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}
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}
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}
The mounting pressure to enable widespread access to electronic health record systems is being felt by healthcare providers. The federal government's Meaningful Use incentives are reason alone for providers to address this significant usability issue. As the healthcare industry considers solutions, attention should be given to the Cloud and the considerable investment that has been made related to the establishment of digital identities and making them interoperable across heterogeneous systems. This research considered how the Cloud could be leveraged by healthcare providers to not only provide patients with a familiar way of accessing electronic resources but also creating a significant cost savings for providers. An examination was performed of similar work being done in other industries as well as the standards laid out by the federal government for EHRs and digital identities. This research lays out a comprehensive framework for healthcare providers to easily follow to integrate with the Cloud for identity validation, while meeting compliance guidelines for security and privacy. To demonstrate the viability of this research, a number of pilots and proof of concept projects have already been implemented at a large regional hospital and have produced immediate and tangible improvements.
{"title":"The forecast for electronic health record access: Partly cloudy","authors":"Brian Coats, Subrata Acharya","doi":"10.1145/2492517.2500329","DOIUrl":"https://doi.org/10.1145/2492517.2500329","url":null,"abstract":"The mounting pressure to enable widespread access to electronic health record systems is being felt by healthcare providers. The federal government's Meaningful Use incentives are reason alone for providers to address this significant usability issue. As the healthcare industry considers solutions, attention should be given to the Cloud and the considerable investment that has been made related to the establishment of digital identities and making them interoperable across heterogeneous systems. This research considered how the Cloud could be leveraged by healthcare providers to not only provide patients with a familiar way of accessing electronic resources but also creating a significant cost savings for providers. An examination was performed of similar work being done in other industries as well as the standards laid out by the federal government for EHRs and digital identities. This research lays out a comprehensive framework for healthcare providers to easily follow to integrate with the Cloud for identity validation, while meeting compliance guidelines for security and privacy. To demonstrate the viability of this research, a number of pilots and proof of concept projects have already been implemented at a large regional hospital and have produced immediate and tangible improvements.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"10 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":"125724716","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}
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}
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}
Teng Wang, Keith C. Wang, Fredrik Erlandsson, S. F. Wu, Robert W. Faris
With the popularity of social media in recent years, it has been a critical topic for social network designer to understand the factors that influence continued user participation in online newsgroups. Our study examines how feedback with different opinions is associated with participants' lifetime in online newsgroups. Firstly, we propose a new method of classifying different opinions among user interaction contents. Generally, we leverage user behavior information in online newsgroups to estimate their opinions and evaluate our classification results based on linguistic features. In addition, we also implement this opinion classification method into our SINCERE system as a real-time service. Based on this opinion classification tool, we use survival analysis to examine how others' feedback with different opinions influence continued participation. In our experiment, we analyze more than 88,770 interactions on the official Occupy LA Facebook page. Our final result shows that not only the feedback with the same opinions as the user, but also the feedback with different opinions can motivate continued user participation in online newsgroup. Furthermore, an interaction of feedback with both the same and different opinions can boost user continued participation to the greatest extent. This finding forms the basis of understanding how to improve online service in social media.
{"title":"The influence of feedback with different opinions on continued user participation in online newsgroups","authors":"Teng Wang, Keith C. Wang, Fredrik Erlandsson, S. F. Wu, Robert W. Faris","doi":"10.1145/2492517.2492555","DOIUrl":"https://doi.org/10.1145/2492517.2492555","url":null,"abstract":"With the popularity of social media in recent years, it has been a critical topic for social network designer to understand the factors that influence continued user participation in online newsgroups. Our study examines how feedback with different opinions is associated with participants' lifetime in online newsgroups. Firstly, we propose a new method of classifying different opinions among user interaction contents. Generally, we leverage user behavior information in online newsgroups to estimate their opinions and evaluate our classification results based on linguistic features. In addition, we also implement this opinion classification method into our SINCERE system as a real-time service. Based on this opinion classification tool, we use survival analysis to examine how others' feedback with different opinions influence continued participation. In our experiment, we analyze more than 88,770 interactions on the official Occupy LA Facebook page. Our final result shows that not only the feedback with the same opinions as the user, but also the feedback with different opinions can motivate continued user participation in online newsgroup. Furthermore, an interaction of feedback with both the same and different opinions can boost user continued participation to the greatest extent. This finding forms the basis of understanding how to improve online service in social media.","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":"131239375","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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