of trust in these networks. This enables us to hypothesize novel theories responsible for explaining formation of trust in online social settings and provide key insights. We find that a certain level of socialization threshold needs to be met in order for trust to develop between two individuals. This threshold differs across persons and across networks. Once the trust relation has developed between a pair of characters connected by some social relation (also referred to as a character dyad), trust can be maintained with a lower rate of socialization. Our first set of experiments is the relationship prediction problem. We predict the emergence of a social relationship like grouping, mentoring and trading between two individuals over a period of time by looking at the past characteristics of the network. We find that features related to trust have very little impact on this prediction. In the final set of experiments, we predict the formation of trust between individuals by looking at the topographical and semantic social interaction features between them. We generate three semantic dimensions for this task which can be recomputed with an observed social variable (say grouping) to create a new semantic social variable. In this endeavor, we successfully show that, including features related to socialization, gives us an approximate increase of 4-9% accuracy for trust relationship predictions.
{"title":"Socialization and trust formation: A mutual reinforcement? An exploratory analysis in an online virtual setting","authors":"Atanu Roy, Z. Borbora, J. Srivastava","doi":"10.1145/2492517.2492550","DOIUrl":"https://doi.org/10.1145/2492517.2492550","url":null,"abstract":"Social interactions preceding and succeeding trust formation can be significant indicators of formation of trust in online social networks. In this research we analyze the social interaction trends that lead and follow formation of trust in these networks. This enables us to hypothesize novel theories responsible for explaining formation of trust in online social settings and provide key insights. We find that a certain level of socialization threshold needs to be met in order for trust to develop between two individuals. This threshold differs across persons and across networks. Once the trust relation has developed between a pair of characters connected by some social relation (also referred to as a character dyad), trust can be maintained with a lower rate of socialization. Our first set of experiments is the relationship prediction problem. We predict the emergence of a social relationship like grouping, mentoring and trading between two individuals over a period of time by looking at the past characteristics of the network. We find that features related to trust have very little impact on this prediction. In the final set of experiments, we predict the formation of trust between individuals by looking at the topographical and semantic social interaction features between them. We generate three semantic dimensions for this task which can be recomputed with an observed social variable (say grouping) to create a new semantic social variable. In this endeavor, we successfully show that, including features related to socialization, gives us an approximate increase of 4-9% accuracy for trust relationship predictions.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"38 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":"125295062","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}
Maximizing product adoption within a customer social network under a constrained advertising budget is an important special case of the general influence maximization problem. Specialized optimization techniques that account for product correlations and community effects can outperform network-based techniques that do not model interactions that arise from marketing multiple products to the same consumer base. However, it can be infeasible to use exact optimization methods that utilize expensive matrix operations on larger networks without parallel computation techniques. In this paper, we present a hierarchical influence maximization approach for product marketing that constructs an abstraction hierarchy for scaling optimization techniques to larger networks. An exact solution is computed on smaller partitions of the network, and a candidate set of influential nodes is propagated upward to an abstract representation of the original network that maintains distance information. This process of abstraction, solution, and propagation is repeated until the resulting abstract network is small enough to be solved exactly. Our proposed method scales to much larger networks and outperforms other influence maximization techniques on marketing products.
{"title":"Hierarchical influence maximization for advertising in multi-agent markets","authors":"M. Maghami, G. Sukthankar","doi":"10.1145/2492517.2492622","DOIUrl":"https://doi.org/10.1145/2492517.2492622","url":null,"abstract":"Maximizing product adoption within a customer social network under a constrained advertising budget is an important special case of the general influence maximization problem. Specialized optimization techniques that account for product correlations and community effects can outperform network-based techniques that do not model interactions that arise from marketing multiple products to the same consumer base. However, it can be infeasible to use exact optimization methods that utilize expensive matrix operations on larger networks without parallel computation techniques. In this paper, we present a hierarchical influence maximization approach for product marketing that constructs an abstraction hierarchy for scaling optimization techniques to larger networks. An exact solution is computed on smaller partitions of the network, and a candidate set of influential nodes is propagated upward to an abstract representation of the original network that maintains distance information. This process of abstraction, solution, and propagation is repeated until the resulting abstract network is small enough to be solved exactly. Our proposed method scales to much larger networks and outperforms other influence maximization techniques on marketing products.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"27 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":"125297582","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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